LDI Research Seminar-Does Hospital Competition Improve Public Hospitals’ Productivity? Evidence from the English NHS Patient Choice Reforms

1. Does Hospital Competition Improve Public Hospitals’ Productivity? Evidence from the English NHS Patient Choice Reforms Zack Cooper The Centre for Economic Performance The London School of Economics

2. MOTIVATION Ambiguous evidence on the effect of competition on quality and productivity – Fixed price competition prominent in Medicare, English NHS, and Dutch health system; – US (and UK) going further and are allowing new providers (including ambulatory surgical centers) to enter the market and compete alongside traditional providers; – Affordable Care Act potentially reduces competition by encouraging vertical integration. What impact does this have on quality and productivity? – Empirical evidence on hospital competition is ambiguous, in part because of the challenge of obtaining causal estimates on competition and the difficulty of measuring productivity (Kessler and McClellan, 2000, Gowrisankaran and Town, 2003);

3. OVERVIEW A series of quasi-natural experiments to test the impact of hospital competition on providers’ quality and productivity • Identification: In 2006, a set of pro-competition reforms introduced across England “The arrival of ‘patient choice’ - the right to choose, initially from at least four hospitals, and by 2008 from any hospital prepared to meet NHS standards and prices - is a symbolic moment in the government’s endeavor to use market forces to drive up health service performance”, Nick Timmins, Financial Times, December 31, 2005 • Research design: difference-in-difference style estimation looking at whether hospitals located in more competitive markets pre-reform had bigger improvements in performance after the reforms were introduced relative to hospitals in monopoly markets – Public and private hospital locations in England are historically determined; – Policy was universal across England; – Patient level data with over 2+ million observations with four years pre-reform and five years post-reform

4. RESEARCH QUESTIONS An analysis of the impact of competition and private market entry on incumbent public hospitals 1. Did the introduction of hospital competition lower death rates in areas facing more competition? 2. Did hospital competition between public providers lead to productivity gains? 3. Did the entrance of private providers (ambulatory surgical centers) improve public providers’ productivity? 4. Did the entrance of new private providers leave incumbent providers treating a more costly mix of patients?

5. SUMMARY Public sector competition improved quality and productivity; private sector competition produced did not produce productivity gains • 1.s.d. increase in hospital competition pre-reform associated with 6.7% relative reduction in AMI mortality post reform (saving approx 300 lives per year in ‘06, ‘07, and ‘08) • Competition between public sector providers improved productivity - 1 hospital increase associated with 4-9% increase in lean production; • Private sector entrance did not help/harm lean operations but led to risk-selection; • Incumbent public hospitals located in areas with more private providers were left with an older and less wealthy mix of patients than led to £700,000 + excess costs from 2006 - 2010 per hospital; • All observed changes in quality and productivity correspond precisely to the introduction of the reforms. All results are robust across a range of specifications and across a number of different measures of market structure

6. THE NHS REFORMS CREATED HOSPITAL COMPETITION Involved changes to the demand and supply side in England + transactional reforms Demand Side Supply Side - Patient choice -Increased hospital autonomy (retain - Publicly provide info on surplus) quality - Allowed private providers to deliver care Competition Between Providers Regulation Transactional Reform - Creation of Healthcare -Prospective, fixed Commission & Monitor price payment system - Paperless referral system

7. TIMELINE OF THE NHS REFORMS The reforms came in on a rolling basis from 2004 - 2008 2002 2003 2004 2005 2006 2007 2008 2009 2010 Patient Fixed choice price Choice of Extended pilots tariff for 4 local choice FT trusts providers network begin Any NHS-funded (FTs, some patient in England private can attend any public or private Fixed provider in the NHS Choose price country and Book tariff for becomes all operational trusts Steady increases in NHS Funding

8. Does Hospital Competition Save Lives?

11. ISSUES ESTIMATING THE EFFECT OF HOSPITAL COMPETITION This literature is marked by a number of estimation issues • Question of how to measure market structure; • Hospital market structure is typically endogenous to providers’ performance (usual reduced form issue); • Is this a ‘city’ thing? • Crucial to demonstrate that the reforms were not driven by pre-reform trends in AMI death rates.

12. DATA Patient-level data on all NHS patients from 2002 - 2010 • Health episodes and statistics (HES) data include all patient observations from 2002 through 2010 ~ 2 million observations – Focus on elective, non-revision knee replacement, hip replacement, hernia repair and arthroscopy (high volume elective surgeries) • Patient characteristics (age, sex, Charlson co-morbidity index, home address) • Able to link patient characteristics to local area characteristics (income vector of the Index of Multiple Deprivations) • Provider characteristics (hospital teaching status, size, ownership) • Use patient’s registered GP practice (8000+) to calculated straight-line distances from GP to hospitals; • Data on private sector providers from Laing and Buisson, a private data holding company; • Use data on population density from the Office of National Statistics 2001 census at the Middle Super Output Area

13. MEASURING MARKET STRUCTURE Twin challenges of avoiding endogenous measures and avoiding capturing urban density Fixed radius market - i.e 20km radius draw around each GP + Radius size unrelated to provider performance - urban rural bias: likely overestimates market size in urban areas; under estimates market size in rural areas Variable radius market - i.e. radius that captures 95% of GP referrals + Radius size more accurately captures true size of the market - market size is potentially endogenous to performance Travel-time based market - i.e. radius that captures 30-minute drive + Radius should more accurately capture the size of the market + Radius is exogenous to quality; - In practice, 80+% correlation with fixed radius market Administrative boundaries - i.e. market defined as an MSOA or county + Radius size unrelated to provider performance - Market definitions are inaccurate if patients can travel across boundaries Kessler and McClellan (2002) index - using predicted demand to measure concentration + Exogenous to providers’ performance - highly correlated with fixed radius market because distance is main component of underlying estimation of demand

14. STRATEGY FOR QUANTIFYING MARKET STRUCTURE Our strategy was to show that results were robust across key measures of market structure Also measure HHIs and counts in: • Fixed radius markets • Travel time markets • Smaller variable radius markets Use predicted patient flows Use IV for market structure Carry out placebo test (school competition

15. MARKET STRUCTURE IN ENGLAND Fixed radius markets, travel-time based radius markets and predicted demand markets all heavily correlated with population density HHI within 20km Fixed Radius HHI within Travel-Time Radius Predicted Demand HHI

16. VARIABLE RADIUS MARKET IN ENGLAND Our variable radius market is far less correlated with urban density

17. OUR MEASURE OF HOSPITAL QUALITY We measured hospital quality using 30-day mortality from acute myocardial infarction Rational for using AMI mortality: 1. AMIs are relatively frequent, easily observable with significant mortality rate (~14%) 2. There is a clear link between timely and effective treatment and survival 3. Not likely to be gaming 4. Emergency procedure where there’s not much opportunity for risk-selection (also attenuates some endogeneity 5. Frequently used by governments (including in the UK) as a measure of hospital quality 6. Frequently used in this literature I.e. Kessler and McClellan (2000), Kessler and Geppert (2005), Volpp et al. (2003), Gaynor et al. (2010)… We observed links with other measures of performance in cross sectional firm level data in 2009: • Positively correlated with overall mortality (r = 0.33) • Positively correlated with LOS (r = 0.30) and waiting times (r = 0.20) • Positively correlated with patient satisfaction (r = 0.20)

18. OUR ESTIMATOR We use a flexible estimator and show our results are robust across several specifications – Deathijkt is an indicator for whether patient i, registered at GP j, treated at hospital k, at time t died within 30-days of admission for an AMI (heart attack) – t is a running counter of quarters since 2002 – is the policy break-point in the spline, which we regard as occurring in the start of the new financial year in 2006 – zjt is our measure of market structure measured for each GP market j at time t Flexible Estimator: • Gives rise to a standard DiD estimator • Gives rise to our preferred spline-based DiD trends estimator

19. OUR MODIFIED DIFF-IN-DIFF ESTIMATOR This allows us to test for the existence of pre-reform trends Outcome Control Treatment Time Policy-On Date

20. OUR MODIFIED DIFF-IN-DIFF ESTIMATOR This allows us to test for the existence of pre-reform trends Outcome Control Treatment Time Policy-On Date (2006)

21. OUR MODIFIED DIFF-IN-DIFF ESTIMATOR This allows us to test for the existence of pre-reform trends Outcome Control Treatment Time Policy-On Date (2006)

22. OUR MODIFIED DIFF-IN-DIFF ESTIMATOR This allows us to test for the existence of pre-reform trends Outcome Control Treatment Treatment effect Time Policy-On Date (2006)

23. OUR MODIFIED DIFF-IN-DIFF ESTIMATOR This allows us to test for the existence of pre-reform trends Outcome Control Treatment Treatment effect Time Policy-On Date (2006)

24. MAIN RESULTS robust across various specifications with and without fixed effects and controls for patient characteristics nlhhi measured in variable radius market

29. Hospitals located in competitive markets began to lower their mortality more quickly from 2006 onwards Policy on Source: Cooper et al. (2010)

30. Other Measures of Market Structure The results are robust using HHIs within other market definitions

31. ALTERNATIVE SPECIFICATIONS Our results are consistent using traditional DD and Year * nlhhi estimation

32. FALSIFICATION TEST AND COUNTERFACTUALS These illustrate that our results are a function of NHS market structure, rather than pop. density

33. Does Hospital Competition Improve Productivity

34. OVERVIEW We separately identify the effect of public and private-sector competition on productivity We take advantage of the phased introduction of the reforms • Identification: a difference-in-difference style estimation strategy with market structure interacted with year dummies – Public and private hospital locations in England are historically determined; – Policy was universal across England; – Patient level data with over 2 million observations with four years pre-reform and five years post-reform – Public sector competition took force in 2006; private sector competition in 2007/8 – Use a measure of lean production that is unbiased by patient characteristics • Questions: – Q1: Did hospital competition between public providers improve hospital productivity? – Q2: Did the entrance of private providers (ambulatory surgical centers) prompt incumbent providers to improve their productivity? – Q3: Did competition induce risk-selection and was this more pronounced with the entrance of private providers We measure productivity in incumbent providers

35. TIMELINE OF THE NHS REFORMS The reforms came in on a rolling basis from 2004 - 2008 2002 2003 2004 2005 2006 2007 2008 2009 2010 Patient Fixed choice price Choice of Extended tariff for 4 local choice pilots FT trusts providers network begin (FTs, Any NHS-funded some patient in England private can attend any public or private Fixed provider in the NHS Choose price country and Book tariff for becomes all operational trusts Steady increases in NHS Funding

36. PUBLIC AND PRIVATE PROVIDERS DELIVERING NHS FUNDED CARE We view the location of both public and private providers as exogenous to performance • Public hospital locations date back to the founding of the NHS – Large tertiary hospitals – Mean of 825 total beds • All private providers could see NHS funded patients if they were approved by the hospital regulatory body and were willing to deliver care according NHS tariffs – Mean of 49 beds; – Mainly deliver elective surgery; • We measure those who could have potentially delivered care, rather than those who did • Private hospitals largely pre-date the founding of the NHS – 158 of 162 prior to 2005 – 90% prior to 2000 – 72% prior to 1990 – Mean opening date: 1979

37. ESTIMATION STRATEGY We use a difference-in-difference style estimator to identify public and private sector competition Count of public providers Year dummies Hospital, GP and (pre-reform) interacted with procedure fixed effects year dummies losijkt = pub_countk • yt`β1 + priv_countk • yt`β2 + yt`δ + xijt`γ + θj + θk + θp + νijkt Count of private providers Patient and hospital (pre-reform) interacted with characteristics year dummies • Public and private counts are interacted with 1 and year dummies I.e. yt = [1 2003, 2004, 2005, 2006…2010] • Error terms are clustered around GPs k • β1 and β2 provide the year specific effects of public and private sector competition (off 2002) • y_pret` = [2003 … 2005] and y_postt` = [y2006 y2007 …2010] for public sector competition and [y2007 y2008 … y2010] for private sector competition • Assumptions – Hospitals would have followed trend of monopoly providers if untreated; – Hospitals located in more potentially competitive markets prior to the reforms would face sharper incentives after the reforms were introduced

38. OUR MEASURE OF MARKET STRUCTURE We create GP-centered markets that expand and contract in rural and urban areas For each GP-practice, define radius r as the distance necessary to capture a circular area around GP k that captures 330,000 adults over 18 – 333,000 people is roughly the population of adults in England divided by number of public hospitals – Also use market definitions that capture 666,000 adults and 999,000 adults Separately measure the count of public hospitals and private hospitals inside these market definitions + Public hospital locations are historical artifact that date back to the 1948 founding of the NHS. We measure counts in 2002; + 158 of 162 private providers in England were established prior to the reforms + We measure the number of potential private providers Center our markets on GP practice + Mimics market structure in England where patient chooses hospital with help from GP + market structure not endogenous to patient choice

39. PREFERRED MEASURE IS LESS CORRELATED WITH POP DENSITY Fixed radius counts and counts in our population market superimposed on a map of England Counts within fixed radius Counts within 666,000 person market radius market

40. MEASURING HOSPITAL PRODUCTIVITY We break patients’ length of stay into its two key components Patient Admitted Patient’s Surgery Patient Discharged Pre-surgery LOS Post-surgery LOS • Overall length of stay has been used as a proxy for efficiency but seemingly quite affected by patient characteristics (Gaynor et al. 2010, Martin and Smith 1996, Cutler et al. 1995 etc.) • Post-surgery LOS is likely heavily influenced by patient characteristics • Pre-surgery LOS should not be biased by patient characteristics for an elective surgery – Turn around time between surgeries; – Hospital admissions procedures; – Staff management (right person right time) • Lower pre-surgery LOS is capturing leaner operations

41. MECHANISM We hypothesize that higher competition will be associated with reductions in LOS Two mechanisms for competition driving reductions in LOS: If reimbursement rate > MC, PPS (or PbR) encourages providers to increase activity in order to increase revenues Hospitals in more competitive markets have more opportunity to increase activity through business-stealing They reduce LOS to create room for new patients Reductions in LOS driven by broad improvements in hospital management performance

42. RESULTS FOR OVERAL LOS, PRE-SURGERY LOS, RISK-SELECTION, COUNTERFACTUAL Count of public providers Year dummies Hospital, GP and (pre-reform) interacted with procedure fixed effects year dummies losijkt = pub_countk • yt`β1 + priv_countk • yt`β2 + yt`δ + xijt`γ + θj + θk + θp + νijkt Count of private providers Patient and hospital (pre-reform) interacted with characteristics year dummies

43. Does hospital competition improve productivity?

44. OVERALL LOS Our preferred specification with GP and hospital fixed effects in our 666,000 market Coef S.E. Count public - - 2003 * public 0.0019 0.0056 2004 * public 0.0262 0.0065 2005 * public 0.0311 0.0076 2006 * public 0.0005 0.0081 2007 * public -0.0348 0.0086 2008 * public -0.0442 0.0089 2009 * public -0.0594 0.0090 2010 * public -0.0881 0.0097 Count Private - - 2003 * private 0.0020 0.0048 2004 * private -0.0220 0.0051 2005 * private -0.0270 0.0059 2006 * private -0.0234 0.0063 2007 * private -0.0003 0.0062 2008 * private 0.0082 0.0061 2009 * private 0.0186 0.0061 2010 * private 0.0192 0.0068 Patient Characteristics Yes GP Fixed Effects Yes Trust Fixed Effects Yes Year Dummies Yes Obs 2,0390,070 R2 0.7567

45. OVERALL LOS Our preferred specification with GP and hospital fixed effects in our 666,000 market Coef S.E. Count public - - 2003 * public 0.0019 0.0056 2004 * public 0.0262 0.0065 2005 * public 0.0311 0.0076 2006 * public 0.0005 0.0081 2007 * public -0.0348 0.0086 2008 * public -0.0442 0.0089 2009 * public -0.0594 0.0090 2010 * public -0.0881 0.0097 Count Private - - 2003 * private 0.0020 0.0048 2004 * private -0.0220 0.0051 2005 * private -0.0270 0.0059 2006 * private -0.0234 0.0063 2007 * private -0.0003 0.0062 2008 * private 0.0082 0.0061 2009 * private 0.0186 0.0061 2010 * private 0.0192 0.0068 Patient Characteristics Yes GP Fixed Effects Yes Trust Fixed Effects Yes Year Dummies Yes Obs 2,0390,070 R2 0.7567

46. Overall LOS Graphical presentation of our preferred specification with GP and hospital fixed effects in our 666,000 market

47. PUBLIC AND PRIVATE COMPETITION ON OVERALL LOS - 333,000 Public Competition Reduces LOS; Private Competition Increases LOS Overall LOS (1) (2) (3) (4) (5) Coef S.E. Coef S.E. Coef S.E. Coef S.E. Coef S.E. Public 0.0834 0.0133 0.0835 0.0128 0.0559 0.0112 - - - - 2003 * public 0.0243 0.0089 0.0152 0.0086 0.0101 0.0085 0.0102 0.0086 0.0098 0.0086 2004 * public 0.0387 0.0109 0.0279 0.0106 0.0224 0.0103 0.0220 0.0105 0.0224 0.0104 2005 * public 0.0305 0.0128 0.0180 0.0125 0.0110 0.0122 0.0121 0.0124 0.0104 0.0123 2006 * public -0.0240 0.0133 -0.0374 0.0131 -0.0490 0.0128 -0.0482 0.0130 -0.0516 0.0129 2007 * public -0.0658 0.0145 -0.0839 0.0142 -0.0941 0.0139 -0.0929 0.0141 -0.0975 0.0140 2008 * public -0.0810 0.0151 -0.0932 0.0148 -0.1008 0.0145 -0.1014 0.0146 -0.1056 0.0146 2009 * public -0.0981 0.0153 -0.1144 0.0149 -0.1171 0.0147 -0.1217 0.0147 -0.1238 0.0148 2010 * public -0.1289 0.0164 -0.1558 0.0159 -0.1593 0.0157 -0.1651 0.0157 -0.1673 0.0158 Private -0.0010 0.0074 -0.0017 0.0072 0.0067 0.0065 - - - - 2003 * private -0.0025 0.0071 0.0002 0.0069 0.0004 0.0067 0.0001 0.0068 0.0006 0.0068 2004 * private -0.0167 0.0076 -0.0168 0.0074 -0.0169 0.0072 -0.0174 0.0073 -0.0167 0.0073 2005 * private -0.0183 0.0082 -0.0191 0.0080 -0.0159 0.0078 -0.0188 0.0080 -0.0168 0.0079 2006 * private -0.0197 0.0084 -0.0176 0.0084 -0.0130 0.0082 -0.0168 0.0083 -0.0137 0.0083 2007 * private 0.0002 0.0088 0.0026 0.0087 0.0055 0.0086 0.0036 0.0087 0.0060 0.0087 2008 * private 0.0052 0.0089 0.0045 0.0087 0.0095 0.0086 0.0081 0.0087 0.0102 0.0087 2009 * private 0.0202 0.0089 0.0193 0.0088 0.0233 0.0086 0.0206 0.0087 0.0210 0.0087 2010 * private 0.0174 0.0094 0.0179 0.0092 0.0178 0.0091 0.0171 0.0092 0.0173 0.0093 Patient Char. No Yes Yes Yes Yes GP F.E. No No No Yes Yes Trust F.E. No No Yes No Yes Year Dummies Yes Yes Yes Yes Yes Obs 2,039,070 2,0390,070 2,0390,070 2,0390,070 2,0390,070 R2 0.7304 0.7495 . 0.7548 0.7551 0.7567

52. PUBLIC AND PRIVATE COMPETITION ON OVERALL LOS - 666,000 Public Competition Reduces LOS; Private Competition Increases LOS Coef S.E. Coef S.E. Coef S.E. Coef S.E. Coef S.E. Count public 0.0593 0.0075 0.0565 0.0074 0.0196 0.0067 - - - - 2003 * public 0.0088 0.0058 0.0063 0.0056 0.0030 0.0056 0.0025 0.0056 0.0019 0.0056 2004 * public 0.0348 0.0067 0.0295 0.0066 0.0272 0.0065 0.0258 0.0066 0.0262 0.0065 2005 * public 0.0441 0.0079 0.0375 0.0077 0.0329 0.0076 0.0320 0.0076 0.0311 0.0076 2006 * public 0.0172 0.0083 0.0104 0.0082 0.0025 0.0080 0.0023 0.0081 0.0005 0.0081 2007 * public -0.0158 0.0088 -0.0241 0.0087 -0.0315 0.0085 -0.0323 0.0085 -0.0348 0.0086 2008 * public -0.0272 0.0091 -0.0326 0.0090 -0.0399 0.0088 -0.0418 0.0088 -0.0442 0.0089 2009 * public -0.0423 0.0092 -0.0510 0.0091 -0.0544 0.0090 -0.0588 0.0090 -0.0594 0.0090 2010 * public -0.0692 0.0099 -0.0817 0.0097 -0.0833 0.0096 -0.0876 0.0096 -0.0881 0.0097 Count Private -0.0014 0.0054 0.0007 0.0053 0.0035 0.0047 - - - - 2003 * private 0.0011 0.0050 0.0005 0.0049 0.0014 0.0048 0.0013 0.0048 0.0020 0.0048 2004 * private -0.0205 0.0053 -0.0218 0.0052 -0.0223 0.0051 -0.0223 0.0052 -0.0220 0.0051 2005 * private -0.0292 0.0062 -0.0303 0.0060 -0.0272 0.0058 -0.0285 0.0059 -0.0270 0.0059 2006 * private -0.0281 0.0064 -0.0271 0.0063 -0.0230 0.0062 -0.0259 0.0063 -0.0234 0.0063 2007 * private -0.0044 0.0065 -0.0041 0.0063 -0.0013 0.0061 -0.0025 0.0062 -0.0003 0.0062 2008 * private 0.0028 0.0063 0.0016 0.0062 0.0069 0.0060 0.0060 0.0061 0.0082 0.0061 2009 * private 0.0164 0.0063 0.0159 0.0062 0.0187 0.0061 0.0183 0.0061 0.0186 0.0061 2010 * private 0.0227 0.0070 0.0205 0.0068 0.0191 0.0067 0.0192 0.0068 0.0192 0.0068 Patient No Yes Yes Yes Yes Characteristics GP Fixed Effects No No No Yes Yes Trust Fixed No No Yes No Yes Effects Year Dummies Obs 2,039,070 2,0390,070 2,0390,070 2,0390,070 2,0390,070 R2 0.7305 0.7496 . 0.7548 0.7551 0.7567

57. PUBLIC AND PRIVATE COMPETITION ON OVERALL LOS - 999,000 Public Competition Reduces LOS; Private Competition Increases LOS Coef S.E. Coef S.E. Coef S.E. Coef S.E. Coef S.E. Count public 0.0377 0.0057 0.0381 0.0056 0.0123 0.0051 - - - - 2003 * public 0.0096 0.0043 0.0070 0.0042 0.0026 0.0042 0.0028 0.0042 0.0016 0.0042 2004 * public 0.0216 0.0050 0.0166 0.0049 0.0138 0.0048 0.0126 0.0048 0.0127 0.0048 2005 * public 0.0287 0.0057 0.0217 0.0056 0.0162 0.0055 0.0159 0.0055 0.0146 0.0055 2006 * public 0.0177 0.0061 0.0096 0.0060 0.0018 0.0059 0.0028 0.0059 0.0005 0.0060 2007 * public -0.0050 0.0064 -0.0144 0.0063 -0.0193 0.0062 -0.0202 0.0062 -0.0214 0.0063 2008 * public -0.0171 0.0067 -0.0251 0.0066 -0.0307 0.0065 -0.0325 0.0065 -0.0340 0.0065 2009 * public -0.0274 0.0069 -0.0366 0.0068 -0.0388 0.0068 -0.0432 0.0067 -0.0435 0.0068 2010 * public -0.0442 0.0075 -0.0567 0.0073 -0.0585 0.0073 -0.0626 0.0072 -0.0630 0.0073 Count Private 0.0027 0.0043 0.0035 0.0042 0.0064 0.0038 - - - - 2003 * private -0.0035 0.0038 -0.0036 0.0038 -0.0015 0.0037 -0.0020 0.0037 -0.0009 0.0037 2004 * private -0.0167 0.0041 -0.0169 0.0040 -0.0168 0.0039 -0.0165 0.0039 -0.0163 0.0039 2005 * private -0.0252 0.0046 -0.0248 0.0044 -0.0213 0.0043 -0.0222 0.0044 -0.0208 0.0044 2006 * private -0.0298 0.0049 -0.0275 0.0048 -0.0235 0.0048 -0.0261 0.0048 -0.0239 0.0048 2007 * private -0.0087 0.0051 -0.0071 0.0050 -0.0067 0.0049 -0.0066 0.0049 -0.0063 0.0050 2008 * private 0.0021 0.0050 0.0030 0.0049 0.0053 0.0048 0.0060 0.0048 0.0064 0.0049 2009 * private 0.0112 0.0052 0.0117 0.0050 0.0111 0.0050 0.0130 0.0050 0.0119 0.0050 2010 * private 0.0125 0.0057 0.0126 0.0055 0.0097 0.0055 0.0116 0.0055 0.0105 0.0055 Patient Char No Yes Yes Yes Yes GP F.E. No No No Yes Yes Trust F.E No No Yes No Yes Year Dummies Yes Yes Yes Yes Yes Obs 2,039,070 2,0390,070 2,0390,070 2,0390,070 2,0390,070 R2 0.7305 0.7495 . 0.7548 0.7551 0.7567

62. PRE- AND POST-SURGERY LOS Our preferred specification with GP and hospital fixed effects in our 666,000 market Pre-surgery Post-surgery Most conservative estimates Coef S.E. Coef S.E. Count public - - - - 2003 * public 0.0038 0.0013 -0.0019 0.0053 2004 * public 0.0082 0.0017 0.0180 0.0060 2005 * public 0.0128 0.0021 0.0184 0.0069 Public Counts 2006 * public 0.0071 0.0023 -0.0066 0.0073 2007 * public -0.0012 0.0025 -0.0336 0.0077 • Pre-surgery relative reduction of 4.2% 2008 * public -0.0020 0.0024 -0.0421 0.0080 2009 * public -0.0096 0.0024 -0.0498 0.0082 • Post-surgery relative reduction of 2.6% 2010 * public -0.0156 0.0024 -0.0725 0.0089 Count Private - - - - 2003 * private 0.0028 0.0010 -0.0008 0.0046 2004 * private -0.0022 0.0013 -0.0198 0.0047 2005 * private -0.0056 0.0015 -0.0214 0.0053 2006 * private -0.0058 0.0018 -0.0176 0.0055 Private Counts 2007 * private -0.0028 0.0018 0.0025 0.0055 • No significant effect on pre-surgery 2008 * private -0.0012 0.0016 0.0094 0.0056 2009 * private 0.0021 0.0016 0.0165 0.0056 • Significant effect on post-surgery 2010 * private 0.0008 0.0016 0.0185 0.0062 Patient Car Yes Yes GP F.E. Yes Yes Trust F.E. Yes Yes Year Dummies Yes Yes Obs 2,039,070 2,039,070 R2 0.3477 0.7462

63. THE IMPACT OF PUBLIC COMPETITION ON PRE-SURGERY LOS Graphical presentation of our preferred specification with GP and hospital fixed effects in our 666,000 market

64. THE IMPACT OF PRIVATE COMPETITION ON POST-SURGERY LOS Graphical presentation of our preferred specification with GP and hospital fixed effects in our 666,000 market

65. PRE- AND POST-SURGERY LOS Our preferred specification with GP and hospital fixed effects in our 666,000 market Counts within radius capturing Counts within radius capturing 666,000 Counts within radius capturing 999,000 333,000 over 18s around patient’s GP over 18s around patient’s GP practice over 18s around patient’s GP practice practice Pre-surgery Post-surgery Pre-surgery Post-surgery Pre-surgery Post-surgery Coef S.E. Coef S.E. Coef S.E. Coef S.E. Coef S.E. Coef S.E. Count public - - - - - - - - - - - - 2003 * public 0.0085 0.0021 0.0013 0.0081 0.0038 0.0013 -0.0019 0.0053 0.0021 0.0010 -0.0005 0.0039 2004 * public 0.0094 0.0026 0.0130 0.0096 0.0082 0.0017 0.0180 0.0060 0.0060 0.0012 0.0067 0.0044 2005 * public 0.0135 0.0032 -0.0031 0.0114 0.0128 0.0021 0.0184 0.0069 0.0068 0.0016 0.0078 0.0050 2006 * public -0.0037 0.0037 -0.0479 0.0118 0.0071 0.0023 -0.0066 0.0073 0.0027 0.0018 -0.0021 0.0054 2007 * public -0.0149 0.0041 -0.0825 0.0126 -0.0012 0.0025 -0.0336 0.0077 -0.0042 0.0019 -0.0171 0.0057 2008 * public -0.0147 0.0041 -0.0909 0.0131 -0.0020 0.0024 -0.0421 0.0080 -0.0083 0.0019 -0.0258 0.0059 2009 * public -0.0236 0.0041 -0.1001 0.0134 -0.0096 0.0024 -0.0498 0.0082 -0.0138 0.0019 -0.0297 0.0061 2010 * public -0.0328 0.0042 -0.1344 0.0145 -0.0156 0.0024 -0.0725 0.0089 -0.0179 0.0020 -0.0451 0.0066 Count Private - - - - - - - - - - - - 2003 * private 0.0024 0.0015 -0.0018 0.0064 0.0028 0.0010 -0.0008 0.0046 0.0021 0.0008 -0.0029 0.0035 2004 * private -0.0012 0.0018 -0.0155 0.0066 -0.0022 0.0013 -0.0198 0.0047 -0.0028 0.0010 -0.0135 0.0036 2005 * private -0.0075 0.0022 -0.0093 0.0071 -0.0056 0.0015 -0.0214 0.0053 -0.0045 0.0012 -0.0163 0.0039 2006 * private -0.0049 0.0025 -0.0088 0.0074 -0.0058 0.0018 -0.0176 0.0055 -0.0065 0.0013 -0.0174 0.0043 2007 * private -0.0060 0.0026 0.0119 0.0077 -0.0028 0.0018 0.0025 0.0055 -0.0040 0.0014 -0.0023 0.0045 2008 * private -0.0051 0.0024 0.0153 0.0078 -0.0012 0.0016 0.0094 0.0056 -0.0003 0.0013 0.0067 0.0044 2009 * private -0.0012 0.0023 0.0222 0.0080 0.0021 0.0016 0.0165 0.0056 0.0017 0.0012 0.0102 0.0046 2010 * private -0.0033 0.0023 0.0206 0.0085 0.0008 0.0016 0.0185 0.0062 0.0004 0.0012 0.0100 0.0050 Patient Char. Yes Yes Yes Yes Yes Yes GP F.E. Yes Yes Yes Yes Yes Yes Trust F.E. Yes Yes Yes Yes Yes Yes Year Dummies Yes Yes Yes Yes Yes Yes Obs 2,039,07 2,039,0 2,039,070 2,039,070 2,039,070 2,039,070 0 70 R2 0.3478 0.7462 0.3477 0.7462 0.3479 0.7462

66. Do new market entrants leave public hospitals treating older and sicker patients?

67. EVIDENCE OF CREAM-SKIMMING ON SOCIO-ECONOMIC STATUS Greater private competition leaves incumbents with less wealthy patient mix 333,000 Person Radius 666,000 Person Radius Counts 999,000 Person Radius Counts Counts Coef. Coef. S.E.S.E. Coef. Coef. S.E.S.E. Coef.Coef. S.E. S.E. Count public - - - - - - 2003 * public -0.0003 0.0004 -0.0002 0.0002 0.0000 0.0002 2004 * public -0.0007 0.0004 -0.0002 0.0003 0.0002 0.0002 2005 * public -0.0007 0.0005 -0.0003 0.0003 0.0002 0.0002 2006 * public -0.0035 0.0005 -0.0013 0.0003 -0.0004 0.0002 2007 * public 0.0040 0.0006 0.0028 0.0004 0.0026 0.0003 2008 * public 0.0025 0.0006 0.0019 0.0004 0.0021 0.0003 2009 * public 0.0025 0.0007 0.0021 0.0004 0.0022 0.0003 2010 * public 0.0027 0.0007 0.0021 0.0005 0.0023 0.0003 Count private - - - - - - 2003 * private -0.0008 0.0003 -0.0003 0.0002 -0.0003 0.0001 2004 * private -0.0007 0.0003 -0.0004 0.0002 -0.0004 0.0002 2005 * private -0.0020 0.0004 -0.0007 0.0002 -0.0006 0.0002 2006 * private -0.0014 0.0004 -0.0005 0.0002 -0.0004 0.0002 2007 * private 0.0016 0.0005 0.0019 0.0003 0.0013 0.0002 2008 * private 0.0017 0.0005 0.0023 0.0003 0.0015 0.0002 2009 * private 0.0013 0.0005 0.0020 0.0003 0.0015 0.0002 2010 * private 0.0009 0.0006 0.0019 0.0004 0.0015 0.0003 Patient Char No No No GP F.E. Yes Yes Yes Trust F.E. Yes Yes Yes Year Dummies Yes Yes Yes Obs 2,039,070 2,039,070 2,039,070 R2 .4246 0.4246 0.4246

68. EVIDENCE OF CREAM-SKIMMING ON AGE Greater private competition leaves incumbents with older patient mix 333,000 Person Radius Counts 666,000 Person Radius Counts 999,000 Person Radius Counts Coef. Coef. S.E. S.E. Coef.Coef. S.E. S.E. Coef. Coef. S.E. S.E. Count public - - - - - - 2003 * public 0.3054 0.0599 0.0633 0.0381 -0.0271 0.0063 2004 * public 0.3119 0.0690 0.1456 0.0427 -0.0296 0.0071 2005 * public 0.3667 0.0720 0.1731 0.0451 -0.0295 0.0075 2006 * public 0.5144 0.0765 0.2439 0.0477 -0.0355 0.0079 2007 * public 0.5808 0.0784 0.2777 0.0485 -0.0508 0.0084 2008 * public 0.4904 0.0817 0.2387 0.0501 -0.0565 0.0089 2009 * public 0.5693 0.0851 0.2830 0.0525 -0.0734 0.0094 2010 * public 0.7575 0.0929 0.4122 0.0555 -0.0801 0.0095 Count private - - - - - - 2003 * private 0.0562 0.0539 0.1256 0.0342 0.0742 0.0268 2004 * private 0.1774 0.0593 0.1515 0.0380 0.0755 0.0284 2005 * private 0.2427 0.0646 0.2041 0.0414 0.1191 0.0316 2006 * private 0.1768 0.0666 0.2018 0.0434 0.1229 0.0331 2007 * private 0.2607 0.0681 0.2551 0.0454 0.1671 0.0337 2008 * private 0.3771 0.0720 0.3039 0.0469 0.2170 0.0353 2009 * private 0.4621 0.0758 0.3534 0.0486 0.2884 0.0367 2010 * private 0.3986 0.0849 0.3107 0.0556 0.2416 0.0399 Patient Char No No No GP F.E. Yes Yes Yes Trust F.E. Yes Yes Yes Year Dummies Obs 2,039,070 2,039,070 2,039,070 R2 0.3430 0.3430 0.3430

69. Test of the counterfactual

70. TEST OF THE COUNTERFACTUAL Results suggest that hospital position, not population density are driving main findings Length of stay Age Socio-economic status Charlson index Coef. S.E. Coef. S.E. Coet. S.E. Coef. S.E. Population density - - - - - - - - 2003 * pop. density 0.0001 0.0003 -0.0006 0.0017 0.0000 0.0000 0.0002 0.0001 2004 * pop. density 0.0004 0.0003 -0.0008 0.0017 0.0000 0.0000 0.0004 0.0001 2005 * pop. density -0.0002 0.0003 -0.0016 0.0018 0.0000 0.0000 0.0005 0.0001 2006 * pop. density -0.0001 0.0003 0.0002 0.0018 0.0000 0.0000 0.0003 0.0001 2007 * pop. density -0.0004 0.0003 -0.0031 0.0017 0.0001 0.0000 0.0004 0.0001 2008 * pop. density 0.0003 0.0003 -0.0033 0.0017 0.0001 0.0000 0.0000 0.0001 2009 * pop. density 0.0005 0.0003 -0.0040 0.0018 0.0001 0.0000 0.0001 0.0002 2010 * pop. density 0.0010 0.0003 -0.0030 0.0018 0.0001 0.0000 0.0000 0.0002 2003 -0.1732 0.0102 0.2786 0.0687 0.0000 0.0004 0.0040 0.0049 2004 -0.3693 0.0105 0.6565 0.0690 -0.0008 0.0004 0.0278 0.0052 2005 -0.5532 0.0112 0.8222 0.0687 -0.0010 0.0004 0.0660 0.0056 2006 -0.7821 0.0118 0.9772 0.0705 -0.0012 0.0004 0.0928 0.0058 2007 -1.0685 0.0122 1.3807 0.0690 0.0094 0.0004 0.1098 0.0060 2008 -1.2386 0.0126 1.6415 0.0696 0.0131 0.0004 0.1536 0.0064 2009 -1.3590 0.0127 1.4349 0.0712 0.0126 0.0005 0.1858 0.0069 2010 -1.5183 0.0128 1.4146 0.0727 0.0132 0.0005 0.2542 0.0073 Patient Char Yes No No No GP F.E. Yes Yes Yes Yes Trust F.E. Yes Yes Yes Yes Year Dummies Yes Yes Yes Yes Obs 2,039,070 2,039,070 2,039,070 2,039,070 R2 0.7576 0.3429 .4243 0.1075

71. QUANTIFYING THE PRODUCTIVITY GAINS We can calculate estimates of the cost/savings from reductions in LOS and rise of risk-selection • An excess bed day in England cost approximately £225.00 • Reducing LOS for our four procedures would result in: – 59,000 saved bed days; £13 million pounds in savings during that period • Across the NHS, this would result in: – 1.6 million fewer bed days; £356 million pounds savings • Across the NHS, savings from reducing pre-surgery LOS would be approximately £40.3 million pounds • From 2007 - 2010, the entrance of private providers left public hospitals treating older, poorer patients patients, which was associated with a cost, measured from post-surgery LOS alone, of £714,000 pounds per year

72. CONCLUDING THOUGHTS The introduction of hospital competition in England was associated with moderate productivity gains • Competition between public tertiary hospitals led to moderate gains of productive efficiency on the order of 4-9%. • Competition between public and private did not lead to increases in productivity; • The entrance of new private providers was associated with a small but significant increase in the age and deprivation of patients at incumbent hospitals – Cannot tell whether this was from these hospitals selecting against these patients or whether wealthier younger patients themselves preferred to go private • Clearly suggests that if we want to produce productivity gains, payments to hospitals needs to more accurately take into account factors which may potentially lead to higher costs

73. FURTHER WORK TO BE DONE ON THIS PAPER • Placebo test using LOS for AMI and emergency fractured neck of femur; • Test results using GLM estimator with negbin and gamma distributions • Test that results are robust when excluding London • Relax our assumption about the linear effect of competition

74. FURTHER RESEARCH

75. AN ANALYSIS OF HOSPITAL PRICING A series of papers using new data with US Hospital transaction prices • Claims data with transaction prices • Series of articles: – Documenting the variation in prices; – Examining the impact of rising prices on overall health care spending; – Examining the impact of hospital market structure on prices; – Examining whether hospital cost-shifting occurs: does a reduction in Medicare and Medicaid premiums lead to increases in the prices charged for private patients • Jointly with John Van Reenen (LSE) and Marty Gaynor (CMU)

76. INTERNATIONAL COMPARISON OF HOSPITAL PERFORMANCE • Patient-level data for the US, UK, Canada and the Netherlands • Working to create matching cohort • Examining whether there is more within or between country differences in hospital performance as a tool to determine the impact of larger health systems issues • Joint with Amitabh Chandra (Harvard), Therese Stukel (University of Toronto), Eddy Van Doorslaer (Erasumus University)

77. SUBSTANTIAL HOSPITAL PRICE VARIATION Component prices charged to a large private insurer for gallbladder removal at hospitals within a medium sized US market

LDI Research Seminar-Does Hospital Competition Improve Public Hospitals’ Productivity? Evidence from the English NHS Patient Choice Reforms

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