1. Death by Market Power:
Reform, Competition and Patient Outcomes
in the British National Health Service
Martin Gaynor
Carnegie Mellon University, University of Bristol, RAND, & NBER
Rodrigo Moreno‐Serra
Imperial College London
Carol Propper
Imperial College London & University of Bristol
Leonard Davis Institute of Health Economics
University of Pennsylvania
October 22, 2010
1

2. INTRODUCTION

3. Motivation
1. Health care reform is happening in most
developed countries: U.S., U.K., Germany,
Netherlands, Belgium, Israel, Australia,...
2. Market‐oriented approaches to health care are
an important reform model outside U.S.
a. Lack of strong research evidence w.r.t. quality.
b. Price not relevant in most systems outside the
U.S., or for U.S. Medicare program (20% of U.S.
spending).
3

4. Motivation, cont’d.
3. In U.S. consolidation in markets has led to
questions about functioning of markets in
health care.
a. How well are markets working?
b. Could further consolidation promoted by health
reform be harmful?
4. Need to add to knowledge about conditions
under which markets might work.
a. Quality, cost, prices (U.S.).

5. Competition and Quality in Health Care
1. Theory
a. Regulated prices – competition increases quality (if price >
marginal cost; e.g., regulated airline models).
i. Quality elasticity of firm’s demand increasing in the # of firms.
ii. Competition increases management effectiveness, thereby increasing
quality.
b. Market determined prices – anything can happen.
2. Evidence is mixed
a. Regulated prices
i. Medicare – competition improves quality (e.g., Kessler & McClellan,
2000)
b. Market determined prices
i. U.S. private markets – not so clear (Volpp et al., 2003)
ii. U.K. 90s reforms – competition reduced quality (Propper et al, 2008)
3. Little evidence from policies designed to introduce
competition

6. Our Contribution
1. Most empirical evidence on the impact of competition
on quality uses variation in market structure across
existing hospital markets.
a. We’d like to randomly assign hospitals to varying degrees
of competition.
b. Clearly not the case.
2. We exploit a policy “experiment” (NHS) to assess the
impact of competition.
a. Examine a range of hospital outcomes (measures of
quality, quantity/access, spending).
3. Find that lower market concentration is associated
with higher quality without a commensurate increase
in expenditure.

7. The NHS Reforms
1. 1997‐ buyers and sellers operated under co‐
operation and negotiated annual budgets on price
and volume.
a. Patients had little choice of hospital.
b. Quality not contracted on (except waiting times).
2. Policy change initiated in 2003, put in place in 2006
3. GoaI – to promote competition among hospitals

8. NHS Reforms, cont’d.
4. Key elements
a. ‘Choose and Book’ – patients must be offered choice of 5
hospitals.
b. Payment by Results (PbR) ‐ movement from negotiated
to fixed prices (HRGs ‐ similar to U.S. DRGs).
i. PbR accounts for almost 70% of activity.
c. Reward /Penalties for Performance.
i. Foundation Trust Status – Retain net income.
ii. Poor performance – management replacment, closure, merger.

9. Expected Effects of the Reform
1. Expected effects
a. ‘Choose and Book’ – increase elasticity of demand facing
hospitals.
b. PbR – change conduct .
i. Hospitals paid for activity.
ii. Focus on quality as prices fixed.
2. Do hospitals have incentives to respond?
a. Not for profit, annual budget constraint.
b. Poor financial & clinical performance heavily penalised.
c. PbR system is very highly geared.
i. Levels of prices key.

10. What We Do
1. Exploit policy change in NHS 2006 to undertake
difference in difference analyses .
2. Use time periods before/after reform and variation
in market concentration.
a. Before/After: 2003/2007
b. Concentration: More/Less Concentrated
3. Post‐policy a hospital in a less concentrated market
faces greater exposure to the policy.
2
1 i
n
i sHHI 

11. Data
1. HES (Hospital Episode Statistics) data from the
NHS
(http://www.hesonline.nhs.uk/Ease/servlet/ContentServer?siteID=1937)
a. Data on all admissions to NHS hospitals in England.
i. Standard hospital discharge data set: diagnoses,
procedures, patient characteristics, location,etc.
ii. ~13 million records per year
iii. We use data on ~160 short term general hospitals per
year
b. We use hospital level data for 2 years – 2003/04,
2007/08
c. Used to construct measures of concentration and
some outcome measures

12. Data, cont’d.
2. Measures of quality and performance
a. Some calculated from HES data (e.g. in‐hospital
deaths within 28 days of admission for various
treatments, deaths in all locations after AMI
admission, LOS)
b. Some derived from official data on hospital
performance (e.g. waiting times data, CQC data)
3. Data from administrative sources
a. NHS staffing data
b. Small area characteristics (wages, mortality)

13. Measures of Concentration
1. HHIs for hospitals based on patient flows
1. Built up from small area (~ 7000 persons) patient
flows to hospitals
a. Calculate HHIs at MSOA level for 2003 and 2007
b. Use all non‐emergency admissions
c. Allow market to be whole country
2. Aggregate to hospitals based on share of patient
flows to hospital from each MSOA
2
1 i
n
i sHHI 

14. WHAT THE RAW DATA SHOW

15. Raw Data
1. Did mortality rates go up (more) in more
concentrated markets after the reform?
2. Did concentration change (before/after
reform)?
3. Did demand change post‐reform?

16. The Paper While Standing on One Foot
28 Day AMI Mortality Rate and HHI

17. The Paper While Standing on One Foot
28 Day AMI Mortality Rate and HHI

18. Still on One Foot
28 Day All Causes Mortality and HHI

19. Still on One Foot
28 Day All Causes Mortality and HHI

20. Decrease in Concentration

21. Levels and Changes in Concentration
by Location

22. Did Demand Change Post Reform?
1. Examine changes in patterns of patient care
seeking 2003‐07 by:
a. Quality of Hospitals (top vs. bottom quartiles of
AMI mortality rate in 2003)
b. Exposure to competition (bottom vs top quartile
of HHI in 2003)

23. Better Hospitals are Attracting More
Patients
AMI mortality rate (2003)
Bottom quartile Top quartile
2003 2007
%
change
(2003-
07) 2003 2007
%
change
(2003-
07)
Number of elective
admissions
33,985 38,274 12.6% 41,398 45,132 9.0%
Average distance
travelled by
patients
11.4 11.7 2.4% 10.0 10.1 1.1%
Share of patients
bypassing nearest
hospital
0.37 0.39 5.4% 0.45 0.43 -4.4%
Number of
hospitals
33 33 32 32

24. Hospitals More Exposed to Policy are
Attracting More Patients
Market concentration: HHI (2003)
Low (bottom quartile) High (top quartile)
2003 2007
%
change
(2003-
07) 2003 2007
%
change
(2003-
07)
Number of elective
admissions
21,757 26,924 23.8% 55,253 61,049 10.5%
Average distance
travelled by patients
8.1 8.3 2.3% 15.5 15.5 0.5%
Share of patients
bypassing nearest
hospital
0.45 0.46 2.2% 0.47 0.47 0.0%
Number of hospitals 41 41 40 40

25. REGRESSION ANALYSIS

26. Econometric Strategy
1. Exploit policy change in NHS 2006 to undertake difference in
difference analyses .
2. Use time periods before/after reform and variation in
market concentration.
3. Identification from cross sectional and time series variation.
4. Policy effect = Market Concentration* Policy On (2007).
a. Parameter δ in regression.
qit =  + I(t=2007) +I(t=2007)*HHIit +  HHIit + Xit i +it

27. Econometric Issues
1. There may remain concerns over endogeneity of
concentration + patient heterogeneity.
2. Control for patient heterogeneity with observables.
a. Patient age, sex, severity (Charlson index) .
b. Local area health, income.
c. Include hospital fixed effects.
3. Replace actual HHI with a measure of market structure
based on factors unrelated to quality or unobserved
patient heterogeneity.
4. Also concerns about whether DiD assumptions are met.
a. Are there pre‐existing differences (observable and
unobservable) between hospitals with different market
structures?

28. Predicted HHI
1. Predicted HHIs from predicted patient flows
from estimated MNL model of hospital choice.
a. a la Kessler and McClellan (2000).
b. Choice in MNL model depends on:
i. hospital characteristics (size, teaching status),
ii. differential distance from patient’s MSOA centroid to
hospital,
iii. patient characteristics (age, sex, level of co‐morbidity).
c. Choice set – all hospitals within 100km extended to
ensure that there is always a first and second choice
within hospital type (size, teaching) with minimum
of 50 admissions.

29. Tests of Difference in Difference
Assumptions
1. If we find a relationship between mortality and market structure is it
due to the policy or to pre‐existing differences between hospitals with
different market structures?
2. We examine:
a. bivariate associations between the observed baseline conditions and the
subsequent four year change in the HHI;
i. Admissions, AMI admissions, doctors, clinical staff, area mortality rate, case mix,
Index of Multiple Deprivation, Charlson Index.
b. bivariate associations between the initial levels of mortality and the
subsequent changes in market structure.
i. In‐hospital AMI mortality, 30 day AMI mortality, In‐hospital all‐cause mortality.
3. If the change in HHI is associated with pre‐existing differences this
may indicate that hospitals that differ in HHI growth may also differ in
unobserved factors.
4. None of the associations signficantly different from zero.
29

30. Regression Results
1. Mortality, Waiting Times
2. Quantity, Expenditure
3. Robustness Checks
30

31. DiD Estimates of Market Structure on
Outcomes and Waiting Times
(1) (2) (3) (4) (5) (6) (7)
28 day
AMI
mortality
rate
(in-
hospital,
ages 55+)
30 day
AMI
mortality
rate
(on or after
discharge,
ages 35-
74)
28 day all
causes
mortality
rate
(in-
hospital)
28 day
mortality
rate
(in-
hospital,
excluding
AMI)
MRSA
bacterae
mia
rate
Patients
waiting
3
months
or more
Attendance
s
spending
less than
4 hours in
A&E
DiD coefficient 0.246*** 0.313** 0.069** 0.066** -0.110 0.078 -0.005
(0.084) (0.116) (0.027) (0.028) (0.118) (0.167) (0.011)
Hospitals 133 133 162 162 161 162 150
Observations 250 250 323 323 318 323 299

32. Estimated Effect of the Policy
1. Hospitals in less concentrated markets had significantly lower
mortality rates post‐reform than those in more concentrated
markets.
a. The policy “worked.”
2. 10% fall in HHI associated with a 2.46% reduction in in‐hospital AMI
mortality rate.
3. 1/3rd of a percentage point at mean AMI mortality rate (13.2%).
4. Similar to #s from previous work.
a. Kessler and McClellan (2000)
i. Change from top to bottom quartile of HHI leads to 3.37 percentage point
decrease in AMI death rate. Our equivalent #: 3.61.
b. Cooper et al. (2010)
i. 1 s.d. change leads to 0.3 percentage point reduction in AMI death rate. Our
#: 0.33.
32

33. Policy Impacts on LOS, Admissions,
and Expenditure
33
(1) (2) (3) (4) (5) (6)
Length-of-stay and admissions
Expenditure and
productivity
Mean
length-of-
stay
(days)
Total
admissions
(number)
Elective
admissions
(share of
total)
Non-
elective
admissions
(share of
total)
Operating
expenditure
(£1,000)
Operating
expenditure
(£1,000)
per admission
DiD coefficient 0.254*** -0.012 -0.005 -0.001 0.007 0.014
(0.059) (0.031) (0.017) (0.024) (0.072) (0.074)
Hospitals 162 162 162 162 162 162
Observations 323 324 324 324 319 319

34. Robustness Checks
1. Results may be driven by pre‐existing differences
between hospitals that are correlated with market
structure.
a. Placebo test using 2001 as before policy and 2003 as
after policy – insignificant.
2. Estimation using only pre‐policy variation in market
structure.
a. HHI*2007 – significant.
3. Add further controls for patient heterogeneity;
income shock from PbR; local area economic
conditions (male wage, ambulance speeds).
a. DiD estimates still significant, magnitudes almost
unchanged.
34

35. Did the Policy Matter?
1. Benefits from the observed change in market
structure post reform.
a. 3,354 life years saved = £227 million (=$350mil).
2. Cost of being in a concentrated market compared to
being in a less concentrated one.
a. An HHI of 2,000 less (=one s.d.) implies a saving of £3.7
billion (=$5.7 bil).
3. NHS budget is £100 billion (=$154 bil).
a. Impact is 0.2% of NHS budget.
b. Immediate impact is small, but we only value deaths
averted and longer term impact of reducing
concentration considerably larger.

36. Conclusions
1. Robust evidence that under a regulated price regime,
within two years a pro‐competitive policy resulted in:
a. an improvement in clinical outcomes, as measured in
death rates,
b. reduction in length of stay,
c. no increase in expenditures.
2. Conclude: policy appears to have saved lives and did
not (measurably) increase costs.
3. Competition can be an important mechanism for
enhancing the quality of care.
4. Monopoly kills.

37. Additional slides

38. Hospitals Used by GPs and Distances Travelled
by Patients

39. Tests of DiD Assumptions
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
Covariate
Total
admissions
AMI
admissions
(ages 55+)
Doctors
(share of
clinical
staff)
Qualified
clinical
staff
(share of
clinical
staff)
Area
standardized
mortality
rate
Case
mix
Index of Multiple
Deprivation
(average for
patients’ areas of
residence)
Charlson
index
(average for
admissions at
the hospital)
28 day AMI
mortality rate
(in-hospital,
ages 55+)
30 day AMI
mortality rate
(on or after
discharge,
ages 35-74)
28 day
all-cause
mortality
rate
(in-hospital)
Coefficient -0.624 -0.051 5.329 -10.233 -1.695 -0.001 81.732 -1.080 -1.808 47.847
(0.642) (0.086) (8.792) (7.947) (1.936) (0.003) (72.197) (4.751) (6.623) (42.840)
P-value for Wald
test 0.129
Observations 162 151 161 161 162 162 162 162 130 130 162

40. Robustness tests
(1) (2) (3)
28 day AMI
mortality rate
(in-hospital, ages 55+)
28 day all-cause
mortality rate
(in-hospital)
Mean
length-of-stay
(days)Robustness test
1. Baseline 0.246*** 0.069** 0.254***
(0.084) (0.027) (0.059)
Observations 250 323 323
2. Placebo DiD test for 2001-2003 -0.047 0.005 -0.036
(0.077) (0.027) (0.047)
Observations 250 309 309
3. Using time invariant pre-reform HHI level (2003) 0.216*** 0.066** 0.245***
as market structure measure (0.079) (0.028) (0.059)
Observations 250 323 323
4. Controlling for the Charlson index 0.246*** 0.067** 0.239***
(0.084) (0.027) (0.060)
Observations 250 323 323
5. Controlling for the Index of Multiple Deprivation 0.278*** 0.067** 0.263***
(0.085) (0.029) (0.061)
Observations 250 323 323
6. Controlling for surpluses/deficits 0.242** 0.076** 0.229***
(0.093) (0.030) (0.066)
Observations 236 302 302
7. All hospitals (weighted by number of admissions) 0.138** 0.069*** 0.261***
(0.069) (0.024) (0.061)
Observations 299 323 323
8. Using levels of the dependent variable and HHI 0.170** 0.069*** 0.197***
(implied elasticity)
Observations 250 323 323
9. Controlling for income (male wage in area) 0.247*** 0.061** 0.258***
(0.086) (0.029) (0.062)
Observations 248 319 319
10. Controlling for the share of urgent ambulance calls 0.238**
responded within eight minutes (0.100)
Observations 233

41. Magnitude of Effects
41
28 day
mortality rate
(all causes)
Panel A - Observed magnitudes
Average number of admissions (2003/04) 63,094
Average number of deaths (2003/04) 1,135.1
Average mortality rate (2003/04) (%) 1.799%
Average number of admissions (2007/08) 72,558
Average number of deaths (2007/08) 1,053.5
Average mortality rate (2007/08) (%) 1.452%
Average change in deaths (2003-07) (positive = deaths averted) 81.5
Average decrease in predicted HHI (2003/04-2007/08) -118
Panel B - Continuous HHI: magnitudes implied by estimated coefficient (summary stats refer to HHI 03)
Baseline coefficient (elasticity) (%) 0.069
Scenario 1: Average decrease in HHI (Policy impact)
Implied counterfactual percentage increase in the mortality rate 2007/08 per hospital (for elasticity calculated at mean HHI) 0.2%
Total lives saved for the whole sample of hospitals (N = 162) 327
Total number of years of life saved for the whole sample of hospitals 3,354
Total savings in £million (value of year of life = £60,000/p.a.) £201
Scenario 2: One standard deviation increase in HHI 03 (= increase of 1928 units, from 4,353 to 6,281 in the whole sample)
Implied counterfactual percentage increase in the mortality rate 2007/08 per hospital (for elasticity calculated at mean HHI) 3.1%
Total lives saved for the whole sample of hospitals (N = 162) 5,336
Total number of years of life saved for the whole sample of hospitals 54,771
Total savings in £million (value of year of life = £60,000/p.a.) £3,286

42. No change in patient type except for
IMD for good hospitals
42
Change 2007-2005
(1) (2) (3) (4) (5) (6)
Elective
admissions
Number of
MSOAs
(electives)
Mean
distance
travelled
(electives)
Mean
IMD
ranking
Charlson
index
Number of
diagnoses
Mean waiting time 56.434* 0.694** 0.004* 13.836*** 0.00002 0.001
(elective admissions) (33.680) (0.295) (0.002) (3.061) (0.00013) (0.001)
Overall quality of services 1165.859 21.762** 0.078 207.347** -0.00030 0.024
(score) (897.260) (9.294) (0.076) (95.395) (0.00519) (0.031)
In-hospital mortality rate -942.177 14.816* -0.175* 14.927 -0.01229* -0.005
(all causes) (1255.291) (8.718) (0.103) (91.709) (0.00638) (0.050)
In-hospital mortality rate -91.980 -1.035 0.001 26.533 0.00086 0.002
(AMI) (227.052) (1.029) (0.011) (21.365) (0.00131) (0.005)
Teaching hospital status 1234.181 20.211 -0.098 335.378** -0.00041 -0.034
(2088.132) (17.763) (0.133) (143.230) (0.01348) (0.042)
Note: Index of Multiple Deprivation (over all patients), where patients in the most deprived locality in the year are attributed
the ranking of 1 and higher values are attributed to patients living in less deprived areas.

43. Competitive hospitals in 2005 not attracting observably
sicker/different patients but are attracting more patients
43
Change 2007-2005
(1) (2) (3) (4) (5) (6)
Elective
admissions
Number of
MSOAs
(electives)
Mean
distance
travelled
(electives)
Mean
IMD
ranking
Charlson
index
Number of
diagnoses
Level of HHI -483.360 -2.620 0.032 41.200 0.002 -0.003
(505.402) (2.788) (0.025) (43.781) (0.002) (0.015)
Indicator for bottom 3156.728** 18.499* -0.084 -139.657 0.001 0.004
quartile of HHI (1513.533) (10.949) (0.096) (162.770) (0.008) (0.056)
Number of hospitals 162 162 162 162 162 162
Note: Index of Multiple Deprivation (over all patients), where patients in the most deprived locality in the year are attributed
the ranking of 1 and higher values are attributed to patients living in less deprived areas.

44. 44
Predicted HHIs based on predicted patient flows based on
MNL model of hospital choice
2
1 1
1 1 1 1 1
ˆ ˆ
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ˆ ˆ
ˆ ˆ ˆ ˆ ˆ ˆ ˆ, 1 ,
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k jj k
n n nn J
j ij k ij k kj jk ij
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n n
HHI HHI HHI
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n n n n n  
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