giving greater financial independence to hospitals—does it make a difference? the case of english...

HEALTH ECONOMICSHealth Econ. 17: 751–775 (2008)Published online 2 October 2007 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/hec.1292

GIVING GREATER FINANCIAL INDEPENDENCETO HOSPITALS}DOES IT MAKE A DIFFERENCE?

THE CASE OF ENGLISH NHS TRUSTSy

GIORGIA MARINI*, MARISA MIRALDO, ROWENA JACOBS and MARIA GODDARD

Centre for Health Economics, University of York, York, UK

SUMMARY

In 2003 a new type of provider organisation, the Foundation Trust (FT), was introduced in England, and the bestperforming NHS hospitals were able to apply for ‘Foundation status’. FTs enjoy greater financial flexibility and aresubject to less central monitoring and control. The phased introduction of FTs represents an opportunity toexamine whether the new financial structures facing FTs have produced any differences in financial performancecompared with non-FTs. We use difference in difference methods to examine whether Foundation status had asignificant effect on financial management. We find that Foundation status has had a limited impact in terms ofacting as an instrument to signal strong financial management of FTs. This result may reflect the relatively earlystage of the FT process or may be due to the fact that all types of Trusts are experiencing a challenging financialenvironment, including the introduction of a prospective payment system. However, we explore the nature of thetrends emerging over time and discuss the implications of our findings for policy. Copyright # 2007 John Wiley &Sons, Ltd.

Received 7 March 2007; Revised 22 June 2007; Accepted 27 July 2007

JEL classification: I18; I11

KEY WORDS: financial management; difference in difference models; Foundation Trusts

INTRODUCTION

The English National Health Service (NHS) is following the experience of New Zealand (Howell, 2004)and many European countries (Maarse et al., 2005) in giving hospitals more freedom to decide how theyachieve national targets. New Zealand gave more autonomy to hospitals in 1993 (although subsequentlyin 2001 they were re-integrated into health boards), Belgium in 1995, Austria in 1998, Denmark andFinland in the late 1990s, the Netherlands in 2000, Italy in 2000 and Germany in 2001. In 2003 the UKParliament passed the Health and Social Care Act to create a new type of organisation transformingEnglish NHS Trusts into Foundation Trusts (FTs) (Health and Social Care Act, 2003). The bill waspart of a major programme of reforms started in 2000 with the NHS Plan and implemented over a10-year timetable. The package of reforms included the introduction of a prospective payment system(termed payment by results, PbR), competition from new independent (private) treatment centres,

*Correspondence to: Centre for Health Economics, Alcuin A Block, University of York, Heslington, York YO10 5DD, UK.E-mail: [email protected]

yThis paper was presented at the Health Econometrics Data Group (HEDG), University of York, May 2006, at the 6th EuropeanConference on Health Economics (ECHE), Budapest, July 2006, at the 69th Health Economists’ Study Group (HESG) Meeting,Centre for Health Economics, University of York, July 2006, at the 1st Workshop Associacao Portuguesa de Economia daSaude, Caramulo, September 2006 and at the Health Economics Research Unit (HERU) Seminars, HERU, University ofAberdeen, April 2007.

Copyright # 2007 John Wiley & Sons, Ltd.

practice-based commissioning and the extension of Patient Choice (Department of Health, 2002a, c,2004). The introduction of FTs was therefore just one strand within a complex set of reforms across thewhole health care system.

FTs are independent not-for-profit public benefit corporations, modelled on cooperative societies andmutual organisations (Maltby, 2002). They are still required to meet national targets, like other Trusts,but have more freedom to decide how they achieve these standards (Department of Health, 2002b).Foundation status brings FTs a range of additional freedoms from central controls, and in particularthey have greater financial flexibility: they do not have to break-even every financial year, are allowed toretain financial surpluses, can invest in buildings and new services, manage their own assets, borrowboth from the public and the private sector, recruit and reward staff with more competitive salaries andare subject generally to less central monitoring and control (Eaton, 2005; Healthcare Commission,2005). They are also subject to a new regulatory body, called Monitor.

Greater autonomy is expected to bring benefits for local communities: FTs should face incentives todevelop their business and adapt their financial and operating structure to local needs, while improvingtheir overall performance and meeting national targets. In particular, FTs should face a different set ofconstraints (limited borrowing from private sector under the Prudential Borrowing Limit set byMonitor, more control over appointing directors, binding contracts with the organisationscommissioning services from the Trust and use of national tariffs to price their activities) andincentives (more control over their own future activity, more and quicker access to capital investment,more local control over setting priorities, more potential competition among Trusts and more freedomin employment of new staff) that may encourage them to change their behaviour (HealthcareCommission, 2005).

FTs are being introduced in a phased manner (Department of Health, 2002b) and in the financial year2005/06 (1st April–31st March) were responsible for around 30% of acute Trust activity (Monitor,2006). The first phase of implementation occurred in 2004/05. In October 2002 the best performingTrusts (assessed in terms of having achieved the highest rating for national performance}‘three stars’)were allowed to apply for Foundation status. The star rating is a composite performance rating thatplaces Trusts into one of four categories of performance: from highest (awarded three stars) to poorest(awarded zero stars). Performance ratings are defined over key government targets such as waiting timesand financial management. In December 2002 12 Trusts were short-listed according to their star ratingperformance. Successful applicants, of which there were 10, were allowed to operate as ‘shadow’ FTsfrom July 2003 and to become fully operational as NHS FTs in April 2004 (we refer to FTs becomingfully operational in April 2004 as ‘wave 1’). In July 2004, out of 20 short-listed, another 10 Trustsbecame fully operative as NHS FTs (‘wave 2’) and, finally, in January 2005, all five short-listedapplicants became fully operational as NHS FTs, meeting the requirements of the application process(‘wave 3’). From 37 short-listed applicants, 25 were fully operative as FTs in 2004/05 (out of 173 acuteTrusts, around 15%). Further phases of implementation took place for 2005/06 and the following years.Although eligibility is set at the highest performance rating, not all eligible Trusts have chosen to applyfor Foundation status. Equally, during the protracted application phase, some FTs have lost stars fromthe time they applied for Foundation status in 2002/03 to the time of implementation in 2004/05.Nonetheless, they have not seen their FT status withdrawn. Table I shows the number and percentage ofTrusts receiving each star rating, for the five years since the Performance rating system has been inplace, including the number and percentage of FTs in each of the waves 1, 2 and 3.

FINANCIAL MANAGEMENT OF FTS

In this paper we focus on examining the new financial freedoms granted to FTs. The phasedintroduction of FTs represents an opportunity to compare the characteristics of the ‘early adopters’

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Copyright # 2007 John Wiley & Sons, Ltd. Health Econ. 17: 751–775 (2008)

DOI: 10.1002/hec

with those of other Trusts and to explore whether the new financial freedoms enjoyed by FTs haveproduced any differences in financial management compared with non-FTs. This analysis formsthe baseline for future work where we will be able to investigate the addition of later wave FTs andwhether changes in financial management of FTs endure once they learn to exploit their new regulatoryregime.

In order to explore the financial management of FTs we focus on two measures: retained surplus(measured as a proportion of total expenditure) and the Reference Cost Index (RCI). The choice ofretained surplus as a financial management measure is driven by the fact that break-even was one of thekey measures in the star rating performance regime, which determined the ability to apply for FT status.However, under FT status, the increased freedoms allow FTs to retain financial surpluses and equally tochoose not to run a financial surplus every year (Department of Health, 2002b). These freedoms shouldenable them to take a longer planning horizon when it comes to borrowing and investment decisionsand may give them long run financial advantages. However, these financial freedoms can also workagainst them. While they may have greater freedoms to borrow and invest, capital investment under thePbR regime may make it more likely that they can incur deficits.

This is because the prospective payment system (PbR tariffs) is based on the average of both recurrentand capital costs, which tends to systematically under-fund new capital stock (Palmer, 2006). Newhospitals are likely to have higher-than-average capital costs compared with older hospitals whosehistoric capital costs may be largely written down. This means that FTs with new hospitals will tend tobe under-funded and more likely to incur deficits in future. Indeed there are several examples of FTsthat have invested to expand their capacity and develop new services. For example, Moorfields EyeHospital has raised capital funds in order to anticipate the opening of an international children’s eyehospital (Robinson, 2005). Cambridge University Hospital has invested in refurbishment and purchasedadditional IT equipment; Guy’s and St Thomas’ Hospital has invested in major refurbishment(Healthcare Commission, 2005; Robinson, 2005). The FT regulator Monitor has already warned thatFTs need to make bigger surpluses if they wish to invest in new services and renew their assets (Monitor,2006). Financial prudence, in the form of break-even, has always been a hallmark of NHS Trusts,indeed break-even has always been one of the key targets for all Trusts in the star ratings system (Jacobset al., 2006) and like all NHS Trusts, the financial balance of FTs is regularly monitored by theirregulator. Monitor seems keen to show positive financial balances for FTs, despite the fact that they areentitled to run a deficit. Indeed, the latest report by Monitor on the financial position of FTs in March2007 has a headline statement on the favourable surplus position of FTs, commenting that this reflects

Table I. Number (and percentage) of acute and specialist Trusts receiving each star rating

0 stars 1 star 2 stars 3 starsNumber of

Trusts

2000/01 12 (7%) 23 (13%) 103 (60%) 35 (20%) 207 (of which 34were not assigned

stars)2001/02 10 (5%) 36 (19%) 92 (48%) 53 (28%) 200 (of which 9

were not assignedstars)

2002/03 14 (8%) 31 (18%) 68 (38%) 63 (36%) 1762003/04 10 (6%) 29 (17%) 60 (34%) 74 (43%) 173

Non-FTs FTs Non-FTs FTs

Wave1

Wave2

Wave3

Wave1

Wave2

Wave3

2004/05 9 (5%) 38 (22%) 48 (28%) 2 (1%) 3 (2%) 0 53 (31%) 8 (4%) 7 (4%) 5 (3%) 173

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‘continuing financial strength’ (Monitor, 2007c). So while FTs have more financial autonomy, the NHSethos of not running debts on public money still seems to hold.

While FTs may not yet have fully exploited their new financial opportunities, we wish to test whether,in the first year of the FT regime, retained financial surplus is systematically different for FTs relative tonon-FTs.

Our second choice of financial management measure, the RCI, is an activity-weighted average of aTrust’s Healthcare Resource Group (HRG) unit costs relative to the national average. With theintroduction of HRG casemix funding under PbR, reference costs are used to set the national tariff onwhich Trusts are reimbursed (Audit Commission, 2004). The RCI can be used to determine a Trust’srelative efficiency}low-cost Trusts will have an RCI below 100, representing higher efficiency, while ahigh-cost Trust will have an RCI above 100 (Department of Health, 2006). FTs with lower casemixcosts may be able to exploit benefits from economies of scope (focusing on the mix of services with costsbelow national tariff) and economies of scale (expanding the volume of activity for services with costsbelow national tariff). Trusts have strong incentives to increase activity since this will increase incomeby much more than it increases costs in the short term and so can help to achieve annual financialbalance (Palmer, 2006). These incentives are even stronger in HRGs where marginal cost is below tariff.We therefore test whether HRG-weighted unit costs are different for FTs relative to non-FTs.

METHODOLOGY

We use a difference in difference (DID) methodology to test whether there are any differences in thefinancial management between FTs and non-FTs following the introduction of Foundation status,whether the policy has made any difference at all or whether indeed there are long-standing differencesin performance between these different types of organisations, which have made some of them morelikely to adopt this policy than others. In effect, we therefore track the two financial measures over timefor FTs before they actually became FTs and investigate how they compare with non-FTs over time. Weexplore the robustness of our results using the two dependent variables, three different control groupsand three estimation methods.

One of the main challenges in evaluating this policy reform is the ability to draw firm conclusionsbased on comparison between FTs and non-FTs, when the decision to become an FT is voluntary(subject to meeting a minimum standard). Allowing for this potential selection bias is therefore a keycomponent of our research and we describe below our approach to this.

We use two methods that have been proposed for dealing with selection bias (or self-selection) intreatment effect models: the DID method and the difference in difference matching method (Blundelland Costa Dias, 2002; Wooldridge, 2002).

The DID methodology considers the introduction of FTs as an experiment and it looks for anaturally occurring comparison group that can mimic the properties of the treatment group in theproperly designed experimental context. This method is termed DID since it is usually implemented bycomparing the difference in average performance before and after the reform for the eligible group(treatment group) with the before and after contrast for the comparison group (Blundell and CostaDias, 2002). This approach can be used to isolate the average effect of the implementation of the reform(treatment) on those Trusts that become Foundations (‘treated’ by the reform). This approach measuresthe average effect of the treatment on the treated (ATT) by removing unobservable Trust effects andcommon macro effects. However, it relies on two critically important assumptions of (i) common timeeffects across groups and (ii) no systematic composition changes within each group (Blundell and CostaDias, 2002). The drawback of the DID approach is that it assumes random assignment to the treatmentgroup, whereas in the context of this reform, assignment of Trusts to the treatment and control groups

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is not random. As a consequence, the estimation of the effect of the policy intervention may be biased bythe existence of confounding factors (Becker and Ichino, 2002).

We therefore use a matching method as a way to correct the estimation of treatment effectscontrolling for the existence of confounding factors based on the idea that the bias is reduced when thecomparison of outcomes is performed using treated and control Trusts that are as similar as possible(Becker and Ichino, 2002). Under the matching assumption, the only remaining difference betweentreated and non-treated groups is the treatment. Consequently, if each Trust undergoing the reform canbe matched with a Trust with the same matching variables that has not undergone the reform, then theimpact of the reform can be isolated (Blundell and Costa Dias, 2002).

We apply a matching based on a function of pre-treatment characteristics. Usually, this is carried outon the conditional probability of receiving a treatment given pre-treatment characteristics, i.e. on thepropensity score:

pðXÞ ¼ PrðF ¼ 1 j XÞ ¼ EðF j XÞ ð1Þ

in which F ¼ f0; 1g is the indicator of exposure to treatment (Foundation status) and X is themultidimensional vector of pre-treatment characteristics. The propensity score is therefore a single-index variable summarising pre-treatment characteristics of each Trust in order to make the matchingfeasible. As a result, if the propensity score pðXÞ is known, the ATT can be estimated as follows:

ATT ¼EðY1 � Y0 j F ¼ 1Þ ¼ EfEðY1 � Y0 j F ¼ 1; pðXÞÞg

¼Ef½EðY1 j F ¼ 1; pðXÞÞ � EðY0 j F ¼ 0; pðXÞÞ� � ½EðY0 j F ¼ 1; pðXÞÞ � EðY0 j F ¼ 0; pðXÞÞ�g

¼EfEðY1 j F ¼ 1; pðXÞÞ � EðY0 j F ¼ 0; pðXÞÞg ð2Þ

in which Y1 and Y0 are the potential outcomes in the two counterfactual situations of treatment and notreatment and the expression Ef*g is computed over the distribution of pre-treatment variables X in thetreated population, pðXÞ j F ¼ 1:Note that the move to the second line of Equation (2) is possible underthe condition that only one of Y1 or Y0 can be observed for any Trust: it is not possible to observe thesame Trust under both treatment and control. By adding and subtracting EðY0 j F ¼ 0; pðXÞÞ in thesecond line of Equation (2), we can decompose the ATT into the average treatment effect (ATE)EðY1 j F ¼ 1; pðXÞÞ � EðY0 j F ¼ 0; pðXÞÞ and the selection bias ½EðY0 j F ¼ 1; pðXÞÞ � EðY0 j F ¼ 0;pðXÞÞ�: Under the assumption of conditional mean independence, EðY0 j F ¼ 1; pðXÞÞ ¼ EðY0 jF ¼ 0; pðXÞÞ and the selection bias reduces to zero.

Using the propensity score matching model, we match FTs with non-FTs in the pre-treatment year onthe basis of observable characteristics. We employ a logit model which models the conditionalprobability of becoming an FT given the pre-intervention characteristics. The model splits the sampleinto approximately equally spaced intervals of the propensity score and tests within each interval thatthe average propensity score of treated and control units does not differ. Within each of these intervalsthe model also tests that the means of each observable characteristic do not differ between treated andcontrol units. This is a necessary condition for the balancing property. The balancing property of thepre-treatment variables is tested to ensure that observations with the same propensity score have thesame distribution of observable (and unobservable) characteristics independent of Foundation status.Thus, within each block the propensity score and the characteristics of Trusts do not differ for treatedand control units (Becker and Ichino, 2002). We match FTs with non-FTs in year 4 (pre-treatment year)on the basis of observable characteristics, other than their financial management measures. We employa logit model for the propensity to be an FT, invoking the common support modelling option (Dehejiaand Wahba 1999, 2002; Smith and Todd, 2005) which restricts the set of data points over which the testof the balancing property is sought to those belonging to the intersection of the supports of thepropensity score of treated and controls. Imposing the common support condition in the estimation ofthe propensity score may improve the quality of the matching process. Thus, for a given propensity



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score, becoming an FT is random and FTs and matched non-FTs should on average be observationallyidentical.

Once outcomes for FTs and non-FTs have been matched, we apply a DID matching model(Heckman et al., 1997, 1998) to eliminate any systematic differences after conditioning on observables.Such differences may arise, for example, because of selection based on unobservable characteristics, orbecause performance outcomes for FTs and non-FTs may be measured in different ways. For example,when data are extracted from different sources as in the case of retained surplus (further details on thisissue are provided in the fourth section), the identification conditions required for matching may beviolated. The DID strategy overcomes this problem by allowing for temporally invariant differences inoutcomes between FTs and non-FTs (Smith and Todd, 2005).

We therefore compare the change in financial management for FTs before and after the Foundationstatus introduction in 2004/05 (year of the policy intervention) with the change in financial managementfor Trusts in a comparator group that is not undergoing the intervention, over the same period. TheDID method enables us to estimate the average effect of Foundation status on the financialmanagement of FTs. To identify the average effect of the policy intervention, we estimate the followingmodel:

yit ¼ b0 þ b1Fi þX4

t¼1

b2tDt þ b3Xit þX4

t¼1

dtFiDt þ Eit ð3Þ

where yit is the financial management measure (either retained surplus or RCI) for Trust i in year twhere t covers 5 years from 2000/01 to 2004/05, Fi is a dummy variable for Foundation status whereFi ¼ 1 if a Trust is an FT and 0 otherwise, Dt is a year dummy for t ¼ 1 (2000/01), 2 (2001/02), and soon. The baseline year is 2004/05. Xit is a vector of observable factors affecting the dependent variable forTrust i in year t:

The FT main effect Fi controls for all time invariant differences between FTs and the control group.The year dummy controls for all other unobserved temporal factors affecting either the surplus or theRCI. The interaction of the year and FT dummies identifies the change in financial performance fromthe base year for FTs relative to control Trusts. The DID methodology assumes that all other temporalfactors affecting surplus or the RCI have the same effects for FTs and the control group.

The effect of Foundation status on FTs (the ATT) is the DID for year 5 (the year when policyintervention was in effect) against a previous year. Since year 5 (2004/05) is the baseline year, the DIDmeasure of year 5 against year 4 is

½Eðyit j Xit;Fi ¼ 1Þ � Eðyit j Xit;Fi ¼ 1;D4 ¼ 1Þ� � ½Eðyit j Xit;Fi ¼ 0Þ � Eðyit j Xit;Fi ¼ 0;D4 ¼ 1Þ�

¼ ½ðb0 þ b1Þ � ðb0 þ b1 þ b24 þ d4Þ� � ½ðb0Þ � ðb0 þ b24Þ� ¼ �d4 ð4Þ

This is the effect of becoming an FT relative to year 4 (2003/04) but we also measure it as the effectrelative to year 3 ð�d3Þ; year 2 ð�d2Þ and year 1 ð�d1Þ: Therefore, we test for the effect of the policyintervention on FTs by checking whether the DID coefficient ð�d4Þ is significantly different from zero.

FTs gained FT status in three ‘waves’ within the first year of the FT regime (in April 2004, July 2004and January 2005). We can test whether there was strategic behaviour in the adoption of FT status byinterpreting the first wave of FTs as the preparatory period for the second wave FTs, and so on(Croxson et al., 2001). To identify the average effect of gaining FT status in April 2004, July 2004 andJanuary 2005, we estimate a modified version of Equation (3):

yit ¼ b0 þX3

j¼1

b1jFij þX4

t¼1

b2tDt þ b3Xit þX4

t¼1

X3

j¼1

dtjFijDt þ Eit ð5Þ

where Fi1; Fi2 and Fi3 are three dummy variables associated with the three waves. Fi1 takes value 1 if anFT is fully operative in April 2004, Fi2 takes value 1 if an FT is fully operative in July 2004 and Fi3 takes

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value 1 if an FT is fully operative in January 2005. Therefore, we can test for the effect of the policyintervention on FTs by checking whether the DID coefficient ð�d4jÞ is significantly different from zerofor FTs gaining FT status in April 2004, July 2004 and January 2005.

We use three types of comparators to estimate the effects of becoming an FT: all non-FTs in the restof England, non-FTs with 3-star status, which would have been eligible for FT status but did not apply,and a matched control group of non-FTs using propensity score matching.

The first control group, all non-FTs, is intuitively plausible, since we wish to test whether changesin the dependent variable are the result of a specific reform and how this impacts on all Trusts inEngland. The advantage of this control group is that coefficient estimates in the regressions may bemore robust since we have a large control group (148 Trusts). The disadvantage of this control group isthat it may be very heterogeneous, including also Trusts that may have unobserved differences inperformance time trends, thereby violating the identifying assumption of the DID method (Dawsonet al., 2007).

The group of non-FTs with 3-star status, which would have been eligible for FT status, consists ofthose Trusts that have chosen not to be exposed to the treatment option (not to opt for Foundationstatus). This control group may be a better comparator than the first, since 3-star non-FTs are morelikely to be similar to FTs in terms of performance characteristics. However, the sample size isconsiderably smaller (53 Trusts) and coefficient estimates may therefore be less well defined (Dawsonet al., 2007).

As a final comparator group, we also use propensity score matching to match FTs with non-FTs onthe basis of observable characteristics, other than their financial performance, in the year prior to theintroduction of FT status (2003/04). The advantage of this method is that statistically there is a strongmatch between FTs and non-FTs on their observable pre-treatment characteristics, although the controlgroup is still quite small (70 Trusts).

Using the DID methodology, for each of the above control groups we ran three types of estimationtechniques: ordinary least squares (OLS), fixed effects and generalised estimating equation (GEE)models.

We ran OLS clustering on Trusts. We also specified the Huber/White sandwich estimator of varianceto calculate robust standard errors. We ran a fixed effects model using the xtreg, fe estimator in Stata 9(Stata, 2005). The model allows the option of clustering on Trusts and the calculation of robuststandard errors. The third estimation method is a population-averaged panel-data model which isequivalent to a random effects model. Using the xtgee estimator in Stata 9 (Stata, 2005), this methodestimates a general linear model and allows one to specify the within-group correlation structure for thepanels. We assume that the within-group correlation structure is the equal-correlation model. Thecorrelation matrix for the equal-correlation model is a square matrix in which each element on thediagonal is equal to 1 and each off-diagonal element is equal to a constant correlation r: We includedthe mean of the time-varying variables as well as the deviation from the mean for these variables, theMundlak adjustment (Mundlak, 1978). The advantage of the Mundlak adjustment is that it filters outpotential correlation of unobserved heterogeneity and regressors. The model again allows the option ofcalculating robust standard errors.

We tested for multicollinearity using the variance inflation factors (VIFs) for the independentvariables specified in the fitted model and dropped variables if there was evidence of excessivecollinearity. In all three estimation methods a regression specification error test (RESET) wasperformed (Ramsey, 1969), and results are shown in each of the regression tables.

The three estimation techniques provide a useful comparison with one another regarding the stabilityof coefficient estimates. However, the fixed effects model may pick up much of the unobservedheterogeneity in the Trust-specific effect. Hence, when comparing the DID results under differentestimation techniques, the fixed effects are the preferred results. The fixed effects model does not,however, provide an estimate of the b1 coefficients, which both the OLS and GEE models do provide.



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For the graphical representations of the DID models, the GEE results have been used. For these reasonswe report only the fixed effects and GEE results.

We ran all models with and without Strategic Health Authority (SHA) effects, using dummy variablesfor the 28 SHAs as fixed for the whole period, even though they only came into existence in 2002. Itcould be argued that SHA effects may wash out any fixed effects between SHAs such as differences indata quality. However, these may also potentially wash out some differences between Trusts, which wedo wish to pick up. When we have small control groups, we also lose additional degrees of freedom byincluding these SHA effects. We therefore chose to report results without SHA effects. However, on thewhole, results were qualitatively similar with and without SHA effects.

DATA

Our data are annual and cover all acute and specialist Trusts in England for a period of 5 years startingin 2000/01. While we have data on both dependent variables since 1997/98, we only run regressionmodels from 2000/01 onwards as the star rating system was not in place before 2000. The databaseincludes 4 years of data prior to the introduction of FTs and 1 year of data post FTs (2004/05), althoughonly wave 1 FTs had Foundation status for the whole year.

The financial management measure of surplus is derived from annual data on retained surplus (deficit)measured as a proportion of total expenditure for each Trust. Both retained surplus (deficit) and totalexpenditure are collected from the Chartered Institute of Public Finance and Accountancy (CIPFA) fornon-FTs, following submission of their Annual Trust Financial Returns (TFRs) to the Department ofHealth. With the reduced central control that FTs enjoy, they no longer have any obligation to presenttheir TFRs to the Department of Health (and hence CIPFA). We therefore had to collect data on Truststhat became FTs in April 2004 (wave 1) directly from their published Income and Expenditure Accounts.Data for wave 2 and 3 FTs were provided by CIPFA for the fraction of the financial year in which theseTrusts were non-FTs, while data concerning the remaining months of the financial year were extractedfrom Income and Expenditure Accounts provided by the Finance Department of each FT.

FTs are subject to different accounting treatments. In particular, these differences pertain to twoaccounting practices: the FRS11 (accounting treatment for impairments) and FRS15 (accountingtreatment for valuation and indexation of fixed assets). While for FTs impairments, indexation andvaluation/revaluation enter the income/expenditure accounts, for non-FTs these just enter under thebalance sheet, thus not affecting retained surplus/deficit directly (Monitor, 2006). While non-FTs musteffectuate these practices every 5 years, FTs can choose the timing as well as frequency with which theywish to carry out these practices. Therefore, the retained surplus used for FTs and non-FTs is built in aslightly different way which may bias our results. The direction of the bias depends on the effect of theimpairments, indexation and valuation/revaluation. Since these are normally negative it means that theretained surplus for FTs might be underestimated. This means that we may be unable to detect asignificant financial management effect, when in fact there is one. The correction of these differencesturned out to be infeasible since it was impossible to disentangle the amount of impairments, indexationand valuation/revaluation in operating expenditures, with each FT being a unique case.

However, given the short period in which the policy has been in place, many FTs said they are stillfollowing the same rules they have always followed in the past. Furthermore, some FTs explained thatthey would have had an incentive to show sound financial balance in the first year, making it less likelyto effectuate in the first year. It is therefore likely that if there were any bias, it would be very small, and,if anything, underestimate our results on the surplus variable.

Our second dependent variable, the RCI, originates from the Department of Health and covers allTrusts within England. All inpatient elective and non-elective schedules within the Reference Costdatabase are based on data truncation, excluding bed days that fall outside of nationally set lengths ofstay (trimpoints). The costs of any days beyond these trimpoints are excluded. This assists in giving a

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like-for-like comparison of activity and costs. The RCI is also adjusted by the market forces factor(MFF) to take account of some areas of the country with higher costs for staff, land or buildings.

All other explanatory Trust variables ðXitÞ that are included in the regression models are for all Trustswithin England. Our data set covers a large number of variables on activity, expenditure, resource use,performance and staffing from numerous sources, including the Department of Health, the HealthcareCommission, Hospital Episodes Statistics (HES), Hospital Activity Statistics (HAS), the CIPFA andDr Foster. The main data sources are listed in Appendix. Some of these variables present missingobservations due to non-available information at the Trust level. Some of these missing observationshave been replaced with previous year values, with the exception of financial year 2004/05 to avoidcontamination of the policy effect.

RESULTS

Table II shows the descriptive statistics for the dependent variables and the explanatory variablesincluded in the models.

Figures 1(a) and 2(a) plot the two dependent variables from 1997/98 onwards, for FTs and the threenon-FT control groups, while Figures 1(b) and 2(b) plot the two dependent variables from 1997/98onwards for the three waves of FTs against all non-FTs.

It is clear that FTs have the highest retained surplus over almost the entire period (Figure 1(a)).However, like most other Trust groups, except 3-star non-FTs, they experience a large decline infinancial balance with the implementation of the reform in 2004/05. All non-FTs have the highestretained deficit over the period. Wave 1 FTs seem to perform well over the entire period, but then see amassive decline in their surplus in the intervention year (to a worse position than all non-FTs) (Figure1(b)). Wave 2 and wave 3 FTs perform reasonably consistently over the period, but while wave 3 FTsmaintain financial balance in 2004/05, wave 2 FTs see a decline in surplus. This result may of course bedue to the fact that wave 3 FTs have operated under FT status for a shorter period than other FTs.

FTs have the lowest RCI across all periods and maintain it in 2004/05 after the policy implementation(Figure 2(a)). All non-FTs have the highest RCI until 2000/01, whereupon 3-star non-FTs have thehighest RCI. Wave 1 FTs seem to have a lower RCI over the whole period compared with the otherwaves of FTs. Even though wave 1 FTs faced a decline in their RCI in the pre-intervention year, afterpolicy implementation their RCI increased (Figure 2(b)). Wave 3 FTs have a lower RCI than all non-FTs with the difference having increased in the last two years. Finally, wave 2 FTs have the highest RCIover most of the period compared with wave 1, wave 3 and non-FTs. Together with all non-FTs, wave 2FTs saw a slight improvement in their RCI after policy implementation.

The matched control group

The results for the logit model are shown in Table III.The sample consisted of 173 Trusts and the model produced a Pseudo R-squared of 0.25. There were

five blocks of Trusts in the final propensity score model, although these were pooled together to producea control group of 70 Trusts under common support, compared with 25 in the treatment group with thebalancing property satisfied. Once FTs and non-FTs are matched, the unmatched comparison units outof the common support are discarded and are not directly used in estimating the treatment impact(Dehejia and Wahba, 2002).

Significant matching variables were sought over the database of Trust variables. These include dataon performance measures, key targets, staffing and vacancy rates, activity, capacity, expenditure,salaries and income. We applied a variety of search techniques within the database to narrow the searchfor significant variables from about 100 key variables. The seven selected variables produced the highestnumber of Trusts in the control group under the common support assumption.



DOI: 10.1002/hec

TableII.Descriptivestatisticsandvariable

definitions,pooleddata

2000/01–2004/05

Variable

Definition

Source

nMean

Std

dev.

Min

Max

surplus

Retained

surplus(deficit)

forthefinancialyearas

proportionoftotalexpenditure

CIPFA

925

�0.039

0.200

�1.769

0.817

rci

Reference

Cost

Index

excluding

excess

bed

days

adjusted

bytheMarket

Forces

Factor(M

FF)

DoH

916

99.612

11.637

61.173

173.084

ftDummyvariable¼

1iftheTrust

isanFT

or¼

0if

theTrust

isanon-FT

Derived}timeinvariant

929

0.130

0.337

01

ftmatch

Dummyvariable¼

1iftheTrust

isanFT

or¼

0if

theTrust

isanon-FTin

thematched

controlgroup


471

0.257

0.437

01

ft3stars

Dummyvariable¼

1iftheTrust

isanFT

or¼

0if

theTrust

isa3-starnon-FT


389

0.311

0.464

01

wave1

Dummyvariable¼

1iftheTrust

becomes

anFTin

April2004or¼

0ifitisanon-FT


929

0.053

0.224

01

wave2

Dummyvariable¼

1iftheTrust

becomes

anFTin

July

2004or¼

0ifitisanon-FT


929

0.051

0.219

01

wave3

Dummyvariable¼

1iftheTrust

becomes

anFTin

January

2005or¼

0ifitisanon-FT


929

0.027

0.162

01

match

wave1

Dummyvariable¼

1iftheTrust

becomes

anFTin

April2004or¼

0if

itis

anon-FT

inthematched

controlgroup


471

0.104

0.306

01

match

wave2

Dummyvariable¼

1iftheTrust

becomes

anFTin

July

2004or¼

0if

itis

anon-FT

inthematched

controlgroup


471

0.100

0.300

01

match

wave3

Dummyvariable¼

1iftheTrust

becomes

anFTin

January

2005or¼

0ifitisanon-FTin

thematched

controlgroup


471

0.053

0.224

01

3stars

wave1

Dummyvariable¼

1iftheTrust

becomes

anFTin

April2004or¼

0ifitisa3-starnon-FT


389

0.126

0.332

01

3stars

wave2

Dummyvariable¼

1iftheTrust

becomes

anFTin

July

2004or¼



389

0.121

0.326

01

3stars

wave3

Dummyvariable¼

1iftheTrust

becomes

anFTin

January

2005or¼



389

0.064

0.246

01

year1

Dummy

variable¼

1if

year¼

2000=01

or¼

0otherwise


929

0.223

0.416

01

G. MARINI ET AL.760


DOI: 10.1002/hec

year2

Dummy

variable¼

1if

year¼

2001=02

or¼

0otherwise


929

0.215

0.411

01

year3

Dummy

variable¼

1if

year¼

2002=03

or¼

0otherwise


929

0.189

0.392

01

year4

Dummy

variable¼

1if

year¼

2003=04

or¼

0otherwise


929

0.186

0.389

01

occupanc

Percentoccupiedbeds

HAS

924

84.007

6.188

57.321

97.201

testsavbeds

Totalim

agingandradiodiagnostictestsper

available

bed

Derived

from

HAS

913

221.369

57.980

0468.673

testsoccuppc

Totalim

agingandradiodiagnostictestsper

occupied

bed

Derived

from

HAS

913

264.102

69.292

0557.491

alos

Averagelength

ofstay

HES

921

6.027

4.362

0.615

53.400

daycase

spell

Number

ofdaycasesper

totalinpatientspells

Derived

from

HES

921

0.426

0.136

00.955

emerg

spell

Number

ofem

ergency

admissionsper

totalinpatient

spells

Derived

from

HES

921

0.344

0.102

0.002

0.959

epspell

Totalinpatientepisodes

per


Derived

from

HES

921

1.112

0.078

12.083

meanwait

Meanwaitingtimeexpressed

innumber

ofdays

HES

918

93.630

31.854

0253

comp

Competitionmeasure

ofnumber

ofTrustswithin

a20km

rangeofeach

Trust


909

9.472

12.655

049

dtc

Dummyvariable¼

1iftheTrusthasanindependent

sectortreatm

entcentreor¼

0otherwise

DoH

andHouse

ofCommons

Health

Committee-

time

in-

variant

929

0.067

0.250

01

testsz

Totalim

agingandradiodiagnostic

tests

HAS

173

174712

91707

7512

574639

day-

case

spellz

Number

ofdaycasesper


Derived

from

HES

173

0.326

0.086

0.003

0.704

ipd

spellz

Inpatientdaysper

totalinpatientspells(a

proxyfor

length

ofstay)

Derived

from

HES

173

3.857

0.959

0.505

8.313

inwtgtstpcz

Percentageofinpatients

waitinglonger

than

stan-

dard

forelectiveadmission

DoH

173

0.062

0.402

04.988

outw

t13wkz

Totalnumber

ofpatients

waitingover

13weeksfor

outpatientappointm

ent

DoH

173

0.928

0.469

0.002

2.406

info

govz

Inform

ationgovernance

anddata

confidentiality

DoH

173

1.578

0.121

1.074

1.822

mansalz

Totalseniormanagersand

managerssalaries

and

wages

(£’000s)

DoH

173

4676

3236

018500

z Usedin

logitmodel,2003/04data

only.



DOI: 10.1002/hec

The probability of becoming an FT in the pre-treatment year is associated with a lower number ofimaging and radio-diagnostic tests, higher salaries for managers, shorter lengths of stay and a higherdaycase rate. They also do better on both measures of waiting times criteria and on compliance withtargets on information governance and data confidentiality.

Financial surplus/deficit

Table IV shows the regression results for the DID models in which we test whether the treatment group(FTs) relative to the three control groups (all non-FTs, matched non-FTs and 3-star non-FTs) were anydifferent in terms of financial surplus between the base year 2004/05 and previous years.

The b1 coefficients indicate the overall difference in performance between FTs and the control groups in2004/05, while the b2 coefficients give the change in the dependent variable for all types of Trusts betweenyear t and the baseline year (2004/05). The b1 coefficients are always insignificant while the b2 coefficientsare positive, mostly highly significant and monotonic. The DID estimates for the change between 2003/04and 2004/05 are all negative, but significant only for the 3-star non-FTs comparator group with �d4 ¼�0:103 for both the fixed effects and the GEE models. While the significance is weak, this suggests that onaverage the policy intervention decreased the FT surplus relative to 3-star non-FTs by around 10percentage points between years 4 and 5. This corresponds with the raw plot in Figure 1(a).

-0.200

-0.175

-0.150

-0.125

-0.100

-0.075

-0.050

-0.025

0.000

0.025

0.050

1997/98 1998/99 1999/00 2000/01 2001/02 2002/03 2003/04 2004/05

non-FTs (all)FTsnon-FTs (matched group) non-FTs (3 stars only)

-0.200

-0.175

-0.150

-0.125

-0.100

-0.075

-0.050

-0.025

0.000

0.025

0.050

1997/98 1998/99 1999/00 2000/01 2001/02 2002/03 2003/04 2004/05

non-FTs (all) wave 1 FTswave 2 FTs wave 3 FTs

(a)

(b)

Figure 1. Retained surplus (deficit) for the financial year as proportion of total expenditure: (a) all Trusts and(b) FTs by wave and all non-FTs.

G. MARINI ET AL.762


DOI: 10.1002/hec

87

89

91

93

95

97

99

101

103

105

107

1997/98 1998/99 1999/00 2000/01 2001/02 2002/03 2003/04 2004/05

)lla(sTF-nonsTFnon-FTs (matched group) non-FTs (3 stars only)

87

89

91

93

95

97

99

101

103

105

107

1997/98 1998/99 1999/00 2000/01 2001/02 2002/03 2003/04 2004/05

non-FTs (all) wave 1 FTswave 2 FTs wave 3 FTs

(a)

(b)

Figure 2. Reference Cost Index adjusted by theMarket Forces Factor: (a) all Trusts and (b) FTs by wave and all non-FTs.

Table III. Logit model for the selection of the appropriatecomparator group

tests �1:475ð2:10Þnn

mansal 0.249ð2:25Þnn

ipd spell �0:562ð1:77Þn

daycase spell 2.607(0.91)

inwtgtstpc �59:969(1.27)

outwt13wk 1.779ð1:88Þn

info gov 8.935ð3:10Þnnn

constant �15:279ð3:13Þnnn

Observations 173R-squared 0.25

Robust t statistics in parentheses. nSignificant at 10%; nnsignificant at5%; nnnsignificant at 1%.



DOI: 10.1002/hec

The R-squared for the fixed effects models is around 0.29. The RESET test is passed in three of the sixmodels.

Figure 3(a–c) shows the surplus for FTs relative to each of the three comparator groups for each ofthe five years. We use the GEE estimates from the previous results to show this. All figures show a non-significant difference between surplus for FTs and all comparator groups over time, since the 95%confidence intervals overlap zero mostly over the entire range except for the difference between FTs andall non-FTs and the matched control group in the pre-intervention year, suggesting that there mighthave been some strategic behaviour of FTs leading to a significant surplus relative to these non-FTgroups that, nevertheless, dissipated in the intervention year.

Concerning the wave analysis (Table V), the b11; b12 and b13 coefficients indicate the overall differencein performance between wave 1, 2 and 3 FTs and the control groups. These parameters are onlysignificant for wave 3, suggesting a higher surplus for wave 3 FTs of around 15 percentage pointsrelative to the all non-FTs control group and 18 percentage points relative to the matched controlgroup. The b2 are positive and mostly highly significant except for the 3-star control group in the pre-intervention and intervention years. The DID estimates for the effect of the FTs intervention by waveare positive and significant for wave 3 FTs against all non-FTs and the matched control comparatorgroups. This suggests that wave 3 FTs saw a larger difference in the surplus/deficit of around 23percentage points compared with the matched control group in 2000/01 relative to the baseline. This

Table IV. Regression results for difference in difference model for overall effect of FT statuson retained surplus (deficit)

Surplus/Deficit

All non-FTs Matched group 3 stars non-FTs

Fixed effects GEE Fixed effects GEE Fixed effects GEE

FT ðb1Þ 0.047 0.050 �0:084(0.77) (0.79) (1.46)

year1 ðb21Þ 0.212 0.196 0.183 0.145 0.058 0.047ð6:97Þnnn ð7:18Þnnn ð4:61Þnnn ð3:86Þnnn ð1:95Þn ð1:74Þn

year2 ðb22Þ 0.166 0.158 0.136 0.102 0.049 0.039ð5:98Þnnn ð5:60Þnnn ð4:30Þnnn ð3:07Þnnn ð2:46Þnn ð2:03Þnn

year3 ðb23Þ 0.132 0.122 0.153 0.112 0.038 0.030ð3:72Þnnn ð3:77Þnnn ð3:89Þnnn ð3:01Þnnn (1.29) (1.03)


FT � year1 ð�d1Þ 0.080 0.074 0.063 0.085 �0:081 �0:082(1.24) (1.17) (0.89) (1.25) (1.24) (1.33)

FT � year2 ð�d2Þ 0.044 0.040 0.019 0.034 �0:087 �0:089(0.66) (0.61) (0.29) (0.51) (1.43) (1.50)

FT � year3 ð�d3Þ 0.003 �0:003 0.033 0.035 �0:101 �0:101(0.04) (0.04) (0.47) (0.51) (1.59) (1.62)

FT � year4 ð�d4Þ �0:015 �0:015 �0:017 �0:014 �0:103 �0:103(0.23) (0.25) (0.26) (0.22) ð1:66Þn ð1:69Þn

constant ðb0Þ 0.016 �0:132 �0:142 0.280 0.038 �0:025(0.13) ð1:76Þn ð6:25Þnnn ð2:09Þnn (0.83) (1.01)

Observations 908 905 470 469 388 388R-squared 0.31 0.32 0.24RESET 0.16 16:25nnn 0.11 6:69nn 1.93 7:44nn

Robust t statistics in parentheses. nSignificant at 10%; nnsignificant at 5%; nnnsignificant at 1%. FT represents the dummy variableft in case of all non-FTs comparator group, the dummy variable ft match in case of matched comparator group and the dummyvariable ft 3stars in case of 3 stars non-FTs comparator group. The sample sizes between the fixed effects and GEE models varybecause of different availability of data on the covariates. Covariates used in the models: tests occuppc, alos, ep spell, meanwait,comp, dtc, rci.

G. MARINI ET AL.764


DOI: 10.1002/hec

decreased to around 17 percentage points in 2001/02 and 2002/03 and 8 percentage points in 2003/04.The trend of a wave effect is slightly less marked but still evident in all non-FTs control group for wave3 FTs. Wave 2 FTs also had a significantly higher retained surplus relative to all non-FTs in 2000/01relative to the baseline 2004/05, though this difference became insignificant over the later years.

-0.20

-0.16

-0.12

-0.08

-0.04

0.00

0.04

0.08

0.12

0.16

2000/01 2001/02 2002/03 2003/04 2004/05

-0.20

-0.16

-0.12

-0.08

-0.04

0.00

0.04

0.08

0.12

0.16

2000/01 2001/02 2002/03 2003/04 2004/05

-0.20

-0.16

-0.12

-0.08

-0.04

0.00

0.04

0.08

0.12

0.16

2000/01 2001/02 2002/03 2003/04 2004/05

(a)

(b)

(c)

Figure 3. Retained surplus (deficit) as proportion of total expenditure: (a) FTs relative to all non-FTs comparatorgroup; (b) FTs relative to matched comparator group; and (c) FTs relative to 3 star non-FT comparator group.



DOI: 10.1002/hec

The wave analysis seems to suggest that wave 3 FTs were achieving a better financial balance comparedwith all non-FTs and the matched control group. However, this advantage slowly dissipated over time,although wave 3 FTs were still in a better financial position relative to the previous year in the interventionperiod by around 8 percentage points compared with all non-FTs and the matched control group.

Table V. Regression results for difference in difference model for overall effect of FT wave analysison retained surplus (deficit)

Surplus/Deficit



FT wave 1 ðb11Þ �0:061 �0:052 �0:186(0.46) (0.40) (1.46)

FT wave 2 ðb12Þ 0.105 0.086 �0:031ð2:37Þnn ð1:88Þn (0.76)

FT wave 3 ðb13Þ 0.145 0.180 0.019ð5:03Þnnn ð5:09Þnnn (1.41)

year1 ðb21Þ 0.213 0.196 0.146 0.140 0.046 0.046ð6:93Þnnn ð7:18Þnnn ð3:39Þnnn ð3:77Þnnn (1.56) ð1:68Þn

year2 ðb22Þ 0.163 0.157 0.097 0.097 0.036 0.037ð5:89Þnnn ð5:58Þnnn ð2:73Þnnn ð2:92Þnnn ð2:05Þnn ð1:95Þn



FT wave 1 � year1 ð�d11Þ �0:039 �0:042 �0:050 �0:040 �0:186 �0:186(0.29) (0.33) (0.37) (0.31) (1.40) (1.47)

FT wave 1 � year2 ð�d21Þ �0:065 �0:062 �0:082 �0:071 �0:179 �0:185(0.49) (0.45) (0.61) (0.53) (1.36) (1.44)

FT wave 1 � year3 ð�d31Þ �0:120 �0:124 �0:089 �0:084 �0:219 �0:218(0.88) (0.92) (0.65) (0.63) (1.61) ð1:65Þn

FT wave 1 � year4 ð�d41Þ �0:105 �0:096 �0:101 �0:094 �0:191 �0:190(0.79) (0.73) (0.75) (0.71) (1.43) (1.44)

FT wave 2 � year1 ð�d12Þ 0.124 0.128 0.111 0.133 �0:019 �0:033ð2:09Þn ð2:59Þnnn ð1:76Þn ð2:45Þnn (0.34) (0.66)

FT wave 2 � year2 ð�d22Þ 0.091 0.083 0.058 0.065 �0:037 �0:052(1.50) (1.50) (0.88) (1.07) (0.61) (0.92)

FT wave 2 � year3 ð�d32Þ 0.070 0.066 0.089 0.088 �0:040 �0:043(1.42) (1.38) (1.64) ð1:66Þn (0.87) (0.98)

FT wave 2 � year4 ð�d42Þ 0.018 0.017 0.020 0.018 �0:069 �0:071(0.29) (0.29) (0.31) (0.29) (1.15) (1.23)

FT wave 3 � year1 ð�d13Þ 0.187 0.196 0.206 0.247 0.047 0.031ð6:09Þnnn ð4:71Þnnn ð4:80Þnnn ð4:93Þnnn (1.61) (1.03)

FT wave 3 � year2 ð�d23Þ 0.150 0.157 0.157 0.187 0.037 0.027ð5:41Þnnn ð4:73Þnnn ð4:33Þnnn ð4:75Þnnn ð2:09Þnn (1.45)



constant ðb0Þ �0:331 �0:120 �0:230 0.307 �0:059 �0:023ð4:42Þnnn (1.59) ð4:35Þnnn ð2:20Þnn ð3:06Þnnn (0.94)

Observations 911 905 469 469 389 388R-squared 0.31 0.34 0.25RESET 0.61 17:14nnn 4:34nnn 8:88nn 11:93nnn 9:54nn

Robust t statistics in parentheses. nSignificant at 10%; nnsignificant at 5%; nnnsignificant at 1%. FT wave 1 represents the dummyvariable wave1 in case of all non-FTs comparator group, the dummy variable match wave1 in case of matched comparator groupand the dummy variable 3stars wave1 in case of 3 stars non-FTs comparator group. The same applies for FT wave 2 and FT wave3. The sample sizes between the fixed effects and GEE models vary because of different availability of data on the covariates.Covariates used in the models: tests occuppc, occupanc, alos, daycase spell, emerg spell, ep spell, meanwait, dtc, rci.

G. MARINI ET AL.766


DOI: 10.1002/hec

Figures 4(a–c) show the DID results for the wave analysis relative to each of the three comparatorgroups for each of the five years. Wave 1 FTs have the lowest retained surplus after policyimplementation compared with each of the comparator groups though it is not significant. Wave 3 FTs

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30

2000/01 2001/02 2002/03 2003/04 2004/05

wave 1 FTs wave 2 FTs wave 3 FTs

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30

2000/01 2001/02 2002/03 2003/04 2004/05


-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30

2000/01 2001/02 2002/03 2003/04 2004/05


(a)

(b)

(c)

Figure 4. Retained surplus (deficit) as proportion of total expenditure: (a) FTs becoming fully operative at differentdates relative to all non-FT comparator group; (b) FTs becoming fully operative at different dates relative tomatched comparator group; and (c) FTs becoming fully operative at different dates relative to 3 star non-FT

comparator group.



DOI: 10.1002/hec

have the highest retained surplus after policy implementation compared with each of the comparatorgroups. The DID results for wave 3 FTs are significantly higher compared with all non-FTs in 2002/03,2003/04 and 2004/05 (Figure 4(a)) and in years 2003/04 and 2004/05 with respect to the matched controlgroup (Figure 4(b)). Interestingly, wave 3 FTs performed significantly below all non-FTs and thematched control group in 2000/01, suggesting that they significantly improved their retained surplusover the 5-year period. These Trusts were therefore improving their financial position relative to otherTrusts well in advance of the implementation of the policy. For wave 2 the DID results are significant inthe last two years with respect to all non-FTs (Figure 4(a)) and in the pre-intervention year with respectto the matched control group (Figure 4(b)).

RCI

Table VI shows the regression results for the DID models for the RCI.The b1 coefficients are always negative but only significant relative to the all non-FTs comparator

group. The coefficients suggest that RCI was around 4 percentage points lower for FTs comparedwith all non-FTs. The DID estimates for 2003/04 against 2004/05 are negative for the matched controlgroup and positive for the remaining control groups and significant only at the 10% level for the3-star non-FT comparator group under fixed effects and GEE. This suggests that in 2004/05 FTsactually increased their RCI relative to 3-star non-FTs compared with the previous year. This is borne

Table VI. Regression results for difference in difference model for overall effect of FT status on RCI

RCI



FT ðb1Þ �3:871 �2:637 �2:995ð2:58Þnnn (1.36) (1.46)

year1 ðb21Þ �1:820 �0:465 �1:164 �0:587 0.663 0.510(1.60) (0.25) (0.70) (0.37) (0.26) (0.20)

year2 ðb22Þ 1.105 0.312 0.968 1.206 2.096 1.943(1.11) (0.16) (0.54) (0.69) (1.02) (0.96)

year3 ðb23Þ 0.805 0.285 1.126 1.288 2.562 2.578(1.03) (0.16) (0.81) (0.94) ð1:74Þn ð1:78Þn

year4 ðb24Þ 0.982 �0:361 1.511 1.554 3.178 3.186(1.65) (0.44) (1.40) (1.45) ð2:62Þnn ð2:67Þnnn

FT � year1 ð�d1Þ 2.206 2.310 �2:188 �2:293 4.489 4.460(0.77) (0.86) (0.76) (0.80) (1.32) (1.33)

FT � year2 ð�d2Þ 0.870 0.557 �1:563 �1:560 3.144 2.930(0.50) (0.35) (0.71) (0.71) (1.30) (1.24)

FT � year3 ð�d3Þ �0:156 �0:770 �0:601 �0:578 2.194 2.192(0.11) (0.51) (0.32) (0.32) (1.17) (1.18)

FT � year4 ð�d4Þ 0.866 0.093 �1:253 �1:183 2.789 2.784(0.71) (0.07) (0.82) (0.78) ð1:70Þn ð1:72Þn

constant ðb0Þ 99.534 135.344 116.359 125.185 127.794 128.892ð183:80Þnnn ð10:96Þnnn ð10:55Þnnn ð5:87Þnnn ð10:94Þnnn ð6:03Þnnn

Observations 916 911 469 467 387 387R-squared 0.72 0.75 0.72RESET 2:46n 132:63nnn 1.04 55:49nnn 1.04 39:78nnn

Robust t statistics in parentheses. nSignificant at 10%; nnsignificant at 5%; nnnsignificant at 1%. FT represents the dummy variableft in case of all non-FTs comparator group, the dummy variable ft match in case of matched comparator group and the dummyvariable ft 3stars in case of 3 stars non-FTs comparator group. The sample sizes between the fixed effects and GEE models varybecause of different availability of data on the covariates. Covariates used in the models: tests avbeds, tests occuppc, occupanc,alos, daycase spell, emerg spell, ep spell, meanwait, comp, dtc.

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out by Figure 2(a) which suggests that 3-star non-FTs reduced their RCI relative to FTs and so closedthe gap.

Figures 5(a–c) show the RCI for FTs relative to each of the three comparator groups for each of thefive years. It is evident that over time FTs have always tended to outperform various comparator groups

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Figure 5. Reference Cost Index: (a) FTs relative to all non-FTs comparator group; (b) FTs relative to the matchedcomparator group; and (c) FTs relative to 3 star non-FT comparator group.



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of non-FTs by achieving a lower RCI. This is not necessarily a new phenomenon brought about by thepolicy. Indeed, the DID results are always significant except for 2002/03 relative to the all non-FTscomparator group and for 2004/05 relative to 3-star non-FT comparator group. Results are always non-significant relative to the matched control group.

Table VII. Regression results for difference in difference model for overall effect of FT wave analysis on RCI

RCI



FT wave 1 ðb11Þ �7:994 �7:698 �8:096ð3:91Þnnn ð3:21Þnnn ð3:33Þnnn

FT wave 2 ðb12Þ �0:129 1.828 1.831(0.06) (0.82) (0.71)

FT wave 3 ðb13Þ �3:434 �2:749 �1:933ð2:04Þnn (0.89) (0.80)

year1 ðb21Þ �1:988 �0:514 �1:143 �2:140 �0:267 �0:406(1.63) (0.28) (0.69) (1.28) (0.11) (0.17)

year2 ðb22Þ �0:754 0.236 0.975 �0:627 0.511 0.422(0.71) (0.12) (0.54) (0.33) (0.25) (0.21)

year3 ðb23Þ �0:812 0.208 1.135 �0:350 0.593 0.695(0.92) (0.11) (0.81) (0.24) (0.38) (0.46)

year4 ðb24Þ �0:008 �0:373 1.518 0.665 1.946 2.007(0.01) (0.45) (1.39) (0.58) (1.44) (1.53)

FT wave 1 � year1 ð�d11Þ 0.140 0.784 �0:165 0.025 2.507 2.459(0.06) (0.33) (0.07) (0.01) (0.72) (0.74)

FT wave 1 � year2 ð�d21Þ 1.430 1.357 1.747 1.408 3.492 3.299(0.81) (0.79) (0.77) (0.64) (1.33) (1.30)

FT wave 1 � year3 ð�d31Þ 0.693 0.799 1.705 1.421 3.041 3.043(0.38) (0.43) (0.75) (0.67) (1.36) (1.39)

FT wave 1 � year4 ð�d41Þ 2.154 2.159 3.100 2.803 4.090 4.091(1.52) (1.45) ð1:91Þn ð1:69Þn ð2:15Þnn ð2:22Þnn

FT wave 2 � year1 ð�d12Þ 4.253 3.118 3.708 4.318 7.794 7.645(0.78) (0.55) (0.69) (0.84) (1.39) (1.40)

FT wave 2 � year2 ð�d22Þ 0.119 �1:133 0.614 0.601 3.644 3.272(0.05) (0.42) (0.20) (0.20) (1.26) (1.15)

FT wave 2 � year3 ð�d32Þ 0.446 �0:305 1.423 1.215 3.317 3.306(0.22) (0.13) (0.60) (0.52) (1.44) (1.47)

FT wave 2 � year4 ð�d42Þ �0:343 �0:652 0.495 0.372 1.844 1.838(0.16) (0.30) (0.21) (0.17) (0.75) (0.77)

FT wave 3 � year1 ð�d13Þ 4.857 3.567 3.931 5.700 8.409 8.222(0.88) (0.68) (0.69) (1.00) (1.39) (1.40)

FT wave 3 � year2 ð�d23Þ 3.207 1.847 2.985 3.769 6.117 5.758(1.07) (0.60) (0.83) (1.16) ð1:66Þn (1.63)

FT wave 3 � year3 ð�d33Þ �3:465 �4:881 �3:174 �2:482 �0:868 �0:906(1.41) ð1:81Þn (1.29) (0.87) (0.27) (0.30)

FT wave 3 � year4 ð�d43Þ �2:040 �2:558 �0:904 �1:270 0.629 0.629(0.99) (1.21) (0.37) (0.57) (0.27) (0.28)

constant ðb0Þ 120.672 136.300 116.704 172.143 138.924 151.008ð10:36Þnnn ð11:19Þnnn ð10:39Þnnn ð8:60Þnnn ð8:53Þnnn ð8:95Þnnn

Observations 911 911 469 469 387 387R-squared 0.73 0.75 0.73RESET 0.14 139:76nnn 0.81 13:68nnn 1.28 7:58nnn

Robust t statistics in parentheses. nSignificant at 10%; nnsignificant at 5%; nnnsignificant at 1%. FT wave 1 represents the dummyvariable wave1 in case of all non-FTs comparator group, the dummy variable match wave1 in case of matched comparator groupand the dummy variable 3stars wave1 in case of 3 stars non-FTs comparator group. The same applies for FT wave 2 and FTwave 3. The sample sizes between the fixed effects and GEE models vary because of different availability of data on the covariates.Covariates used in the models: tests occuppc, occupanc, alos, daycase spell, emerg spell, ep spell, meanwait, dtc, rci.

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Table VII shows the DID results for the wave analysis using the RCI. b11 is significant and negativerelative to the comparator groups, suggesting that RCI for wave 1 FTs was 8 percentage points lowerthan all comparator groups. b13 is only significant when FTs are compared with all non-FTs,

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Figure 6. Reference Cost Index: (a) FTs becoming fully operative at different dates relative to all non-FTcomparator group; (b) FTs becoming fully operative at different dates relative to the matched comparator group;

and (c) FTs becoming fully operative at different dates relative to 3 star non-FT comparator group.



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suggesting that the RCI for wave 3 FTs was 3 percentage points lower than all non-FTs. Thiscorresponds with Figure 2(b).

�d41 is significant at the 10% level, suggesting an increase in the RCI of around 3 percentage pointsbetween 2003/04 and 2004/05 for wave 1 FTs compared with the matched control and of around 4percentage points when compared with 3-star non-FTs, where it is significant at the 5% level.

Figures 6(a–c) show the RCI by FT waves relative to each of the three comparator groups. Thesefigures show that over the whole period wave 1 FTs have a significantly lower RCI than eachcomparator group. However, after policy implementation, we observe a narrowing of the gap. For wave3 FTs the DID results are significant in the intervention year relative to all non-FTs. For wave 2 FTs theDID results are never significant for all comparator groups.

DISCUSSION AND CONCLUSIONS

In the previous sections we tested whether amongst the ‘early adopters’ of this policy we could detectany effects in terms of superior financial management. We might expect FTs to have lower RCIs andtake advantage of their efficiency to make surpluses under PbR. However, we might expect that retainedsurplus could be either positive or negative, depending on whether benefits under PbR outweighpotential problems with capital investments leading to bigger deficits.

Our results are suggestive of certain trends. Wave 3 FTs seemed to outperform other waves of FTswith respect to retained surplus over most of the period we examined. Wave 3 FTs were thereforebehaving in an optimal and prudent way with regard to their management of financial balancefor a long time prior to the policy intervention. Not only were wave 3 FTs likely more prudent giventhe inherent uncertainty in being the first to undergo the policy treatment, but Monitor’s desire tobuild a critical mass of FTs with the first wave of implementation may also have meant that theirentry criteria were initially less stringent. This would suggest a possible explanation for why wave 1 FTsdid worse than wave 3 FTs on retained surplus in the intervention year (Monitor, personalcommunication).

According to a Department of Health report explaining the NHS deficits from 2003/04 to 2005/06,one important reason for the emergence of deficits has been the slow adjustment to the change inaccounting rules, which from 2004/05 onwards disallowed virement flexibility between capital andrevenue accounts. This amounted to between £200 and £300 million of the aggregate deficit(Department of Health, 2007). This exogenous shock would have affected all Trusts, but those withbetter financial management expertise may have been better placed to weather this shock. Althoughwave 3 FTs were only FTs for a short part of 2004/05, they may have been more proficient at makingthe most of accounting and management skills, relative to other Trusts in a difficult financialenvironment. Their ability to maintain financial balance is evident over a sustained period, well beforethe implementation of the FT policy.

FTs seemed to display a low RCI over the entire period. This suggests that they were operating inan efficient manner and poised to exploit the introduction of PbR some time before the policywas actually introduced. On average FTs’ RCI was around 4 percentage points lower than non-FTs.In particular though wave 1 FTs displayed a lower RCI than other groups, on average about 8percentage points lower. This may suggest that after policy implementation the wave 1 FTs, while notbeing in the strongest position compared with wave 3 FTs in terms of financial balance, still displayedsuperior performance on Reference Costs, which they could then use to their advantage under PbR toimprove their financial position. While there seemed to be a widening of the gap between the threewaves in terms of retained surplus, there seemed to be a slight narrowing of the gap in the RCI measure.Due to the short period of data we have post intervention, we are unable as yet to identify whether wave1 FTs with a lower RCI would be able to translate their profits under PbR into financial surpluses.

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However, a low RCI under PbR may not necessarily translate into higher surplus if adaptation to thenew policy requires incurring transaction costs in the first years of the policy, which might pushrevenues down.

While the DID results for the change in financial position in the final year (the policy intervention) aretherefore not strong, the interest lies in the fact that there are long-standing differential trends betweenthese different groups of Trusts, which has driven them to engage in the uptake of the policy at differentrates. Whether this trend is sustained as the policy is rolled out to additional hospitals, and whetherimproved financial performance is gained at the expense of quality, is a matter for future study.

The financial performance of FTs in the future will also be affected by the regulatory environment inwhich they operate. The principles underlying the freedoms granted to FTs allow them greater flexibilityto decide on the appropriate level of retained surplus. Thus, they are not subject to the same level ofcentral pressure not only to tackle deficits but also to meet ambitious targets for surpluses that otherhospitals have experienced recently (Day, 2006). However, FTs are not exempt from these financialpressures. Out of 59 FTs, Monitor has rated four of them as risk rating 2 for financial matters(signifying ‘risk of significant breach in terms of authorisation in the medium term, e.g. 9–18 months inthe absence of remedial action’) and a further 14 FTs as risk rating 3 (signifying ‘regulatory concerns inone or more components. Significant breach of terms of authorisation is unlikely’) (Monitor, 2007a).Results for 2005/06 showed that while overall the financial position of FTs was relatively healthy, therewere 11 FTs with deficits, with one FT running a deficit of almost £40 million (Monitor, 2006). Resultsfor Q4 2006/07 showed a consistent improvement in the financial performance of FTs. Out of 59 FTs,56 reported a surplus, with the same one FT running a deficit in excess of £1m (Monitor, 2007b).Monitor has emphasised that the generation of greater surpluses is key to the FT regime and is a signalof efficiency. FTs have been encouraged to move away from the notion that it is somehow inappropriateto make surpluses when other Trusts are in financial difficulty or to worry that commissioners will usethe existence of healthy surpluses as a reason for adopting a hard negotiating position (Monitor, 2006).Monitor also suggests that previous experience shows that FTs are able to ‘turn-around’, respondingquickly and effectively to financial challenges, and they are therefore optimistic that the financialdifficulties faced by a few FTs will be temporary. If they prove to be more entrenched, it is not clearwhat the mechanism will be for tackling these issues}revoking the licence to operate as an FT will workas an interim measure but will be weaker once the FT process rolls out to all Trusts.

Our analysis assesses an early stage in the FT process, so in some respects we did not expect to findmajor effects. However, we have also been able to explore the period during which Trusts werepreparing to become FTs and we have provided a useful baseline analysis on which future research canbe based.

APPENDIX: DATA

The database on NHS hospitals consists of 173 Acute and Specialist Trusts for the period 2000/01–2004/05 and contains approximately 400 variables. The main data sources are:

1. Reference Costs: http://www.dh.gov.uk/PolicyAndGuidance/OrganisationPolicy/FinanceAnd-Planning/NHSReferenceCosts/fs/en.

2. Trust Financial Returns (TFR data) and Trust Accounting Costs (TAC data) from CIPFA http://www.cipfastats.net/health/default.asp.

3. Hospital Activity Statistics: http://www.performance.doh.gov.uk/hospitalactivity.4. Hospital Episodes Statistics: http://www.hesonline.nhs.uk/Ease/servlet/DynamicPageBuild?si-

teID=1802&categoryID=192&callingCatID=325.5. Dr Foster: http://www.drfoster.co.uk/ghg.



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6. Workforce data: http://www.dh.gov.uk/PublicationsAndStatistics/Statistics/StatisticalWorkAr-eas/StatisticalWorkforce/fs/en.

7. Vacancy survey: http://www.dh.gov.uk/PublicationsAndStatistics/Statistics/StatisticalWorkAreas/StatisticalWorkforce/StatisticalWorkforceArticle/fs/en?CONTENT ID=4087110&chk=1m8J3Q.

8. Performance ratings: http://www.dh.gov.uk (for years 2000/01 and 2001/02), http://www.chi.nh-s.uk/ratings (for year 2002/03), http://ratings2004.healthcarecommission.org.uk (for year 2003/04)and http://ratings2005.healthcarecommission.org.uk (for year 2004/05).

9. Waiting times: http://www.performance.doh.gov.uk/waitingtimes/index.htm.

ACKNOWLEDGEMENTS

We would like to thank Angela Bate, Diane Dawson, Marin Gemmill, Miguel Gouveia, John Hutton,Gavin Lewis, Pedro Pita Barros, James Raftery, Nigel Rice, Gavin Roberts, Luigi Siciliani and NunoSousa Pereira for these useful comments. We also thank Sue Slipman, Director of the Foundation TrustNetwork, Jenny Reindorp, Project Manager of the Foundation Trust Network, Miranda Carter,Assessment Director at Monitor for authorising the use of Consolidation reports from a number ofFoundation Trusts, Robert Harris, Policy Director at Monitor and Edward Lavelle, RegulatoryOperations Director at Monitor for discussions on our results, and Steve Martin and Carol Propper forsharing data with us, as well as two anonymous referees for their useful comments on a previous versionof this paper.

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