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Centre for Market and Public Organisation

Can pay regulation kill? Panel data evidence on the effect of labor markets on hospital

performanceEmma Hall, Carol Propper John Van Reenen

Feb 2008

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Motivation

• Unintended consequences of wage regulation– Pay setting (e.g. public sector) often has

“geographical equity” despite different local labor markets. Implies problems of labour supply - and poor performance - when outside labour mkts strong

• How do labour markets affect firm performance?– Hard to identify as wages reflect equilibrium outcomes

of demand and supply shocks. – In our design, pay regulation help identification

• Policy issue in hospital performance– What are causes of large performance variation (note

also large productivity dispersion in other industries)

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Our Design• Wages for nurses (and doctors) in UK National Health

Service centrally set by National Pay Review Body. NPRB “Mandates” wage rates for doctors and nurses by grade. Uprated each year.

• Very little local variation in regulated pay despite substantial local variation in total private sector– E.g. 65% private sector pay gap between North-East

England and Inner London but only 11% in NPRB regulated pay

– Use exogenous variation in “outside wage” and examine impact on hospital outcomes (quality, prody)

• Institutional setting one in which selection of patients to hospitals is limited

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Our Results• Main Finding: Hospitals in high outside wage

areas have lower hospital quality (higher AMI death rates) and lower output per head.

• Not result of general UK labour market conditions– Placebo experiments on similar sectors: no evidence

of negative effect of outside wages on productivity

• One mechanism: greater reliance on lower quality temporary/agency staff.

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Geographical variation in:

Outside wages Agency nurses In-hosp AMI deaths

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1. Models: What is the effect of pay regulation?

2. Empirical models

3. Data

4. Results

5. Conclusions

OUTLINE

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1. Effects of high outside wage relative to regulated wage

• Employers– try to circumvent by “over-promoting” (grade drift) and increasing

non-wage benefits. Limited by regulation/union enforcement– Substitution to other factors: health care assistants, maybe

capital. But limited due to nature of needed expertise.– Substitute temporary agency staff. Lower job-specific human

capital so less productive/lower quality (cf Autor & Houseman, 2006)

• Employees – Lower participation, higher vacancies for permanent staff– More likely to become agency staff.– Permanent staff also less motivated, lower relative quality

compared to low outside wage areas

Implication: Worse hospital performance in high outside wage areas

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Implications

• In high outside wage areas– Problems of labour supply for permanent staff

• higher vacancies• lower participation in nursing• Greater reliance on agency nurses

– Worse health outcomes• Lower quality (AMI death rate)• Lower productivity

– See this in raw data at regional level

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2. Empirical Models

dit

di

di

di

dit

dOit

dit

dNURSESit

dPHYSit

dit rzwwSSd 21

1. Hospital quality equation

For hospital i in year t:d = 30 day death rate from emergency AMI admission for 55+ year oldsSPHYS = share of clinical workforce who are physiciansSNURSES= share of clinical workforce who are nurses (and AHPs)(base group is health care assistants)wO = ln(outside wage)Z = controls for casemix, area mortality rates, hospital size, teaching statusw = ln(inside wage)η = hospital dummiesτ = time dummies, r=regional dummies

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itiiiitOitit

NURSESit

PHYSitit rzwwSSLY 21)/ln(

2. Hospital productivity equation

Ln(Y/L) = ln(Finished Consultant Episodes per clinical worker)SPHYS = share of clinical workforce who are physicians SNURSES= share of clinical workforce who are nurses (and AHPs)(base group is health care assistants)wO = ln(outside wage)Z = controls for casemix, area mortality rates, hospital size, teaching statusw = ln(inside wage)r = regional dummiesτ = time dummiesη = hospital dummies

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itiiiitOitit

QUALitit rzwwSLR 1)/ln(

3. Placebo productivity equation

Ln(R/L) = ln(revenues/worker)SQUAL = share of workforce who are qualified (nursing homes: with nursing quals; ln (cap/labor) ratio other industries)wO = ln(outside wage)Z = total staffing (+ gender mix, age of staff for nursing homes)w = ln(inside wage)r = regional effectsτ = time dummiesη = firm fixed effectRun for 42 industries + nursing homes

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Issues

• Unobserved heterogeneity: OLS, long differences and “System GMM”

• Endogeneity of wages and shares: – Outside wage: hospitals are a small % of local

labor market– Skill shares: GMM-SYS (Blundell-Bond,2000;

Bond and Soderbom, 2006)

• Standard errors allow for heteroscedacity, autocorrelation and clustering by region

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Issues

• Endogeneity of patient quality– Selection of hospitals– Association of illhealth and economic activity

• Hospital selection limited by inst. structure– AMI patients sent to nearest hosp.– Hospitals not monitored on quality; in theory financial

incentives exist but no systems to implement

• Upswings less associated with increase in hrs (due to higher labor protection); also undertake extensive checks to ensure no rel. between community health and ‘good times’

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3. Data

• Hospital level panel data• 3 groups of clinical workers: Physicians, nurses

(AHPs) and Health Care Assistants. Total employment. From Medical Workforce Statistics

• Agency staff – hospital financial returns• Hospital quality: 30 day in-hospital death rates

for Emergency admissions for Acute Myocardial Infarction (AMI) for over 55 year olds. From HES (Hospital Episode Statistics).

• Productivity: Finished Consultant Episodes (HES) per worker

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Wage Data

• Outside wage– New Earnings Survey (NES) 1% sample of all

workers– Use travel to work area (78 in England)– Compare results with 9 main regions– Female non-manual wage

• Inside Wage– Average wage in hospital (but can just reflect grades)– Predicted wage based on NPRB regulation including

regional allowances (Gosling-Van Reenen, 2006)

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Final Dataset

• 211 hospitals between 1996-2001

• 907 observations

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1. Models: What is the effect of pay regulation?

2. Empirical models

3. Data

4. Results

5. Conclusions

OUTLINE

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Dependent variable Ln(AMI Death Rate) Ln(AMI Death Rate) Ln(AMI Death Rate) Estimation technique OLS 3 year annual Long

Differences GMM-SYS

(1) (2) (3) Ln (Area outside wage) 0.407***

(0.124) 0.766** (0.386)

0.460*** (0.175)

Physicians share -0.856*** (0.316)

-0.654 (0.616)

-2.629** (1.258)

Qualified Nurses share -0.480** (0.227)

-0.288 (0.467)

-1.416 (0.959)

(omitted base is unqualified nurses/ health care assistants) Hospital fixed effects No No Yes Casemix controls (14) Yes Yes Yes Year dummies (6) Yes Yes Yes Region dummies (10) Yes No Yes SC(1) p-value 0.000 SC(2) p-value 0.142 Hansen-Sargan p-value 0.923 No of Hospitals 210 133 210 Observations 901 345 901

Table 2: Death Rates from AMI

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Magnitudes (col 3)

• From 90th to 10th percentile of area outside wage difference is a fall of 33%. Associated with– a 14% fall in death rates (a quarter of the 62% 90-10 spread)

• Increase in physician share from 10th to 90th percentile is 7 percentage points. Associated with– 37% fall in AMI death rates (60% of 90-10 diff)

• Effect on AMI death rates of outside wage not dissimilar magnitude to drug based medical interventions (aspirin, beta blockers) – 10% increase in outside wages leads to 1 pp increase in AMI

fatality – Heidenrich and McClellan (2001) increase use of aspirins by

70% resulted in 3.3 p.p fall in AMI mortality

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Dependent variable Ln(Productivity) Ln(Productivity) Ln(Productivity) Estimation technique OLS 3 year annual Long

Differences GMM-SYS

(1) (2) (3) Ln (Area outside pay) -0.662***

(0.145) 0.252 (0.279)

-0.551*** (0.181)

Physicians share 3.837*** (0.360)

0.248 (0.411)

3.909*** (0.898)

Nurses share 0.386* (0.201)

0.006 (0.216)

1.736*** (0.627)

(omitted base is unqualified nurses/health care assistants) Hospital fixed effects No No Yes Casemix controls (39) Yes Yes Yes Year dummies (6) Yes Yes Yes Region dummies (10) Yes No Yes SC(1) p-value 0.004 SC(2) p-value 0.462 Hansen-Sargan p-value 0.042 No of Hospitals 210 133 210 Observations 901 345 901

Table 3: Productivity (FCEs per employee)

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Placebo tests

• Nursing homes– Provide medical care and other care services

to elderly– Wages not regulated– 649 randomly selected homes: data for 1998

and 1999– No evidence from OLS regression that outside

pay associated with lower output (beds) per hour of staff time

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Other placebo tests

• 42 service industries

• Dependent variable ln(revenues/worker)

• Only in 7/126 regression was outside wage neg. and significant

• Inside wage significant in almost all

• Suggests our finding of neg. effect of outside wages is a result of regulated pay maxima

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A possible mechanism: Agency nurses

• Higher outside wages associated with significantly greater use of agency staff

• Doubling of agency staff increases AMI death rates by 5%; no remaining effect of outside wages

• Agency nurses disproportionately in A and E wards

• Less effect on outside wages in productivity equation, but agency use still significant

• Use of agency staff related to MRSA rates (for 2001-2002)

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Robustness checks

Upswings lead to poorer health in local labour market (e.g. Ruhm)

• Case-mix and local wages– AMI severity (HRG category) not related to outside wages– controls for HRG not significant for AMI deaths; total case-mix

not significant for prody

• Are outside wages associated with higher community death rates?– Our model implies weakly so – Ruhm type argument – strong positive relationship– We find weak n.s. positive relationship– Also find no relationship between two key drivers of poor health-

upswing relationship (pollution, smoking)

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Robustness checks

Outside labor market affecting ambulance care• More economic activity – slower road speeds (‘floor to

door’)– Control for ambulance speeds

• Poorer quality of ambulance crew (door to needle time’)– Ambulance crew have no autonomy over which hospital to go to;

administration of reperfusion (to stop clotting) by crews under 0.6%.

Other tests– Financial pressure– Dynamics– Regional heterogeneity in impact outside wage

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Conclusions

• Regulated pay costs lives (and productivity) in high outside wage areas– Higher death rates (and lower productivity) in areas

where labour markets are tight– Some of this affect seems to operate through greater

reliance on temporary agency staff– Not a feature of other UK service industries where

(maximum) pay regulation does not operate• Labour markets important for health on supply

side of medical care as well as demand side• Policy solution – allow wages to reflect local

labour market conditions?

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Back Up Slides

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Next Steps

• Other explanations – e.g. technology adoption (Acemoglu and Finkelstein, 2006)?

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Underlying structural model

• Hospitals choose mix of factors depending on environment and adjustment costs

• Factor with high adjustment costs changed more slowly

• Implies that lagged values predict future values• Empirical identification requires that adjustment

costs be sufficiently different across the factors to avoid weak instruments problems

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System GMM

itititititit utaaxy ;

1) Difference equation eliminates firm fixed effects

0][ , itsti uxE

Moment conditions allow use of suitably lagged levels of the variables as instruments for the first differences (assuming levels error term serially uncorrelated, see Arellano and Bond, 1991)

Equation of interest

for s > 1 when uit ~ MA(0), and for s > 2 when uit ~ MA(1), etc.

Test assumptions using autocorrelation test and Sargan

Problem of weak instruments with persistence series…..

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System GMM

2) Use lagged differences as instruments in the levels equationadditional moment conditions (Arellano and Bover, 1998; Blundell and Bond, 2000):

0)]([ , itisti uxE

Requires first moments of x to be time-invariant, conditional on common year dummies

Can test the validity of the additional moment conditions

We combine both sets of moments for difference and levels equations to construct “System GMM” estimator

We assume all firm level variables are endogenous, while industry level variables are exogenous in main specifications (relax in some specifications)

for s = 1 when uit ~ MA(0), and for s = 2 when uit ~ MA(1)

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Alternative to regulation

• Avoiding permanent pay increases (Houseman et al, 2003)– Pay more observable than in US– Differences in pay and quality across regions

are persistent

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Big spread in productivity between hospitals (Fig 3)

Note: productivity measured by finished consultant episodes per worker

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Mean Standard deviation Min Max AMI Variables AMI death rate (55 plus) 21.14 4.483 7.454 36.941 Total AMI deaths (55 plus) 79.99 33.83 13 294 Total AMI admissions (55 plus) 385.02 160.84 151 1,348 Productivity and FCE (finished Consultant Episodes) Productivity (total FCEs/ total clinical staffing) 31.17 7.57 12.09 65.12 Total FCEs 58,664.58 24,515.83 13,490 138,984 Staffing Variables Total clinical staffing (physicians + nurses + Allied Health Professionals + Health Care Assistants)

1675.79 692.25 398.61 4010.70

Physicians share of staffing 0.148 0.030 0.058 0.270 Qualified Nurses (plus qualified Allied Health Professionals) share

0.597 0.037 0.476 0.741

Health Care Assistants share 0.246 0.046 0.121 0.393 Hospital Expenditure Variables Share of expenditure on agency staff as a proportion of total expenditure (“Agency”)

0.034 0.028 0.001 0.163

Retained Surplus (£K) (745 obs) -206.1 1313.4 -11487 8505

Sample characteristics

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Wages Ln(Area outside wage) 9.60 0.140 9.27 9.99 Ln(nurse inside wage) 9.99 0.152 9.52 10.50 Ln(area inside wage) 10.09 0.110 9.53 10.45 Other variables Directly Standardized Mortality rate in local area (per 100,000)

723.43 77.13 518.73 944.21

Teaching trust 0.111 0.341 0 1 Proportion of emergency admissions (to total admissions) 0.411 0.082 0.224 0.808 Proportion of transfer admissions (to total admissions) 0.160 0.066 0 0.448 Proportion of AMI admissions with HRG code E11 .162 0.075 0 0.667 HRG case mix index (892 obs) 93.98 9.08 75.49 175.89 MRSA rate (216 obs) 0.169 0.088 0.02 0.55

Sample characteristics cont

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Large spread in death rates from AMI between hospitals

• Improvements over time (cf. TECH Investigators) • 1996: 10 percentage point (60%) difference between top and bottom (90th =27%,10th =17%)

Worst 10%

Best 10%

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Simple model

• 2 areas: high outside wage “South” and low outside wage “North”

• Regulated wage the same in both areas

• Regulated wage lower than equilibrium wage

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Wages

N, employmentNSOUTHNNORTH

Labour Supply, South

Labour Supply,North

Labor Demand

Regulated Wage

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Wages

N, employmentNSOUTH

Labour Supply, South

Labor Demand

Regulated Wage

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Wages

N, employmentNPERMANENT

Labour Supply, South

Labor Demand

Regulated Wage

Agency Wage

NTOTAL

Agency staff

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Dependent variable

Ln(AMI Death Rate)

Ln(AMI Death Rate)

Ln(AMI Death Rate)

Ln(AMI Death Rate)

Ln(Productivity)

Ln(Productivity)

Ln(Productivity)

Ln(Productivity)

Estimation technique

OLS Long Differences

GMM-SYS GMM-SYS OLS Long Differences

GMM-SYS GMM-SYS

(1) (2) (3) (4) (5) (6) (7) (8) Ln (Area outside pay)

0.406*** (0.122)

0.765** (0.384)

0.431** (0.172)

0.431** (0.172)

-0.659*** (0.144)

0.244 (0.282)

-0.547*** (0.172)

-0.548*** (0.180)

Average inside wage

-0.286*** (0.101)

-0.126 (0.161)

-0.334** (0.168)

0.071 (0.115)

0.097 (0.128)

0.241** (0.125)

Predicted ln(inside wage using NPRB IV)

-0.371 (0.716)

0.264 (0.342)

Physicians share

-0.498 (0.342)

-0.544 (0.641)

-1.787 (1.236)

-2.145* (1.286)

3.750** (0.390)

0.201 (0.394)

4.130*** (0.930)

3.979*** (0.904)

Nurses share

-0.313 (0.224)

-0.253 (0.471)

-0.910 (0.822)

-1.002 (0.856)

0.347* (0.207)

0.004 (0.212)

1.680*** (0.607)

1.734** (0.628)

(omitted base is unqualified nurses/health care assistants) SC(1) p-value

0.000 0.000 0.002 0.004

SC(2) p-value

0.162 0.173 0.436 0.485

Hansen- p-value

0.795 0.716 0.81 0.32

Table 4: Controls for inside wages

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Dependent variable

Ln(revenues/hour) Ln(revenues/hour) Ln(revenues/hour) Ln(revenues/hour) Ln(output/hour)

Estimation technique

OLS OLS OLS OLS OLS

(1) (2) (3) (4) (5) Ln (Area outside pay)

-0.009 (0.191)

0.095 (0.171)

0.125 (0.364)

-0.084 (0.228)

-0.075 (0.201)

Ln(Inside Pay) 0.166*** (0.031)

0.179*** (0.030)

0.166*** (0.037)

0.179*** (0.044)

0.049* (0.028)

Ln (average hours) -0.466*** (0.056)

Nursing Home fixed effects?

No No Yes Yes No

Year dummies (1) Yes Yes Yes Yes Yes Region dummies (10)

Yes Yes Yes Yes Yes

Number of Nursing Homes

649 649 443 513 649

Observations 1,054 1,054 886 513 1,068

Table 6: Placebo experiments: nursing homes

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A possible mechanism: Agency nurses

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Dependent variable

Ln(Agency)

Ln(AMI)

Ln(AMI)

Ln(AMI)

Ln (productivi

ty)

Ln (productivi

ty)

Ln (productivi

ty)

(1) (2) (3) (4) (5) (6) (7) Ln (Area outside pay)

2.851** (1.138)

0.314* (0.170)

0.175 (0.202)

-0.805*** (0.182)

-0.729*** (0.194)

Ln(Inside Pay)

0.077 (1.045)

-0.494*** (0.153)

-0.477*** (0.161)

0.219 (0.134)

0.296** (0.141)

Ln(Agency) 0.057** (0.026)

0.046* (0.024)

-0.106*** (0.027)

-0.057*** (0.018)

No. of hospitals

176 176 176 176 176 176 176

Observations 523 520 520 520 520 520 520

Figure 5: Agency Nurses, outside wages and AMI death rates

All regressions include hosp fixed effects, region dummies, year effects.

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Robustness checks: coefficient on outside wage

Dependent variable Ln(AMI) Ln(Productivity) Obs. (1) (2) (3) 1 Baseline 0.460**

(0.175) -0.547*** (0.172)

901

2 Additional casemix controls 0.427*** (0.170)

-0.556*** (0.153)

900 (for AMI) 892 (for prody)

3 Include hospital financial surplus 0.399** (0.182)

-0.516*** (0.184)

745

4 Include lagged dependent variable: long-run [p-value]

0.508*** [0.008]

-0.572*** [0.020]

901

5 Drop Inner and Outer London 0.304** (0.156)

-0.383** (0.173)

776

6 Drop big jumps in outside wage 0.530** (0.197)

-0.622*** (0.167)

885

7 Balanced Panel 0.600*** (0.207)

-0.612*** (0.163)

582

8 Regional outside wage 0.609 (1.022)

-0.445 (0.587)

901

9 Regional outside wage (drop regional dummies) 0.520*** (0.172)

-0.493** (0.169)

901

10 Include alternative total hospital employment measure

0.404** (0.160)

-0.540** (0.170)

901

11 Include higher order and cross product terms in skill shares

0.541*** (0.200)

-0.637*** (0.181)

901

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Cost effectiveness• Effect on AMI death rates of outside wage not dissimilar

magnitude to drug based medical interventions (aspirin, beta blockers) – 10% increase in outside wages leads to 1 pp increase in AMI fatality;

Heidenrich and McClellan (2001) increase use of aspirins by 70% resulted in 3.3 p.p fall in AMI mortality

• Cost of a life year saved by an 1% increase in (inside) nurse wages to all staff and an 1 p.p. increase in physician and nurses skill shares– Increasing inside wages: $100,000 – physician share: $60,000 – nurse share: $36,000 – Value of QALY c $60,000

• Comparison with greater use of drug based medical technology, increasing wages for nurses and skill shares in hospitals expensive, but cheaper than the current cost of AMI treatment in the US (Skinner et al 2006)

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Higher nurse vacancy rates1 in stronger labor markets (fig 4)

mean ln(outside wage)

Vacancy Rates for nurses predicted vacancy rate

9.4 9.6 9.8 10

1

2

3

4

5

North Ea

East MidYorkshirSouth We

West Mid

North We

East of

South Ea

Outer Lo

Inner Lo

1 Percentage of nurse posts that have been vacant for 3 months or more

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Higher use of agency nurses in stronger labor markets (Fig 6)

mean ln(outside wage)

Intensity of using agency nurse predicted Agency rate

9.4 9.6 9.8 10

0

2

4

6

North Ea

East MidYorkshir

South We

West Mid

North We

East of South Ea

Outer LoInner Lo

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mean ln(outside wage)

AMI Rate AMI = 1.96*W -0.10W2

9.4 9.6 9.8 10

20

21

22

23

North EaEast Mid

Yorkshir

South We

West Mid

North We

East of

South Ea

Outer LoInner Lo

Higher death rate from AMI admissions in stronger labor markets (fig 7)

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Changes in AMI death rates and changes in outside wages

av. outside wage gr 1996-2001

AMI growth pa 1996-2001

.044 .046 .048 .05 .052

-.06

-.04

-.02

0

North We South We

East of

Outer Lo

Inner Lo

East MidWest Mid

North EaSouth Ea

Yorkshir

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Magnitudes

• From 90th to 10th of area outside wage difference is a fall of 33%, associated with:– a 16% increase in productivity (a quarter of

the 90-10 productivity difference)

• Increase in physician share from 90th to 10th is 7 percentage points– 35% increase in productivity (58% of the 90-

10 diff)