1 Centre for Market and Public Organisation Evidence on the impact of pay regulation on hospital...

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Centre for Market and Public Organisation

Evidence on the impact of pay regulation on hospital quality and productivity

or Can pay regulation kill?Emma Hall, Carol Propper John Van Reenen

(Preliminary)

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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 labor supply and poor hospital performance when outside labor mkts strong

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

of demand and supply shocks. Regulated pay helps 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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Large spread in death rates from AMI between hospitals (Fig 2)

• 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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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)

• Main Finding: Hospitals in high outside wage areas have lower hospital quality (higher AMI death rates) and lower output per head. One mechanism: greater reliance on lower quality temporary/agency staff.

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Highest outside wage

Lowest outside wage

Geographical variation inOutside wages

London

Manchester

Birmingham

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High intensity of agency nurses

Low intensity of agency nurses

Geographical variation in use of agency nurses

London

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High AMI death rates

Low AMI death rates

Geographical variation in emergency AMI death rates

London

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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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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


Labor Demand

Regulated Wage

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Wages

N, employmentNPERMANENT


Labor Demand

Regulated Wage

Agency Wage

NTOTAL

Agency staff

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Implications

• In high outside wage areas– Problems of labor 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

– What do we see in data?

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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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Lower nurse participation rate in stronger labor markets (fig 5)

nurse relative pay premium

nurse participation rate Predicted participation rate

-.3 -.2 -.1 0 .1

.4

.5

.6

.7

.8

Lndon-In

SouthEas

Lndon-Ou

NorthWes

SthWest

Mdlnds-W

E.Anglia

Mdlnds-E

North

Yorks

Note: participation rate is the % of women with nursing qualifications who areworking as nurses

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


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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AMI Rate AMI = 1.96*W -0.10W2

9.4 9.6 9.8 10

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21

22

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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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2. Empirical models

3. Data

4. Results

5. Conclusions

OUTLINE

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2. Empirical Models

dit

di

di

dit

dOit

dit

dNURSESit

dPHYSit

dit zwwSSd 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, region dummies, etcw = ln(inside wage)η = hospital fixed effectτ = time dummies

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itiiitOitit

NURSESit

PHYSitit zwwSSLY 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, area, etcw = ln(inside wage)η = hospital fixed effectτ = time dummies

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Issues

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

• Endogeneity: – 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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2. Empirical models

3. Data

4. Results

5. Conclusions

OUTLINE

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3. Data

• New 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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Casemix

• AMI – Do not have co-morbidity– Demographics of those admitted for AMI (14 gender

age-bands)– Control for hospital fixed effects– Mortality rate in area– Drop hospitals with under 150 AMI cases per year

• Productivity– 36 age-gender groups– Type of admission– Control for fixed effects– Experiment with conditioning on relative cost index

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Wage Data

• Outside wage– New Earnings Survey (NES) 1% sample of all workers– Use travel to work area (100 in England)– Compare results with 9 main regions– Female non-manual wage – Robustness: all females, all non-manuals, average wage,

unemployment rates– Labor Force Survey (like CPS) “corrected” spatial wages taking

nurse characteristics into account

• 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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2. Empirical models

3. Data

4. Results

5. Conclusions

OUTLINE

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Dependent variable Ln(AMI Rate) Ln(AMI Rate) Ln(AMI Rate)

Estimation technique OLS Long Differences (3 years)

GMM-SYS

Ln (Area outside pay)

0.303**(0.150)

0.823**(0.381)

0.431**(0.188)

Physicians share -1.107***(0.359)

-2.198**(0.883)

-5.267**(2.753)

Nurses share -0.524*(0.276)

-1.435**(0.638)

-2.194*(1.262)

Hospital fixed effects No No Yes

No of Hospitals 211 211 211

Observations 907 348 907

Table 2: Death Rates from AMI

All columns include controls for area mortality rates, year dummies, casemix control, region dummies, hospital size (employment). HCA (Health Care Assistants) is base skill group

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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)

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Dependent variable Ln(Productivity) Ln(Productivity) Ln(Productivity)

Estimation technique OLS Long Differences (3 years)

GMM-SYS


-0.454***(0.159)

0.241(0.275)

-0.495**(0.230)

Physicians share 5.552***(0.434)

2.869***(0.507)

4.654***(0.905)

Nurses share 0.149(0.225)

1.071***(0.369)

1.523**(0.701)

Hospital fixed effects No No Yes



Table 3: Productivity (FCEs per employee)

All columns include controls for area mortality rates, year dummies, casemix control, region dummies, hospital size (employment). HCA is base skill group

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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)

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A possible mechanism: Agency nurses

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

• Greater use of agency staff associated with lower hospital quality

• Quantitatively, agency staff appear to account for c.70% of the effect of outside wages on AMI death rates

• Agency staff also lowers productivity (maybe 10%+ of outside wage effect)

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Dependent variable

Ln(Agency) Ln(AMI) Ln(AMI) Ln(AMI)

(1) (2) (3) (4)


2.557***(1.131)

0.423**(0.189)

0.131(0.254)

Ln(Agency) 0.091***(0.028)

0.076**(0.029)

SC(2) p-value

.132 .126 0.239 .180

Hansen-Sargan p-value

.390 .128 0.124 .161

Number of hospitals

177 177 177 177

Observations 523 523 523 523

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

All columns include all controls in Table 2 (skills, year dummies, casemix control, region dummies, area mortality, etc.). Estimation by GMM-SYS.

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Dependent variable

Ln(Agency) Ln(productivity) Ln(productivity) Ln(productivity)

(1) (5) (6) (7)


2.557***(1.131)

-0.703**(0.232)

-0.622*(0.235)

Ln(Agency) -0.100***(0.031)

-0.046**(0.021)

SC(2) p-value

.132 .126 0.239 .180

Hansen-Sargan p-value

.390 .128 0.124 .161

Number of hospitals

177 177 177 177


Figure 5 – cont.: Agency Nurses, outside wages and Productivity

All columns include all controls in Table 2 (skills, year dummies, casemix control, region dummies, etc.)

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Robustness (Table 6)

• Internal Market (pre-1997 more flexibility). Row 2• High outside wages implies higher costs (e.g.

rents), financial distress and worse outcomes. Row 3

• Not regulation? Houseman et al (2003) US case studies: (i) buffer, (ii) “hidden” monopsony, (iii) screening. BUT long-run effects in our data (figures and dynamics row 4)

• Model implies effects should be stronger in South – drop London (row 5)

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Dependent variable Ln(AMI) Ln(Productivity)

1 Baseline 0.431**(0.187)

-0.495**(0.230)

2 “Internal market” period interaction with outside wageLinear outside wage

-0.222*(0.119)0.608***(0.204)

-0.133(0.092)-0.394*(0.229)

3 Include hospital financial surplus 0.397*(0.199)

-0.441**(0.250)

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

0.506**[0.02]

-0.487**[0.045]

5 Drop Inner and Outer London 0.314*(0.174)

-0.375*(0.224)

6 Drop big jumps in outside wage 0.488**(0.227)

-0.549**(0.201)

7 Drop Regional Dummies 0.674***(0.207)

-0.428**(0.170)

8 Regional outside wage 1.352(1.104)

-0.429(0.598)

9 Regional outside wage (drop regional dummies)

0.735***(0.224)

-0.390**(0.167)

10 Include alternative total hospital employment measure

0.364**(0.178)

-0.383**(0.184)

Table 6: Robustness Checks

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2. Empirical models

3. Data

4. Results

5. Conclusions

OUTLINE

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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 labor

markets are tight– Much of this affect seems to operate through greater reliance on

temporary agency staff

• Also find that skill mix matters for hospital outcomes• Labor markets important for health on supply side of

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

conditions?

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Back Up Slides

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Next Steps

• Policy simulations

• What is it about agency staff that is the problem?

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

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Dependent variable Ln(AMI) Ln(AMI) Ln(Productivity) Ln(Productivity)

Estimation technique

GMM-SYS GMM-SYS GMM-SYS GMM-SYS

Average inside wage

-0.240(0.188)

0.249**(0.096)

Predicted ln(inside wage using NPRB IV)

-0.693(1.113)

0.237(0.698)


0.427**(0.210)

0.449**(0.190)

-0.688***(0.216)

-0.476**(0.215)

Physicians share -4.096**(2.228)

-6.046**(2.336)

2.911***(1.008)

5.798**(1.002)

Nurses share -1.945*(1.153)

-2.375**(1.134)

-0.113(0.601)

1.579*(0.610)

No of Hospitals 211 211 211 211


Table 4: Inside Wage controls

All columns include controls for area mortality rates, year dummies, casemix control, region dummies, hospital size (employment). HCA is base skill group

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Single Regulated Wage in areas of differential outside wage

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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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Estimation technique GMM-SYS GMM-SYS GMM-SYS

Ln (Area outside pay/inside pay)

-0.586***(0.196)

-0.527*(0.270)

-0.491*(0.261)


4.107***(1.555)

5.068***(1.168)

Hospital fixed effects yes yes Yes



Table A: Productivity (FCEs per employee) – Controlling for relative costs index

All columns include controls for nurses share, area mortality rates, year dummies (1999-2002), casemix control, region dummies, hospital size

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Estimation technique GMM-SYS GMM-SYS GMM-SYS


-0.545***(0.186)

-0.575***(0.163)

-0.503*(0.143)

Ln (Inside pay) 0.585***(0.115)

0.195*(0.105)


Nurse share 0.620**(0.205)



Table B: Productivity (FCEs per employee) – Show effect of adding inside wageand skill mix sequentially

All columns include controls for nurses share, area mortality rates, year dummies (1999-2002), casemix control, region dummies, hospital size

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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) 21.125 4.520 2.964 36.941

Total AMI deaths (55 plus) 7993.624 3382.425 1100 29400

Total AMI admissions (55 plus) 384.958 160.261 151 1348

Productivity and FCE (finished Consultant Episodes)

Productivity (total FCEs/ total staffing) 30.981 7.718 5.094 65.121

Total FCEs 58,620.82 2,441.15 13,490 138,984

Staffing Variables

Total staffing (physicians+nurses+AHP+Health Care Assistants)

1909.447 774.049 432.9 4269.83

Physicians share of staffing 0.130 0.030 0.047 0.249

Nurses (plus qualified Allied Health Professionals) share of staffing

0.646 0.034 0.493 0.765

Hospital Expenditure Variables

Share of expenditure on Agency staff as a proportion of total expenditure

0.035 0.028 0.001 0.163

Wage Variables

Ln(Area outside wage) 9.602 0.141 9.272 9.987

Ln(Predicted NPRB wage) 9.711 0.088 9.558 9.991

Table 1: Descriptive Statistics

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