Elliott, Robert

English

Aberdeen University Research

Briefing paper Jan 09BackgroundIn the financial year 2008-09 the Treasury's currentexpenditure was £441 billion. 25% (£111 billion) wasspent on health with just under £96 billion going to theNHS in England. Central government funds for thehealth service in England are distributed according to aformula. The formula measures the need for healthservices and variations in the costs of providing theseservices. The adjustment for cost takes account ofunavoidable differences in the local price of labour, landand the other inputs that are required to provide theseservices and is called the Market Forces Factor (MFF).This research reviewed the theoretical underpinnings forthat formula and hence the case for an MFF. Theresearch was commissioned as part of a review of theresource allocation formula conducted by theDepartment of Health in England.The theory underpinning the MFF is that the recruitmentand retention of medical and nursing professionals tothe NHS is strongly influenced by what happens in1. The costs ofrecruiting andretaining NHSprofessionals differbetween localities inEngland2. The formula thatdistribute funding for thehealth service shouldinclude a Market ForcesFactor (MFF) to reimbursethese cost differences3. An MFF isappropriate fornurses but notdoctorsH E A L T H E C O N O M I C S R E S E A R C H U N I TKey MessagesDISTRIBUTING PUBLIC FUNDINGTO THE NHS IN ENGLAND:TAKING ACCOUNT OFDIFFERENCES IN LOCAL LABOURMARKET CONDITIONS ON NHSRECRUITMENT AND RETENTIONBriefing Paperprivate sector labour markets. We observe that pay inthe private sector differs between regions. Thesedifferences are driven by the need to compensateemployees for differences in the cost-of-living and theamenities (the attractiveness) of working in differentparts of England. The regional pattern of private sectorpay therefore indicates the appropriate pattern of pay forNHS staff and is used to generate the MFF.However the way that pay is set in the NHS is verydifferent from the way it is set in the private sector. Inthe NHS it is set by Review Bodies, which establishnational, UK-wide, rates of pay. Pay in the NHS revealsmuch less regional variation than pay in the privatesector. As a result the wage premium for working in thehighest cost, least attractive area is less in the NHS thanit is in the private sector. A stylised representation of thedifferences in the regional patterns of pay in the twosectors is shown in Figure 1.Figure 1: Regional Patterns of Pay after controllingfor all Measurable DifferencesWhere the regional pattern of pay in the private sectordiffers from the regional pattern for pay in the NHS, thiswill affect the ability of the NHS to attract and retain thestaff it needs. The NHS will experience higher indirectlabour costs, higher labour turnover and vacancies, inthose areas in which the regional wage premium isless than in the private sector.The regional patterns of pay in the private sector andNHS can be identified and mapped by estimatingStandardised Spatial Wage Differentials (SSWDs).SSWDs control for all measurable differences betweenregions in the composition of the workforce andindustries, for we know that that such factors alter paylevels. Once we have controlled for these differenceswe can test for an association between differences inthe patterns of pay and measures of recruitment andretention in the NHS. In the study vacancy rates wereused to measure recruitment and retention, andmultiple regression techniques are used to estimate therelationship between them and the regional patterns ofpay.MethodSSWDs for the private sector are generated byestimating:ln(wij) = x' β + vj + εijwhere wij is the hourly earnings of individual i whoworks in the private sector of the economy in area j,the vector x contains all the control variables (age,age2, gender, year dummies, industry dummies andoccupational dummies), vj are the area-specific effectsand εij are the individual-specific error terms. The area-specific effects represent the SSWDs and are estimatedusing a dummy variable for each area.The SSWDs are expressed on the log scale but the MFFis required as an adjustment on the original wagescale. The MFF is obtained by exponentiating thesmoothed SSWDs and expressing these relative to theSSWD for all of Britain (v) :MFFj = 100*We tested for an association between vacancies andthe SSWD gap, calculated as the private sector SSWDminus the NHS SSWD. Thus it measures differencesbetween the premia employees in the private sectorand those in the NHS receive for working in a particulararea. The specification is as follows:Vjk = α + γ (SSWD RIVATE – SSWD NHS ) + z'δ + εjk= α + γ SSWD GAP+ z'δ + εjkwhere the dependent variable is the vacancy rate, kdenotes the kth hospital trust which is located withinLAD j, SSWDNHS is the SSWD for the NHS staff group attrust k, SSWDPRIVATE is the SSWD for the private sector inLAD j, and z is a vector of control variables. We expectto find a positive relationship between the MFF gapand vacancies.DataThe empirical analysis uses improved data from theAnnual Survey of Hours and Earnings (ASHE). ASHE isan annual employer survey where the sample frame isa 1% random sample of the employed population.This gives approximately 200,000 observations peryear. The data was made available at the individuallevel for 2003-5. The data include part-timers, usesthe ASHE employee population weights and poolsobservations across three years. The geography oflabour markets in England is specified as 354 LocalAuthority Districts (LADs).ResultsTable 1 reveals the importance of standardising in theconstruction of the MFF. The results show clearly thatas we add in further controls in Columns (2) to (4) thescale of wage variation decreases; the standarddeviation and the decile range steadily decrease, theminimum increases and the maximum reduces. Theadjusted R2 rises steadily from 0.139 without controlsto 0.640 using the full set of controls available.RegionalPayVariationsCost of living and lowest amenity areaHighestLowestPrivate SectorNHSexp(vj)exp(v)j kjkkjTable 2: Zero Inflated Negative Binomial regression model of number of nurse vacanciesSource of NHS pay data ASHEVariable Coefficient Standard Deviation SignificanceSSWD Gap (GLM-NHS) 0.023 0.006 0.000Log likelihood -1250.956Cragg & Uhler’s R2 0.377Vuong test (ZINB vs NB) 0.72 (sig = 0.2358)Dispersion = Nr of established posts, Nr of obs = 260 hospital trusts. Standard errors are clustered by LAD geography and are robust. All estimations are inflated by thegap SSWD, other variables are excluded from the inflation process due to multicollinearity.In Table 2 the results of the model for nurses isreported. The gap between the SSWDs for allemployees in the private sector and SSWDs for NHSnurses, (GLM-NHS) is constructed using data fromASHE. For nurses, the MFF gap is significantly(p<0.05) and positively associated with vacancies aspredicted: the greater the gap between the regionalpremia in the private sector and those in the NHS thehigher the NHS nursing vacancy rate. Controls areincluded but not reported for hospital types.The results are different for doctors; they are presentedin Table 3. The relationship between vacancy ratesand the MFF gap is negative. Vacancies are lowestwhere the gap is higher – vacancies are lower in highcost areas. The model was extended by including ameasure of the private earnings of doctors in differentareas. If doctors earn more from private practice inhigh cost areas this may explain lower vacancy ratesin these areas. However it is insignificant - though thismay be because it is measured at an aggregateStrategic Health Authority level. The coefficients on allthe dummies for hospital type are positive. Inparticular, the coefficient for Acute, Community, Multi-services and Others hospital types are significant,indicating that these hospitals find it easier to fillvacancies relative to the reference category, AcuteSpecialist hospitals.Table 1: Distribution of estimated MFFs with different control variablesStatistic (1) (2) (3) (4) (5)LAD Model (1) Model (2) Model (3) Model (4)identifiers with age and with with industry with part-only sex dummies occupational dummies time dummyadded dummies added addedaddedMean 103.3 103.1 101.3 101.2 101.2Standard Dev. 21.2 19.6 10.9 10.2 10.1Minimum 61.5 72.6 80.8 82.1 82.510th centile 84.4 85.7 91.2 91.8 91.650th centile 98.1 97.9 98.6 98.5 98.490th centile 128.6 126.4 114.4 114.9 114.7Decile range 44.2 40.7 33.2 23.1 23.1Maximum 250.4 237.7 166.6 159.4 158.6Adj. R2 0.139 0.334 0.621 0.639 0.640For the calculation of these statistics, each LAD value assumes equal weight. The decile range is the difference between the 90th and 10th centiles of the distribution.HERU is supported by the Chief ScientistOffice (CSO) of the Scottish GovernmentHealth Directorates (SGHD) and theUniversity of Aberdeen. The views expressedhere are not necessarily those of the CSO.CONTACT US . . .HEALTH ECONOMICS RESEARCH UNITInstitute of Applied Health Sciences,Polwarth Building, Foresterhill, Aberdeen AB25 2ZDTel: +44 (0) 1224-553480/553733Fax: +44 (0) 1224-550926Email: heru@abdn.ac.uk www.abdn.ac.uk/heruElliott, R. A Ma, A Scott, D Bell, and E Roberts(2007) “Geographically differentiated pay in thelabour market for nurses”, Journal of HealthEconomics, Vol. 26, Issue 1, pp. 191-212.Further information about this topic can be obtainedby contacting Bob Elliott, HERU, University ofAberdeen, Foresterhill, AB25 2ZD (tel: 01224553733; e-mail: r.elliott@abdn.ac.uk).ReferencesDiscussionThe analysis reveals a significant association betweenthe spatial pattern of pay in the private sector and thevacancy rates of nurses. The findings reveal that thelabour markets in which the NHS recruits nurses areconnected to the private sector and the differences inthe patterns of regional pay variations between theNHS and private sector alter the recruitment andretention of nurses. These findings provide empiricalunderpinnings for the application of an MFF for nurses.In contrast the analysis reveals that there is noassociation between the spatial pattern of privatesector pay and NHS doctor vacancy rates. Indeed theanalysis suggests that doctors are attracted to areaswhere the cost of living is higher and area amenitiesare lower. The analysis shows that the choice over joblocation for medical staff is determined less by currentpay than by other factors and hospitals located in highcosts areas find it easier to fill medical vacancies.There is no case for an MFF for doctors.Table 3: Zero Inflated Negative Binomial regression model of number of doctor vacanciesModel GLMVariable Coefficient Standard Deviation SignificanceSSWD Gap (GLM-NHS) -0.008 0.003 0.018Foundation Trust -0.449 0.168 0.008Private earnings -0.005 0.014 0.722Hospital type1Acute 0.781 0.295 0.008Teaching 0.139 0.324 0.667Community 0.803 0.324 0.013Multi-services 1.473 0.304 0.000Other 1.167 0.333 0.000Constant -3.809 0.386 0.000Log-likelihood -881.625Cragg & Uhler’s R2 0.396Vuong test (ZINB vs NB) 1.89 (sig 0.0291)Dispersion = Nr of established posts, Nr of obs = 245 hospital trusts. Standard errors are clustered by LAD geography and are robust. All estimations are inflated bythe gap SSWD, other variables are excluded from the inflation process due to multicollinearity.

Distributing public funding to the NHS in England : taking account of differences in local labour market conditions on NHS recruitment and retention

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Distributing public funding to the NHS in England : taking account of differences in local labour market conditions on NHS recruitment and retention

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