The impact of policy on hospital productivity: a time seriesanalysis of Dutch hospitals

Jos L. T. Blank & Evelien Eggink

Received: 16 July 2013 /Accepted: 8 November 2013# Springer Science+Business Media New York 2013

Abstract The health care industry, in particular the hospitalindustry, is under an increasing degree of pressure, by anageing population, advancing expensive medical technologya shrinking labor. The pressure on hospitals is further in-creased by the planned budget cuts in public spending bymany current administrations as a result of the economic andfinancial crises. However, productivity increases may allevi-ate these problems. Therefore we study whether productivityin the hospital sector is growing, and whether this productivitygrowth can be influenced by government policy. Using aneconometric time series analysis of the hospital sector in theNetherlands, productivity is estimated for the period 1972–2010. Then, productivity is linked to the different regulationregimes during that period, ranging from output funding in the1970s to the current liberalized hospital market. The resultsindicate that the average productivity of the hospital sector indifferent periods differs and that these differences are relatedto the structure of regulation in those periods.

Keywords Hospitals . Regulation . Productivity . Costfunction . Time series

1 Introduction

The ageing population in a number of western countries isincreasing cost faced by the hospital industry. As the

population ages, the advances in expensive medical technol-ogy will also increase demand for hospital care. As demandincreases, personnel shortages are to be expected due to theshrinking labour force as a result of the ageing population anddeclining birth rates. On average, the health care sector ac-counts for about 10 % of GDP in 2010. This percentageincreases, in most countries, at an average of 2 % annuallybetween 2000 and 2010 [1]. The hospital sector comprises athird of health care spending, varying between 24% and 44%[2]. The pressure on hospitals is further increased by theplanned budget cuts in public spending by many currentadministrations as a result of the economic and financialcrises. The above-mentioned problems can, however, partlybe alleviated by an increase in productivity. The question weask here is whether government policy can improve produc-tivity. It is therefore important to have an insight into produc-tivity change and how productivity change can be influenced.In other words, does the government enforce external incen-tives or reinforce barriers to change? From these issues, tworesearch questions can be derived:

& What is the productivity change in the hospital sector inthe past three decades?

& Has productivity change been influenced by governmentpolicy?

We answer these two questions by presenting a historicalanalysis of the productivity change of the hospital sector in theNetherlands. Using an econometric time series analysis of thehospital sector, hospital productivity is estimated for the peri-od 1972–2010. From this aspect of the research, productivityis linked to the different regulation regimes during that period.Whereas output funding was imposed in the 1970s, this wasreplaced by a budgeting system in the 1980s and 1990s andlater by the current liberalized hospital market. We test thehypotheses that the budgeting system outperforms the system

J. L. T. Blank (*)Delft University of Technology/Erasmus University Rotterdam,PO Box 5015, 2600 GA Delft, The Netherlandse-mail: j.l.t.blank@tudelft.nl

E. EgginkThe Netherlands Institute for Social Research, PO Box 16164,2500 BD The Hague, The Netherlandse-mail: e.eggink@scp.nl

Health Care Manag SciDOI 10.1007/s10729-013-9257-8

of output financing, and the competitive market system out-performs the budgeting system. Thus, the hypothesis to betested in this paper is that the average productivity of thehospital sector in different periods of time differs and thatthese differences are related to the structure of regulation inthose periods of time.

The paper is structured as follows. We begin with a conciseoverview of the literature on the relationship between regula-tion and productivity of the hospital sector. This descriptionfocuses on funding, ownership, capacity regulation and com-petition. Section 3 contains a brief description of the Dutchhospital sector and the significant institutional developmentsbetween 1972 and 2010, as well as describing the relevantdata and quantitative outline of historical trends in key vari-ables, such as production volume, staffing and costs. Section 4discusses the econometric approach used to measure produc-tivity, including the mathematical description of the cost func-tion model that relates the cost to production, resource pricesand to the different regulatory periods is provided, as well asthe methodological approach for estimating the parameters. InSection 5 we show the results of the empirical analysis andpresent the main conclusions, while the final conclusions aregiven in Section 6.

2 Literature review on productivity and regulationof hospital care

2.1 Why do governments intervene in the hospital sector?

There is a general consensus on the important role of healthcare in social welfare, and citizen access to health care iscrucial from both an individual and a collective perspective.Unfortunately, the health care market is confronted with mar-ket imperfections due to serious information problems. Inparticular, patients lack the medical expertise to assess theappropriateness of the recommendations of physicians. Asidefrom the information problem, the hospital market also hasstrong entry barriers due to a high fixed cost of physical andhuman capital leading to monopolistic tendencies in the mar-ket. The outcome of a market process will therefore deviatefrom a social optimum. This market failure provides thegovernment with a strong argument for intervention in healthcare markets. However, the government also has to deal with anumber of serious problems that can be characterized asgovernment imperfections. The government also faces infor-mation problems, in particular on the patient-doctor level.They can only govern the process on a higher aggregationlevel by quality regulation, by inspections, by licensing, byfixing budgets and so on. Insurance companies also play animportant role alongside government and health care sup-pliers. Theymay be capable of reducing information problemsandmay act as a countervailing power to health care suppliers.

On the other hand, the involvement of insurance companiesmay lead to new problems, such as moral hazard (at patientlevel), adverse selection (patients), cream skimming (insurers)and high transaction cost (high administrative expenses). Gov-ernment policy on health care has been a struggle to find asolution to all these problems and to find a balance betweenthe roles of patients, insurance companies, health care sup-pliers and government.

2.2 Government policy instruments

The Government has a number of policy instruments at itsdisposal. Depending on the system structure of health careservices that vary from a national publicly owned health caresystem to forms of regulated market-oriented health care.Systems can generally be characterized along four dimen-sions: ownership, financing, market regulation and capacityplanning. Government can bring its influence to bear on eachof the four dimensions in various degrees. In the case of publicownership, for instance, the government manages a hospitaldirectly, whereas in a case of private ownership the board ofdirectors, the supervising council and shareholders (in the for-profit sector) are responsible for day-to-day management andstrategic decisions. The same holds for the dimensions offinancing a hospital, hospital service price-setting and hospitallocation and capacity decisions. There is a vast amount ofliterature on each of these subjects. A summary of these issuescan be found in Blank & Valdmanis [3].

2.3 Ownership

Property rights theory claims that for-profit (FP) hospitals mayperform more efficiently than non-profit ones, due to attenu-ation of wealth in the latter. It is also argued that in the case ofnon-profit hospital’s management and physicians may striveto maximize quantity and quality of production at a givenbudget [see e.g. 4]. In order to do so, they will have a strongincentive to maximize net revenues. Aside from the profit/not-for-profit issue, allocative constraints may also affect efficien-cy outcomes. In the case of a hospital that is government-owned and has general rules and instructions on how toconduct business operations, leading to non-optimal resourceallocations. In publicly owned hospitals managers may alsobehave like bureaucrats with all the attendant inefficiencies[see e.g. 5]. Mutter & Rosko [6] conclude from an extensiveliterature review and their own research that, in the US, FPhospitals are more cost efficient than NFP hospitals which inturn are more cost-efficient than publicly-owned hospitals.Also, Mache et al. [7] indicate a higher level of productivityin German FP hospitals, controlling for case mix. Without theinclusion of case mix, efficiency may be masker by possiblecream skimming effects. In addition to the patient case mixwhich positively affects resource use, here are no indications

J.L.T. Blank, E. Eggink

that FP hospitals provide less quality than NFP hospitals.However, Eggleston et al. [8] find that for-profit hospitalsare more productive, and their quality level is inversely related.Other studies do not find productivity differences between FPand NFP hospitals [e.g. 9–11], or even that NFP hospitals aremore productive [12]. From other studies (Valdmanis [13]Daidone and D’Amico [14]) it seemas that public and not-forprofit hospitals will operate less efficiently. Thus: there is noconsensus regarding the impact of ownership.

2.4 Capacity planning

Location and capacity planning refer to the supply of hospitalsthereby linking to issues of market concentration and econo-mies of scale and scope. In an extensive literature reviewBazzoli [15] states that mergers in the U.S.A. do generatesome cost savings. However, the efficiency gains are minimaland only apply to mergers between small hospitals. Further,economies of scale are non-existent in the case of amalgam-ation of multi-hospital health systems. In most cases, hospitalconsolidation has led to higher prices, revenue and profits. Itmay also be argued that managers and doctors may behavemore or less like shareholders, since they are able to convertpotential profits into higher wages and financial compensa-tions, also known as “profits in disguise” [see e.g. 16]. Thereis also a long list of studies on European hospitals indicatingthat many large hospitals face diseconomies of scale [see e.g.17–24]. Two conclusions can be drawn from the results ofthese studies: that long-term trends in consolidation maynegatively affect productivity change and that a governmentmay wish to intervene in this process by setting up rules todiscourage mergers such as by creating disincentives via thefinancing systems.

2.5 Concentration and competition

Sari [25] describes the concentration of increasing numbers ofmergers of US hospitals during the last two decades. Asidefrom the possible economies of scale effect, the concentrationitself may have serious anticompetitive effects and as a resultnegative consequences on hospital efficiency. However, it wasargued in specific court cases that mergers in US hospitalsmight increase efficiency leading to lower costs. This finding,while counterintuitive in traditional economics, makes sensein the hospital sector in which competition is waged on thenumber of inputs, i.e. the medical arms race.. According toSari [25] the literature shows that increased competition leadsto higher prices and has negative welfare consequences. Theeffects of competition on productivity are ambiguous, sinceincreased competition may put some pressure on serviceprices and consequently lead to higher efficiencies [26]. Insome studies no relationship between competition and pro-ductivity existed at all at all [27, 28]. In markets where price

competition does not exist, market pressures may lead toimproved (and occasionally overabundant) luxury or over-treatment, sometimes resulting in efficiency declines. Gaynorand Voigt [29] review studies related to competition. Becauseof changes in hospital regulation (such as prospective pay-ment), competition in the US hospital industry has shiftedfrom non-price to price competition.

2.6 Financing systems

The relationship between financing and productivity in hos-pitals has been a topic of research, specifically the replacementof fee-for-service payment systems (FFS) by prospective pay-ment systems (PPS). The theoretical reason behind thischange is that a volume-based reimbursement in hospitalindustry increases demand and the potential of unnecessaryor excessive usage of medical services caused by physicians.This problem of over-utilization of medical services, supplierinduced demand, the well-known Roemer’s law, can be mit-igated by the development of a reimbursement system thatcreates incentives for hospital management to contain costs. Inthis case, establishing adequate prospective (fixed) paymentsdepends on identifying hospitals’ products and determiningaccurately the cost of each product. Studies on financingreforms among European/OECD countries confirming thesetheoretical considerations include: Austria [30], Norway [31]and Japan [24].

The brief discussion given above does not suggest that apanacea for government policies enhancing hospital produc-tivity currently exists. Some instruments are only effectiveunder certain conditions, whereas other instruments mightonly work when accurate parameters are established. In addi-tion, the above discussion only relates to the microeconomicbehaviour of various (individual) actors, whereas in practicepolicymakers also have to deal with feedback or interaction ona higher aggregated level. This is illustrated by the fact that thetightening of tariffs for self-employed physicians may stimu-late physicians to become employed by the hospital, leading toa probable lowering of productivity [for evidence from theNetherlands see [32]. There is little literature available thatempirically identifies this type of sector effect. In order to fullyunderstand how these effects can be operationalized, othertypes of analysis are probably more appropriate, such as timeseries analysis and cross-country comparisons. Examples canbe found in Jonker [33] where the entire hospital industry isthe object of the study.

3 Regulation in the Dutch hospital sector

The hospital sector forms a substantial part of the Dutch publicsector. In 2010 the costs amounted to almost 13 % of totalpublic expenditure and about 14 % of civil service personnel.

The impact of policy on hospital productivity

In the Netherlands there are three types of hospitals: general,academic and specialized hospitals.

Nearly all hospitals in the Netherlands are non-profit insti-tutions. There are a number of for-profit Dutch hospitals, butin terms of total hospital cost they comprise a small section ofthe total market, and they are omitted from the researchpresented here. The planning, quality and finance of thenon-profit hospitals is regulated by the central government.In a legal sense they all are private firms (foundations). In the1970s a substantial number of hospitals were still owned bythe (local) government. In the 1980s all of these municipalityhospitals were closed or transformed into private hospitals.

From 1972 through 2010, three periods are distinguished,following the transitions in financing schemes and other formsof regulation. The cut-off points in our analysis coincide withthe (most significant) dates of the introduction of major re-forms during this time period. Since most reforms are intro-duced in phases or during an extended period of transition,exact reform dates are not readily available. Therefore, estab-lishing the cut-off points, we take some empirical consider-ations into account, which will be explained in the estimationsection. With these caveats in mind, the three major periodswith a relatively consistent policy regime are:

1. 1972–1982: output financing/public-private ownership2. 1983–2001: budget financing/private ownership/capacity

regulation3. 2002–2010: competition (hybrid form with budgeting)

3.1 1972–1982: output financing/public-private ownership

Since the early 1970s hospitals have been reimbursed on acost basis, i.e. an output-oriented financing system. This sys-tem led to inefficiencies due to a non-optimal allocation ofresources among hospital departments as well as overproduc-tion. As hospital costs steadily increased, the role of govern-ment spending and government regulation also increased.

3.2 1983–2001: budget financing/private ownership/capacityregulation

In 1983, a budgeting system was introduced that reimbursedhospitals on the basis of previously agreed production levels,and fixed prices per product. Due to the relatively long imple-mentation time, as of 1988 about 70 % of the budgets werestill based on historical costs and therefore unrelated to theproduction level. This substantial fixed budget hampered ef-ficiency, and competition was non-existent.

In 1988 the size of this fixed budget-component was re-duced and was based on the hospital’s operational serviceregion rather than the hospital itself. A much larger compo-nent of financing was now related to production. This systemwas implemented, with some refinements, until 2001.

3.3 2002–2010: competition (hybrid form with budgeting)

The prevalent system during this time period included anincentive for mergers and concentration, since larger hospitalsreceived higher prices for some of the items in the budgetingsystem. In addition, waiting lists started to grow, as exceedingthe agreed production levels did not yield more reimburse-ment. At the beginning of this century the governmentabolished the strict budgets and the strict building regimeand commenced the liberalization of the hospital market inorder to reduce the waiting lists. Since 2005, the hospitalshave been reimbursed on the basis of product-prices. Follow-ing this first step, competition was introduced in the hospitalmarket in 2006, starting with about 10 % (in terms of cost) ofhospital production. Prices and the production of the remain-ing 90 % were still under the control of the central authorities.This proportion of “liberalized production” increased to 70 %in 2012.

3.4 Backgrounds of policy reforms

Before the 1980s the hospital sector traditionally deliveredcare without considering the financial consequences. As hos-pital costs started increasing rapidly in the seventies, costcontainment and productivity became an issue in politicsand policy. The possibilities of system reforms in order toimprove productivity came into view. The budgeting systemattempted to improve productivity by providing services givenlimited resources. In other words, the professional wish tomeet growing health care demand combined with budgetconstraints was expected to be a strong incentive for hospitalsto increase productivity. In the 1990s it turned out that, al-though costs were contained, health care demand was ra-tioned. Waiting lists occurred and quality of care was at stake.To address this dilemma, policy makers embraced the allegedadvantages of a more liberalized and competitive health caremarket. Liberalization and competition were assumed to in-clude more financial incentives to the system and to reduce thebarrier of allocating resources optimally in order to improveproductivity.

4 Data and historical trends

4.1 Data collection

The empirical analyses performed for this study are based on aset of time-series indicators of the Dutch hospital sector. Thedata are derived from the Database Public Sector (DPS) thatcontains information on various public sector services in theNetherlands, including education, safety and health care, frompublicly accessible information, principally from the Statlinedatabase of Statistics Netherlands. The data are converted into

J.L.T. Blank, E. Eggink

time series indicators. Here indicators on production, costsand the use of resources for the Dutch hospital sector are used.

Even though regulation differs slightly between the typesof hospitals, this research studies the hospital sector as awhole, including general, academic and specialized hospitals.The data cover the period 1972–2010, yielding us 39 obser-vations. Table 1 describes the data used in the analyses.

In the analysis of productivity and cost changes, threeresources are distinguished: personnel, material supplies andcapital inputs. The costs of personnel measure the costs of allpersonnel, including physicians and temporary personnel. Thecosts of material supplies include the costs of, for example,food and medical supplies. Finally, the costs of capital inputsare measured by the respective depreciation rates.

Since the resource prices are not directly available from thedata base we use here, other indicators must be derived. Theprice of personnel is computed as the personnel costs perfulltime equivalent. The price of material supplies is set atthe consumer price index. Finally, the price of capital inputs iscomputed by dividing the capital costs by the volume ofcapital inputs, calculated as explained below. The volume ofpersonnel is given by the number of fulltime equivalents(FTEs) corrected for the number of working hours per year;the volume of material supplies is calculated by dividing thematerial costs by the price index of material supplies. Thevolume of capital inputs is derived from data on depreciationand investments in the hospital sector, using the PerpetualInventory Method [34]. According to this method, the actualinput of capital is equal to an aggregate of historical flow ofinvestments, taking into account the depreciation of capitaland the price of investment goods.

Production is measured by the number of admissions. Nodistinction is made between day admissions and (clinical)admissions. Since day admissions require fewer resourcesthan admissions, a shift from admissions to day admissionscan be seen as an increase in productivity. More detailedinformation on production, such as the number of outpatientvisits or the percentage of patients over 65 as a measure ofcase mix was tested, but did not improve the model’s expla-nation of productivity. Finally, production of admissions (in-cluding day-admissions) varies between 1.4 million in 1972 to3.9 million in 2010. In addition, the number of first outpatientvisits without an admission varied between 1.4 million in1972 and 6.9 million in 2008.

4.2 Historical trends

In recent decades, the number of Dutch hospitals has de-creased, from 260 in 1972 to 107 in 2008. This was causedby the withdrawal of government from hospital managementin the 1970s; the incentives for mergers included in the fi-nancing system during the 1990s (see Section 3.1); the incen-tive for increasing market power in the last decade and bytechnological developments that require expensive equipmentthat only larger hospitals can afford. It is during the period ofliberalization that annual production increased substantially;an average 2.8 % increase in the number of admissions (in-cluding day admissions) is realized. This growth is mainly dueto the increase in day admissions, which were non-existent in1972, but account for half of the total number of admissions in2010. This means that the average length of stay has decreased

Table 1 Descriptive statistics,variables on the Dutch hospitalsector 1972–2010

Notation Mean St. Dev. Minimum Maximum

Costs

total costs(× million Euro) c 9,000 5,600 1,800 22,600

personnel costs (× million Euro) cp 6,100 3,500 1,400 14,000

costs material supplies (× million Euro) cm 2,400 1,700 0,300 6,500

capital costs (× million Euro) cc 0,500 0,400 0,100 2,000

Resource prices

price personnel (index 1972=100) wp 332 147 100 618

price material supplies (index 1972=100) wm 236 71 100 351

prices capital inputs (index 1972=100) wc 240 81 100 360

volume resources

Volume personnel (fte × 1.000) pers 123 15 100 161

Volume material supplies (index 1972=100) mat 275 129 100 575

Volume capital inputs (index 1972=100) cap 203 117 100 760

Production

admissions incl. day-admissions (× 1.000) adm 2,200 700 1,400 3,900

outpatients (× 1.000) outp 3,700 2,100 1,400 6,900

The impact of policy on hospital productivity

substantially, from 17 days per admission in 1972 to about3 days in 2010.

Corresponding to the growth of production, the nominalcosts have increased on average by 6.8 % per annum, from 1.8billion euros in 1972 to 22.3 billion euros in 2010 (See Fig. 1).Personnel costs account for the largest share of the costs, butthis share has decreased from 77% to 62%, whereas the shareof material cost increased (from 18 % to 29 %).Part of theincreasing costs may be attributed to increasing prices. Forinstance, inflation may amount to an annual price increase ofabout 3.5 %. In Figure 2 it is shown that personnel costsincrease even faster by a factor of 6 (4.9 % annually), whilematerial supplies tripled (3.4 % annually). The price of capitalinputs increase until 2004, but decreases thereafter due tolower rents. The price of capital inputs almost triples between1972 and 2010.

5 Specification and estimation

5.1 A cost function model

Productivity changes are derived using an estimated costfunction. The productivity changes are measured at thenational level, with the hospital sector as the unit ofobservation. The model is based on the neoclassicaltheory describing the (optimal) management of organi-zations [see e.g. 35, 36]. This assumes a relationshipbetween resources and services delivered modelled usingthe objective of cost minimization. A cost functionmodel allows for a multiple-resources multiple-servicesanalysis that is suitable for studying complex sectorssuch as the hospital sector. From the cost function, costshare equations can be derived that describe the demandfor resources [37].

A hybrid translog cost function [see 38] is used here, sincenot all the second order terms are included to reduce thenumber of parameters to be estimated. The translog functionis a rather flexible form that allows for varying economies ofscale, varying resource substitution and varying technicalchange which comes from varying production levels, varyingresource prices and over different points in time. However,since the number of parameters would grow too large for ourdataset that consists of 39 observations, we have to restrict theflexibility to some extent by using a hybrid form. The secondorder terms of input prices are included since they appear(linearly) in the cost share equations and can therefore easilybe estimated. However, the cross terms between resourceprices and services are omitted. The cost equation also in-cludes a first order lag operator that will be explained below.This leads to the following cost function model:

ln Cð Þ ¼ a0 þXm¼1

M

bmln ymð Þ þXn¼1

N

cnln wnð Þ

þXn¼1

N Xn0 ¼1

N

cnn0 ln wnð Þln wn0� �þ struc

þXn¼1

N

j1n � time � ln wnð Þ þ b0Xm¼1

M

bmΔln ymð Þ

þu

Where:

ym production service m (m =1, … M )Δym change in production service m (m =1, … M )wn resource price n (n =1, … N)struc structural variabletime year- 1972u error term.

0

200

400

600

800

1000

1200

1400

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

cost admissions (incl. day-care admissions)

Fig. 1 Cost and production ofDutch hospitals 1972–2010(index, 1972=100)

J.L.T. Blank, E. Eggink

bm, cn, cnn, j1n, and parameters to be estimated.And:

struc ¼Xp¼1

P

a1 þXq¼1

p

aaq � Tq−Tq−1� �þ aap � year−Tp

� �" #

� year ∈Ip� �

Where:

Ip time period pTp last year of period pT0 first year of analysis

a1, aap and aaq parameters to be estimated.In order to make sure that the graph is “tied” at the cut-off

point, additional restrictions are imposed:

apþ1 ¼ a1 þ aa1 � T 1ð Þ þ…þ aap � Tp−T p−1ð Þ� �

p ¼ 1; 2;…;P

The structural variable distinguishes the period of analysisinto different time periods, in which a different regulationregime is valid. In Section 3 we explained the choice for thethree different time periods. We have chosen to fix the cut-offpoints at 1982 and 2002, based on the historical major policyreforms. The three periods are indicated here by I1 (1972–1982), I2 (1983–2001) and I3 (2002–20010).

The corresponding cost share equations can be derived byusing Shephard’s Lemma and are given by:

Sn ¼ cnln wnð Þ þXn0¼1

N

ln wn0� �þX

n¼1

N

j1n � time þ u;

n ¼ 1…N

Where:

Sn cost share of resource n

According to theory, some parameters need to be restricted,such as the symmetry in the effects of resource prices thatleads to:

cnn0 ¼ cn0n

For the parameters of resource prices there is a homogene-ity restriction (of degree 1). This means that a generic priceincrease leads to a proportional cost increase. In terms ofparameter restrictions this yields:

Xn¼1

N

cn ¼ 1;Xn¼1

N

cnn0 ¼ 0 ∀n0

� �;Xn¼1

N

jtn ¼ 0 ∀tð Þ

The cost function must be non-decreasing in resourceprices (a price increase cannot lead to a cost decrease)and concave in resource prices (a 1 % increase of aninput price does not increase cost by more than 1 %times the cost share of that resource). As usual, theserequirements are examined after the estimation. The costfunction is non-decreasing if all cost shares predictedbased on the estimated parameters are positive. Theconcavity is checked by testing whether the price elas-ticities of demand are negative (a price increase leads toa decrease in the quantity demanded). The specific priceelasticities of demand for resource n are given by:

ηnn ¼ cn 1þ cnnS2n

−1

Sn

� �ð4� 3Þ

where:

ηn elasticity of demand of input n .

0

100

200

300

400

500

600

700

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

personnel material supplies capital inputs

Fig. 2 Resource prices Dutchhospitals, 1972–2010 (index,1972=100)

The impact of policy on hospital productivity

5.2 Estimation method

Whereas in many productivity studies sophisticated methodssuch as Data Envelopment Analysis or Stochastic FrontierAnalysis are applied, we prefer to conduct a time seriesregression analysis. The reason behind this is twofold. Fron-tier techniques are applied to derive efficiency scores of indi-vidual firms under the assumption of a constant technologyfor all firms. Since we are interested in historical changes weare dealing with continuously changing technologies. Theconstant technology assumption obviously does not hold.The second reason is that we do not focus on efficiencydifferences between hospitals but on productivity changesthrough time for the whole sector.

The cost function model includes a large number of param-eters, especially if all various resources and services are in-cluded. In particular, in a time series (as is this case here),estimating a large number of parameters leads to econometricproblems arising from a number of causes.

Firstly, time series usually have relatively few observa-tions, leading to a limited number of degrees of freedom.Secondly, most time series are non-stationary, implying thatusing OLS would lead to spurious correlation. The strongcorrelation between observations would also lead tomulticollinearity, yielding non-efficient estimators. The strongcoherence between explanatory variables makes it impossibleto attribute the variation in the endogenous variable to indi-vidual explanatory variables.

Econometrics offers several solutions to this problem. Thesimplest and most widely used method is to allow for auto-correlation by applying an autoregressive transformation to allthe variables in the model. This means that the estimationdeals with changes in variables rather than the levels them-selves. This means that each variable f in the model is trans-formed as follows f-ρf (-1). The parameter ρ is established onthe basis of grid search and fixed at 0.85. The trend and thecorresponding correlation is then eliminated from the model.Multicollinearity can only be avoided by including additionalinformation, such as fixing some parameters beforehand,based on values found in earlier research, or by imposingtheoretical restrictions. Here the latter option is used, byimposing constant returns to scale . This means that an in-crease in production leads to a proportional increase in costs:

Xm¼1

M

bm ¼ 1

Another problem lies within the dynamics of economicsystems. Usually, a change in a variable such as productionlevel does not immediately lead to a change in cost. Initially,the present capacity will allow for an extension of production,whereas hiringmore staff and increasing capital inputs will lag

behind. The estimated effects will therefore describe short-term reactions rather than long-term relationships. In order tocapture this effect, an additional term is added to the modelreflecting the change in services. In the case of hospitals, asudden increase in production may be interpreted as an in-crease in the hospital’s occupancy rate of the hospital capacity.If the system responds immediately to such a change in theoccupancy rate, the productivity does not change; if the re-sponse is slow, the productivity may either increase ordecrease.

Since the cost function model consists of a system ofequations with parameter restrictions between equations, themethod of Seemingly Unrelated Regression is adopted. Theresults are then reviewed on the basis of explained variance,the Breusch Godfrey test (indicating remain trends) and thestatistical significance of parameters.

5.3 Estimation results

Table 2 shows the estimates, the standard errors and t -valuesof the parameters, the Breusch-Godfrey tests on autocorrela-tion, and the corresponding p -values (for each of theequations).

In Table 2 the results of the model neatly represents thecosts of the hospital sector given that a majority of parametersare statistically significant at the 5 % level. The Breusch-Godfrey statistics indicate that there is no autocorrelationremaining (trend in the trend), except for the cost share ofthe capital equation (although not significant at 1 % level).Additional statistical tests indicate that the model satisfies theconcavity requirement (all elasticities of demand arenegative).

On average, the autonomous costs decrease by 0.3 % an-nually (not related to production or price changes) in theperiod 1972–1982. This implies a modest increase in produc-tivity. The following periods show an annual autonomousdecrease in costs of 1.2 % (1983–2002) and 0.3 % (2002–2010). The corresponding standard error in the first period israther high (0.09) indicating that there is much variation inthese autonomous trends during different years within thisperiod and implying that the effect of the output financingdoes not seem to be very consistent. We should also bear inmind that this parameter is based on a rather short period, sothe finding may be an artifact of the data used here. The lowstandard errors for the estimates for the autonomous trend inthe second and third period imply that the estimated effects ofthe budgeting and competition seem to be more reliableestimates.

Since we imposed constant returns to scale (CRS) on thecost function, an increase in production automatically leads toa proportional cost increase. This implies that the possible costimpact of the increasing scale of hospitals will be captured bythe autonomous cost change parameters. Since we know from

J.L.T. Blank, E. Eggink

cross-section analysis that Dutch hospitals typically operate inthe area of decreasing returns to scale [17], this CRS constraintdampens the measured autonomous cost changes.

One very interesting estimated effect concerns the produc-tion change variable (b0=−0.45) which indicates that produc-tion change itself leads to reduced costs (and thus increasedproductivity) due to the lagged responses of hospitals to thepermanently growing services. Since hospital production hasbeen consistently growing over the years, this variable ex-plains the general increase in overall productivity.

Price changes have led to substitution between the inputs asreflected by the elasticities of substitution. The estimates ofthe price coefficients indicate that the inputs are substitutes: allelasticities of substitution are positive. Increases in the price ofone input will lead to an increased use of the other inputs.Especially the lower prices of capital inputs since 1992 led to asubstitution from personnel and material supplies to capitalinputs. Aside from the economic substitution, we also seetechnical change affecting resource allocation. The estimatesof the technological change parameters (j 11<0, j12>0, j13>0)indicate that through time personnel has been substituted bymaterial supplies, and to a lesser extent capital inputs. Thustechnical change has thus not been Hicksian neutral.

In spite of the fact that the estimates of technical changediffer substantially between the first and second time period

(−0.3 % versus −1.2 %), there is a lack of strong statisticalevidence. It shows that the hypothesis that states that thedifference between the parameters aa2 and aa1 equals zerocannot be rejected (p -value=0.31, see Table 3). When wecompare the second and third period, we observe that thereis statistical evidence for different technical change (p -value=0.04). During this budgeting period, technical change wassubstantially higher than in the period referred to as the periodof competition (1.2% vs. 0.3 %). This is contrary to the notionthat competition would improve productivity that fuelled thepolicy reforms. Although productivity has increased duringthe competition reforms, this is not a result of induced tech-nical change but rather a result of increased production levels.In periods of production growth the productivity observed aredue tot the fact that the necessary adaption of resources lagsbehind. The decreased productivity growth implies that the

Table 2 Estimates cost function model (1972–2010)

Variable Parameter Standard deviation T-value

Constant a1 0.099 0.103 0.953

Trend 1972–1982 aa1 −0.003 0.009 −0.302Trend 1983–2001 aa2 −0.012 0.002 −5.851Trend 2002–2010 aa3 −0.003 0.003 −0.815Production change b0 −0.449 0.095 −4.716Admissions incl. day admissions b1 1.000 0.000 0.000

Price personnel c1 0.754 0.012 62.473

Price material supplies c2 0.214 0.028 7.542

Price capital inputs b c3 −0.221 0.030 −7.295Price personnel × price personnel c11 0.007 0.012 0.597

Price personnel × price material supplies c12 0.217 0.016 13.842

Price personnel × price capital inputs c13 0.136 0.037 3.659

Price material supplies × price material supplies c22 0.085 0.015 5.670

Price material supplies × price capital inputs c23 0.030 0.008 3.641

Price capital inputs × price capital inputs c33 −0.092 0.010 −9.468Trend × price personnel j11 −0.006 0.001 −11.834Trend × price material supplies j12 0.005 0.001 8.251

Trend × price capital inputs j13 0.001 0.000 3.067

Breusch-Godfrey Test-value P-value

Cost function 2.950 0.086

Cost share personnel 0.000 1.000

Cost share material supplies 0.001 0.975

Cost share capital inputs 5.807 0.016

Table 3 Test results for hypotheses productivity change (1972–2010)

Hypotheses Hypothesis p-value Result

Trend 72-82=trend 83-01 aa1=aa2 0.31 Not rejected

Trend 72-82=trend 02-08 aa1=aa3 1.00 Not Rejected

Trend 83-01=trend 02-08 aa2=aa3 0.04 Rejected

The impact of policy on hospital productivity

Dutch hospital sector has not benefited from the introductionof competition reforms.

6 Conclusions and discussion

In this research we focus on the changes in the productivity ofthe hospital sector from a historical perspective as a functionof policy changes. By applying a time series analysis, we areable to relate productivity changes to different policy regimes.We introduce a cost function model that relates hospital sectorcosts to services delivered, resource prices, and technicalchange. Productivity changes are derived from the parameterestimates and tied to the type of regulation. The model isapplied to the Dutch hospital sector in the period 1972–2010. During this period hospital sector regulation haschanged substantially. Three regulation approaches are distin-guished: the output financing/public-private ownership period(1972–1982), the budgeting/private ownership/capacity regu-lation period (1983–2001) and the competition period (2002–2010). Applying economic theory we formulated the hypoth-eses that productivity change in the budgeting period to behigher than in the output financing period (no incentives), butlower than in the period of competition (strong incentives). Byestimating a time series cost function model it becomes ap-parent that these changes have had an impact on productivitychange. Productivity only increases marginally during the firstperiod (output financing), since no incentives for efficiency orproductivity improvement are included in the system. Oncebudgeting is introduced there is a significant increase in pro-ductivity. The permanent growth of hospital demand and themodest budget increases represent a constant pressure to thesystem, leading to productivity improvements. One cost ofthis productivity improvement is serious rationing of healthcare and longer waiting lists. Contrary to our hypothesis theautonomous productivity change slowed down during thecompetition period. Even though it may appear that competi-tion leads to increased productivity, this result may also beattributed to adaptation lags. During the competition period,production grew fast, but this may be due to the lack ofproduction or budget limitations. Substantial increases in bud-gets under a budget financing regulation would probably haveresulted in the same or better outcome. From our findingspolicy makers cannot claim unequivocally that the competi-tion reform was successful in improving hospital sector pro-ductivity. This is probably due to the fact that they have notbeen able to create a competitive market, but instead a marketwith serious market failures.

Acknowledgments The authors would like to thank Vivian Valdmanisfor her useful comments on an earlier draft of this article. Further wewould like to say thanks to our colleague Alex van Heezik for all hisefforts in collecting the data.

References

1. OECD (2012) Health care expenditure and financing: Total expendi-ture. OECD, Paris

2. OECD (2012) % total expenditure on health by provider. OECD,Paris, First published

3. Blank JLT, Valdmanis VG (2008) Evaluating hospital policy andperformance: Contributions from hospital policy and productivityresearch. Elsevier JAI, Oxford

4. Newhouse JP (1970) Toward a theory of nonprofit institutions: aneconomic model of a hospital. Am Econ Rev 60(1):64–74. doi:10.2307/1807855

5. Niskanen WA (1968) The peculiar economics of bureaucracy. AmEcon Rev 57(2):293–321

6. Mutter RL, Rosko MD (2008) The impact of ownership on the cost-efficiency of U.S. hospitals. In: Blank JLT, Valdmanis VG (eds)Evaluating hospital policy and performance: Contributions fromhospital policy and productivity research, vol 18. Elsevier, Oxford,pp 113–138

7. Mache S, VitzthumK, Nienhaus A, Klapp BF, Groneberg DA (2009)Physicians’ working conditions and job satisfaction: does hospitalownership in Germany make a difference? BMC Health Serv Res9(1):148

8. Eggleston K, Shen Y-C (2011) Soft budget constraints and owner-ship: empirical evidence from US hospitals. Econ Lett 110(1):7–11.doi:10.1016/j.econlet.2010.09.016

9. Farsi M, Filippini M (2008) Effects of ownership, subsidization andteaching activities on hospital costs in Switzerland. Health Econ17(3):335–350

10. Ferrier GD, Valdmanis VG (2008) Efficiency and productive changesin large urban hospitals 1994–2002: Ownership, markets, and theuninsured. In: Blank JLT, Valdmanis VG (eds) Evaluating hospitalpolicy and performance: Contributions from hospital policy andproductivity research, vol 18. Elsevier, Oxford, pp 157–176

11. Herr A, Schmitz H, Augurzky B (2011) Profit efficiency and owner-ship of German hospitals. Health Econ 20(6):660–674

12. Lee K-H, Yang S-B, ChoiM (2009) The association between hospitalownership and technical efficiency in a managed care environment. JMed Syst 33(4):307–315

13. Valdmanis V (1992) Sensitivity analysis for DEA models: an empir-ical example using public vs. NFP hospitals. J Public Econ 48(2):185–205. doi:10.1016/0047-2727(92)90026-C

14. Daidone S, D'Amico F (2009) Technical efficiency, specializationand ownership form: evidences from a pooling of Italian hospitals. JProd Anal 32(3):203–216. doi:10.1007/s11123-009-0137-7

15. Bazzoli GJ (2008) Hospital consolidation and integration activity inthe United States. In: Blank JLT, Valdmanis VG (eds) Evaluatinghospital policy and performance: Contributions from hospital policyand productivity research, vol 18. Elsevier, Oxford, pp 45–61

16. Pauly M, Redisch M (1973) The not-for-profit hospital as a physi-cians’ cooperative. Am Econ Rev 63(1):87–99. doi:10.2307/1803128

17. Blank JLT, Merkies AHQM (2004) Empirical assessment of theeconomic behaviour of Dutch general hospitals. Health Econ 13(3):265–280

18. Farsi M, Filippini M (2006) An analysis of efficiency and productiv-ity in Swiss hospitals. Schweizerische Zeitschrift fur Volkswirtschaftund Statistik/Swiss. J Econ Stat 142(1):1–37

19. Preyra C, PinkG (2006) Scale and scope efficiencies through hospitalconsolidations. J Health Econ 25(6):1049–1068

20. Arocena P, Garcia-Prado A (2007) Accounting for quality in themeasurement of hospital performance: evidence from Costa Rica.Health Econ 16(7):667–685

21. Smet M (2007) Measuring performance in the presence of sto-chastic demand for hospital services: an analysis of Belgian

J.L.T. Blank, E. Eggink

general care hospitals. J Prod Anal 27(1):13–29. doi:10.1007/s11123-006-0021-7

22. Kristensen T, Bogetoft P, Pedersen KM (2010) Potential gains fromhospital mergers in Denmark. Health Care Manag Sci 13(4):334–345. doi:10.1007/s10729-010-9133-8

23. Zhao Y, Foley M, Eagar K (2011) Assessing economies of scale inpublic hospitals. Econ Pap 30(3):341–347

24. Besstremyannaya G (2012) The impact of Japanese hospital financ-ing reform on hospital efficiency: a difference-in-difference ap-proach. Jpn Econ Rev. doi:10.1111/j.1468-5876.2012.00585.x

25. Sari N (2008) Competition and market concentration. In: Blank JLT,Valdmanis VG (eds) Evaluating hospital policy and performance:Contributions from hospital policy and productivity research, vol18. Advances in health economics and health services research.Elsevier, Oxford, pp 139–156

26. Chua CL, Palangkaraya A, Yong J (2011) Hospital competition,technical efficiency and quality. Econ Rec 87(277):252–268

27. Chu C-L, Chiang T-L, Chang R-E (2011) Hospital competition andinpatient services efficiency in Taiwan: a longitudinal study. HealthEcon 20(10):1268–1280

28. de Castro Lobo MS, Ozcan YA, da Silva ACM, Lins MPE, FiszmanR (2010) Financing reform and productivity change in Brazilianteaching hospitals: Malmquist approach. CEJOR 18(2):141–152.doi:10.1007/s10100-009-0097-z

29. Gaynor M, Voigt WB (2000) Antitrust and competition inhealth care markets. In: Schmalensee R, Willig RD (eds)The handbook of industrial organization. vol third impression.Elsevier, Amsterdam

30. Sommersguter-Reichmann M (2000) The impact of the Austrianhospital financing reform on hospital productivity: empirical evi-dence on efficiency and technology changes using a non-parametricinput-based Malmquist approach. Health Care Manag Sci 3(4):309–321

31. Biørn E, Hagen TP, Iversen T, Magnussen J (2003) The effect ofactivity-based financing on hospital efficiency: a panel data analysisof DEA efficiency scores 1992–2000. Health Care Manag Sci 6(4):271–283. doi:10.1023/a:1026212820367

32. Douven R, Mocking R, Mosca I (2012) The effect of physician feesand density differences on regional variation in hospital treatments.Centraal Planbureau, Den Haag

33. Jonker J-J (2012) Countries compared on public performance: Astudy of public sector performance in 28 countries. Centraal enCultureel Planbureau, Den Haag

34. MeinenG, Verbiest P, DeWolf P (1998) Perpetual InventoryMethod:Service lives, discard patterns and depreciation methods. CentraalBureau voor de Statistiek, Den Haag

35. Fried HO, Lovell CAK, Schmidt SS (2008) The measurement ofproductive efficiency and productivity growth. Oxford UniversityPress, New York

36. Nicholson W, Snyder C (2008) Microeconomic theory: Basic prin-ciples and extensions, 10th edn. Thompson and South-Western,Sydney

37. Shephard RW (1970) Theory of cost and production functions.Princeton University Press, Princeton

38. Christensen LR, Jorgenson DW, Lau LJ (1973) Transcendental log-arithmic production frontiers. Rev Econ Stat 55(1):28–45

The impact of policy on hospital productivity