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1 Centre for Market and Public Organisation Evidence on the impact of pay regulation on hospital quality and productivity or Can pay regulation kill? Emma.

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Presentation on theme: "1 Centre for Market and Public Organisation Evidence on the impact of pay regulation on hospital quality and productivity or Can pay regulation kill? Emma."— Presentation transcript:

1 1 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)

2 2 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).

3 3 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 (90 th =27%,10 th =17%) Worst 10% Best 10%

4 4 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.

5 5 Highest outside wage Lowest outside wage Geographical variation in Outside wages London Manchester Birmingham

6 6 High intensity of agency nurses Low intensity of agency nurses Geographical variation in use of agency nurses London

7 7 High AMI death rates Low AMI death rates Geographical variation in emergency AMI death rates London

8 8 1.Models: What is the effect of pay regulation? 2.Empirical models 3.Data 4.Results 5.Conclusions OUTLINE

9 9 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

10 10 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

11 11 Wages N, employmentN SOUTH N NORTH Labour Supply, South Labour Supply, North Labor Demand Regulated Wage

12 12 Wages N, employmentN SOUTH Labour Supply, South Labor Demand Regulated Wage

13 13 Wages N, employmentN PERMANENT Labour Supply, South Labor Demand Regulated Wage Agency Wage N TOTAL Agency staff

14 14 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?

15 15 Higher nurse vacancy rates 1 in stronger labor markets (fig 4) 1 Percentage of nurse posts that have been vacant for 3 months or more

16 16 Lower nurse participation rate in stronger labor markets (fig 5) Note: participation rate is the % of women with nursing qualifications who are working as nurses

17 17 Higher use of agency nurses in stronger labor markets (Fig 6)

18 18 Higher death rate from AMI admissions in stronger labor markets (fig 7)

19 19 1.Models: What is the effect of pay regulation? 2.Empirical models 3.Data 4.Results 5.Conclusions OUTLINE

20 20 2. Empirical Models 1. Hospital quality equation For hospital i in year t: d = 30 day death rate from emergency AMI admission for 55+ year olds S PHYS = share of clinical workforce who are physicians S NURSES = share of clinical workforce who are nurses (and AHPs) (base group is health care assistants) w O = ln(outside wage) Z = controls for casemix, area mortality rates, hospital size, region dummies, etc w = ln(inside wage) η = hospital fixed effect τ = time dummies

21 21 2. Hospital productivity equation Ln(Y/L) = ln(Finished Consultant Episodes per clinical worker) S PHYS = share of clinical workforce who are physicians S NURSES = share of clinical workforce who are nurses (and AHPs) (base group is health care assistants) w O = ln(outside wage) Z = controls for casemix, area mortality rates, hospital size, area, etc w = ln(inside wage) η = hospital fixed effect τ = time dummies

22 22 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

23 23 1.Models: What is the effect of pay regulation? 2.Empirical models 3.Data 4.Results 5.Conclusions OUTLINE

24 24 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

25 25 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

26 26 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)

27 27 Final Dataset 211 hospitals between observations

28 28 1.Models: What is the effect of pay regulation? 2.Empirical models 3.Data 4.Results 5.Conclusions OUTLINE

29 29 Dependent variableLn(AMI Rate) Estimation techniqueOLSLong 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) ** (0.883) ** (2.753) Nurses share-0.524* (0.276) ** (0.638) * (1.262) Hospital fixed effectsNo Yes No of Hospitals211 Observations 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

30 30 Magnitudes (col 3) From 90 th to 10 th percentile of area outside wage difference is a fall of 33%, associated with: –a 14% fall in death rates (a quarter of the 62% spread) Increase in physician share from 10 th to 90 th percentile is 7 percentage points. Associated with –37% fall in AMI death rates (60% of diff)

31 31 Dependent variableLn(Productivity) Estimation techniqueOLSLong Differences (3 years) GMM-SYS Ln (Area outside pay) *** (0.159) (0.275) ** (0.230) Physicians share5.552*** (0.434) 2.869*** (0.507) 4.654*** (0.905) Nurses share0.149 (0.225) 1.071*** (0.369) 1.523** (0.701) Hospital fixed effectsNo Yes No of Hospitals211 Observations 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

32 32 Magnitudes From 90 th to 10 th of area outside wage difference is a fall of 33%, associated with: –a 16% increase in productivity (a quarter of the productivity difference) Increase in physician share from 90 th to 10 th is 7 percentage points –35% increase in productivity (58% of the diff)

33 33 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)

34 34 Dependent variable Ln(Agency)Ln(AMI) (1)(2)(3)(4) Ln (Area outside pay) 2.557*** (1.131) 0.423** (0.189) (0.254) Ln(Agency)0.091*** (0.028) 0.076** (0.029) SC(2) p- value Hansen- Sargan p- value Number of hospitals 177 Observations523 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.

35 35 Dependent variable Ln(Agency)Ln(productivity) (1)(5)(6)(7) Ln (Area outside pay) 2.557*** (1.131) ** (0.232) * (0.235) Ln(Agency)-0.100*** (0.031) ** (0.021) SC(2) p- value Hansen- Sargan p- value Number of hospitals 177 Observations523 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.)

36 36 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)

37 37 Dependent variableLn(AMI)Ln(Productivity) 1Baseline0.431** (0.187) ** (0.230) 2Internal market period interaction with outside wage Linear outside wage * (0.119) 0.608*** (0.204) (0.092) * (0.229) 3Include hospital financial surplus0.397* (0.199) ** (0.250) 4Include a lagged dependent variable: long-run effect [p-value] 0.506** [0.02] ** [0.045] 5Drop Inner and Outer London0.314* (0.174) * (0.224) 6Drop big jumps in outside wage0.488** (0.227) ** (0.201) 7Drop Regional Dummies0.674*** (0.207) ** (0.170) 8Regional outside wage1.352 (1.104) (0.598) 9Regional outside wage (drop regional dummies) 0.735*** (0.224) ** (0.167) 10Include alternative total hospital employment measure 0.364** (0.178) ** (0.184) Table 6: Robustness Checks

38 38 1.Models: What is the effect of pay regulation? 2.Empirical models 3.Data 4.Results 5.Conclusions OUTLINE

39 39 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?

40 40 Back Up Slides

41 41 Next Steps Policy simulations What is it about agency staff that is the problem? Other explanations – e.g. technology adoption (Acemoglu and Finkelstein, 2006)?

42 42 Dependent variableLn(AMI) Ln(Productivity) Estimation technique GMM-SYS Average inside wage (0.188) 0.249** (0.096) Predicted ln(inside wage using NPRB IV) (1.113) (0.698) Ln (Area outside pay) 0.427** (0.210) 0.449** (0.190) *** (0.216) ** (0.215) Physicians share-4.096** (2.228) ** (2.336) 2.911*** (1.008) 5.798** (1.002) Nurses share-1.945* (1.153) ** (1.134) (0.601) 1.579* (0.610) No of Hospitals211 Observations706 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

43 43 Single Regulated Wage in areas of differential outside wage

44 44 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

45 45 System GMM 1) Difference equation eliminates firm fixed effects 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 u it ~ MA(0), and for s > 2 when u it ~ MA(1), etc. Test assumptions using autocorrelation test and Sargan Problem of weak instruments with persistence series…..

46 46 System GMM 2) Use lagged differences as instruments in the levels equation additional moment conditions (Arellano and Bover, 1998; Blundell and Bond, 2000): 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 u it ~ MA(0), and for s = 2 when u it ~ MA(1)

47 47 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

48 48 Dependent variableLn(Productivity) Estimation techniqueGMM-SYS Ln (Area outside pay/inside pay) *** (0.196) * (0.270) * (0.261) Physicians share3.683*** (1.181) 4.107*** (1.555) 5.068*** (1.168) Hospital fixed effectsyes Yes No of Hospitals Observations Table A: Productivity (FCEs per employee) – Controlling for relative costs index All columns include controls for nurses share, area mortality rates, year dummies ( ), casemix control, region dummies, hospital size

49 49 Dependent variableLn(Productivity) Estimation techniqueGMM-SYS Ln (Area outside pay) *** (0.186) *** (0.163) * (0.143) Ln (Inside pay)0.585*** (0.115) 0.195* (0.105) Physicians share4.252*** (0.395) Nurse share 0.620** (0.205) No of Hospitals 211 Observations706 Table B: Productivity (FCEs per employee) – Show effect of adding inside wage and skill mix sequentially All columns include controls for nurses share, area mortality rates, year dummies ( ), casemix control, region dummies, hospital size

50 50 Big spread in productivity between hospitals (Fig 3) Note: productivity measured by finished consultant episodes per worker

51 51 MeanStandard deviation MinMax AMI Variables AMI death rate (55 plus) Total AMI deaths (55 plus) Total AMI admissions (55 plus) Productivity and FCE (finished Consultant Episodes) Productivity (total FCEs/ total staffing) Total FCEs58, , ,490138,984 Staffing Variables Total staffing (physicians+nurses+AHP+Health Care Assistants) Physicians share of staffing Nurses (plus qualified Allied Health Professionals) share of staffing Hospital Expenditure Variables Share of expenditure on Agency staff as a proportion of total expenditure Wage Variables Ln(Area outside wage) Ln(Predicted NPRB wage) Table 1: Descriptive Statistics


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