Pharmaceutical expenditure, total health-care expenditure and GDP

HEALTH ECONOMICSHealth Econ. 17: 1187–1206 (2008)Published online 29 November 2007 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/hec.1317

PHARMACEUTICAL EXPENDITURE, TOTAL HEALTH-CAREEXPENDITURE AND GDP

JESUS CLEMENTEa,*, CARMEN MARCUELLOb and ANTONIO MONTANESa

aDepartment of Economic Analysis, University of Zaragoza, Zaragoza, SpainbDepartment of Business Organisation, University of Zaragoza, Zaragoza, Spain

SUMMARY

This paper analyses the evolution of pharmaceutical expenditure with respect to GDP for a group of the mostimportant OECD economies. We find that this relationship is not stable across the sample considered (1960–2003),and heterogeneity is found in the temporal evolution of the variables and across countries. Furthermore, we can seedifferences in the income elasticity estimation when we disaggregate pharmaceutical expenditure into its private andgovernment components or when the total health-care expenditure (Total HCE) is disaggregated into itspharmaceutical and non-pharmaceutical components. We conclude that the changes in the elasticity ofpharmaceutical expenditure and in the Total HCE elasticity are due to the private component and thepharmaceutical expenditure behaviour, respectively. Copyright # 2007 John Wiley & Sons, Ltd.

Received 19 September 2005; Revised 11 October 2007; Accepted 12 October 2007

JEL classification: I10; I11; I20; C11; C22; C23

KEY WORDS: pharmaceutical expenditure; total health-care expenditure; gross domestic product; elasticity;cointegration and structural breaks

INTRODUCTION

Pharmaceutical expenditure is one of the main components of total health-care expenditure (TotalHCE). We can understand its importance if we consider the following questions. First, pharmaceuticalexpenditure has specific characteristics associated with the high level of concentration in thepharmaceutical sector, the importance of RþD activities in this sector and its simultaneousconsideration as a consumer item and as an investment in human capital (reduction in working days lostto illness, higher productivity achieved by healthier workers and so on). Second, we would underline theimportance that is being given to policies aimed at reducing this expenditure. An example is theannouncement of a plan to reduce government pharmaceutical expenditure put forward by the SpanishMinistry of Health in 2005. Almost certainly, the idea is to transfer the expenditure from thegovernment administration to the citizen, thereby externalizing the expenditure and improving the totalbudget of the Ministry.

This policy is understandable from the point of view of a developed economy with increasing incomethat allows households to take on the burden of this expenditure without causing excessive financialhardship, but it contradicts the traditional concept of universal healthcare in European economies.From this perspective, government health services can be considered as a public commodity, availableto all members of society. As a consequence, government health budget deficits should be resolved bybetter management of existing expenditure or, if necessary, by increasing the budget allocation.

*Correspondence to: Gran Vıa, 2, 50005, Zaragoza, Spain. E-mail: [email protected]

Copyright # 2007 John Wiley & Sons, Ltd.

This debate will undoubtedly continue and will not only include pharmaceutical expenditure but alsoall components that make up Total HCE. We, therefore, believe that an analysis of the aggregatedpharmaceutical expenditure is necessary in order to provide information so that policymakers maymake better informed and more efficient decisions.

Against this background, we first examine the behaviour of pharmaceutical expenditure relative tothe other components of Total HCE and its private–government composition. Second, we analyse thepresence or otherwise of a similar structure in terms of the level of total pharmaceutical expenditure, fora group of the most important OECD economies. This is particularly relevant, given the fact that thereis considerable variance in different health-care systems and, as a consequence, in the responses ofgovernments and citizens to changes in the economic situation for each country (see Clemente et al.,2004). Finally, if we can provide evidence in favour of pharmaceutical expenditure possessing specificcharacteristics within the components of Total HCE, it is worth examining its relationship with incomeor GDP. It is also of interest to compare, at international level, its components, government and privatepharmaceutical expenditure. We would point out that while government pharmaceutical expenditure isbased on political decisions related to issues such as budget deficits and the health-care model, privatepharmaceutical expenditure can be partially considered as a response to changes in the governmentcomponent. Furthermore, if we consider, initially, private pharmaceutical expenditure as a luxurycommodity, an increase in family income would mean a more than proportional increase in thisexpenditure. If growth continues, consumers may demonstrate two types of behaviour that haveopposite effects on pharmaceutical expenditure. Firstly, if families consider that they have alreadycovered their pharmaceutical needs, its characteristic as a luxury commodity will be lessened. Secondly,if the other vital necessities have been covered, they will spend an increasing percentage of income onpharmaceutical products in order to increase life expectancy. Consequently, income elasticity couldincrease or decrease depending on the relative importance of these two effects.

The empirical approximation to the question of the influence of GDP on Total HCE, in general, andon pharmaceutical expenditure, in particular, is not a simple matter as there are very few empirical ortheoretical references available. This study is an attempt to start filling that gap. The structure of thispaper is as follows: Section 2 contains a review of the literature as well as a description ofpharmaceutical expenditure from an international perspective. Section 3 presents the methodology thatwe use and the results obtained. Finally, Section 4 concludes and suggests some possibilities for futureresearch.

PHARMACEUTICAL EXPENDITURE: REVISION OF THE LITERATURE ANDDESCRIPTIVE ANALYSIS

Given the importance of pharmaceutical expenditure in the OECD economies, it is useful to commenton some papers related to this topic and to analyse some descriptive facts related to this variable. In thissection, we review the literature and we detect some gaps that this paper tries to fill. Afterwards, wejustify our point of view showing the behaviour of this expenditure in some OECD countries.

Literature review

Pharmaceutical expenditure is generally considered a relevant component of Total HCE and itsevolution has tended towards deficit in health-care budgets. Consequently, different papers have beendevoted to analysing this expenditure from different points of view, such as aggregate evolution,determinants of pharmaceutical expenditure, regional analysis and methods of expenditure control. Allof them are concerned with the increase of pharmaceutical spending. Our perspective is the first, but wewould like to offer a non-extensive review of all of them.

J. CLEMENTE ET AL.1188

Copyright # 2007 John Wiley & Sons, Ltd. Health Econ. 17: 1187–1206 (2008)

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From an aggregate point of view, some papers focus their analysis on the estimation of the incomeelasticity of pharmaceutical expenditure. For example, Alexander et al. (1994) find that the incomeelasticity is greater than one when the OECD countries are considered as a homogeneous group.However, Okunade and Suraratdecha (2006), using a very different empirical methodology thatincludes ‘expenditure inertia’ based on habits (lagged adjustment) and a Box-Cox specification wherethe functional empirical specification is determined in the estimation process, find that drug expenditurebehaves as a luxury commodity in some countries and as a necessity in others and that the empiricalmodel is different across countries. Finally, Karatzas (2000), using a cointegration approach, examinesdifferent health subcategories in the USA, including pharmaceutical expenditure, and the influence ofincome, health stock and demographic variables. This paper found that these variables are affected indifferent ways across subcategories.

However, these papers do not take into account the possible presence of structural breaks1 and do notconsider private and public expenditure separately, which is the approach followed in this paper.

Another group of studies has analysed the determinants of pharmaceutical expenditure usingdescriptive statistics. These papers are focused on a particular country and, consequently, cannot offeran international comparison. Dazon and Pauly (2002) find a positive effect of the changes of coverageand age groups during the nineties on pharmaceutical spending growth for the USA case. Similarly,Kildemoes et al. (2006), for Denmark, analyse the relationship between age and public pharmaceuticalexpenditure and propose that an efficient policy has to focus on rational pharmacotherapy and cost-effective pharmaceuticals more than on drug use among the elderly. They conclude that the aging of thepopulation is important for the evolution of pharmaceutical expenditure, but it is not the only factor.Cremiuex et al. (2005), for Canada, examine the relationship between pharmaceutical spending andoverall health outcomes and detect a positive relationship.

Additionally, there are some papers that study the determinants of the pharmaceutical expenditure ina single country from a demand perspective or from the perspective of the finance system of thisexpenditure. Herriksson et al. (1999) look at pharmaceutical expenditure in Sweden and analyse itscomponents and the methods of public control – centralized or decentralized – and the reference pricesystem. Huttin (2000), using the National Medical Expenditure Survey, studies the determinants ofhousehold pharmaceutical expenditure in the USA at a regional level and in relation to the income andindividual characteristics of the population and finds that the income elasticity depends on geographicalvariables. Finally, some papers, such as Lopez-Casasnovas (2005) for the Spanish case, analyse theeffects of the policies focused on the constraint of this expenditure.

Furthermore, there have been a number of important studies on the economy of health care thatcompare the efficiency of government and private health expenditure. Buchanan (1965) argued thatTotal HCE would be better managed by government than regulated by market rules, while Niskanen(1971) suggested that increases in government health-care expenditure are due to a lack of incentives tomake bureaucrats more efficient. Newhouse (1977) concluded that Total HCE elasticity with respect toGDP is greater than one, and we therefore find ourselves with a service that behaves as a luxurycommodity.

To sum up, these papers are focused on explaining the increase of pharmaceutical expenditure. Fromour perspective, we think that, simultaneously with the study of determinants and behaviour, it isnecessary to develop suitable international temporal analyses to understand the evolution ofpharmaceutical expenditure in OECD economies, a question that is important when a health policyhas to be implemented. In fact, it is difficult to determine these effects if we do not know, for example,the responses of households to policy implementation, in other words, how private health expenditure isgoing to react to changes in government pharmaceutical expenditure policy. In this paper we explore

1See Clemente et al. (2004).

PHARMACEUTICAL EXPENDITURE, TOTAL HEALTH-CARE EXPENDITURE AND GDP 1189


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three new directions related to this idea. Firstly, we carry out a new aggregate analysis of thiscomponent of health expenditure. Secondly, we consider that government and private pharmaceuticalexpenditure depends on different determinants and, consequently, we analyse whether they share thesame income elasticity, a topic that has not been taken into account by the previous literature and whichcan help policymakers. And finally, we analyse the differences between the income elasticity ofpharmaceutical and non-pharmaceutical expenditure because the proliferation of new medicalcommodities might indicate a change in the demand for this kind of product. Our perspective isinternational and we analyse the existence of changes in the response of this expenditure to GDPthrough the estimating of income elasticity. We find that there is no homogeneity in the context ofOECD countries.

Descriptive analysis

This section is devoted to analyzing pharmaceutical expenditure from a descriptive point of view. Wecarry out this analysis from three different perspectives that could help us to cast some light on some ofthe pre-existing beliefs in the literature on pharmaceutical expenditure and Total HCE growth. The firstperspective is the temporal evolution of these variables. The second considers the measurement of totalpharmaceutical expenditure as a percentage of Total HCE or of GDP, which is an indicator of theintensity with which the health-care system itself distributes a part of its resources on pharmaceuticalexpenditure. Finally, the third perspective covers the study of the evolution of pharmaceuticalexpenditure with respect to its government or private elements. We should note that the design of thehealth-care system determines the relative importance of one type of expenditure or another and,furthermore, that their determinants are different; government expenditure is related to politicalconsiderations while private expenditure is a consequence of the utility maximization process ofhousehold decisions.

In the rest of this section, we analyse these three perspectives for three representative countries. Wehave chosen two European countries, France and Finland, with different systems for financing healthcare (tax and insurance, respectively) and a non-European country, the USA. In our view, they reflectthe common behaviours of the countries used in the empirical section. Additionally, when the ratiohealth expenditure/GDP is considered,2 two new countries (Spain and Iceland) are included becausethey show a different evolution. Figures 1–3 show total pharmaceutical expenditure and private andgovernment pharmaceutical expenditure as a percentage of Total HCE, and Figures 4–6 reflect thepercentage of total, government and private pharmaceutical expenditure over GDP.

0%

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1960196119621963196419651966196719681969197019711972197319741975197619771978197919801981198219831984198519861987198819891990199119921993199419951996199719981999200020012002

USA Finland France

Figure 1. Total pharmaceutical expenditure as percentage of Total HCE

2We use the 2005 OECD Health Database for the descriptive analysis.



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0%

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1960196119621963196419651966196719681969197019711972197319741975197619771978197919801981198219831984198519861987198819891990199119921993199419951996199719981999200020012002

USA Finland France

Figure 2. Private pharmaceutical expenditure as percentage of Total HCE

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Figure 3. Government pharmaceutical expenditure as percentage of Total HCE

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Figure 4. Total pharmaceutical expenditure as percentage of GDP

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USA Finland France Iceland

Figure 5. Private pharmaceutical expenditure as percentage of GDP



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If we begin the analysis by considering the USA case, we observe the reduced importance ofpharmaceutical expenditure over the Total HCE (Figure 1). Additionally, government pharmaceuticalexpenditure is less than private expenditure (Figures 2 and 3). Consequently, it comes as no surprise thattotal and private pharmaceutical expenditures share the same temporal evolution. It is also of interest tonote that the proportion of total pharmaceutical expenditures in relation to Total HCE dropscontinuously from the beginning of the sample to the late 1970s. Later it stabilizes and, from the middleof the 1990s, increases slightly. If we now turn to the study of the private pharmaceutical expenditure/GDP ratio in Figure 6, we observe that its value is constant until 1990, and grows from then on. Thisbehaviour is replicated by the total and government pharmaceutical expenditure ratios (Figures 4 and 5).This might be evidence in favour of accepting that pharmaceutical expenditure behaves as a firstnecessity commodity at the beginning of the sample and as a luxury at the end.

If we consider the case of France, the picture changes. For example, the first period of decrease oftotal pharmaceutical expenditure begins later than in the USA case and ends later. After this,expenditure grows slightly until the late 1990s when growth is stronger. This behaviour is very differentin private and government pharmaceutical expenditures (Figures 2 and 3): while private expendituretends to be constant and the percentage is stable at around 8%, government expenditure moves jointlywith the Total HCE. With respect to the ratio pharmaceutical expenditure/GDP, the graphical analysisshows a similar behaviour as the USA case, but the importance of private expenditure is lower and thatof government expenditure is higher.

Finally, the Finnish case shows differences because the ratio government pharmaceutical expenditure/Total HCE (Figure 1) grows and the private pharmaceutical expenditure/Total HCE (Figure 2) isconstant. If we look at the pharmaceutical expenditure/GDP ratios (Figures 4–6), we find that totalpharmaceutical expenditure was a constant fraction of the GDP until the late 1980s, and later increasesfrom 0.5 to 1%. The behaviour of private pharmaceutical expenditure is different because it decreases inthe early years and is constant after the 1980s. Finally, the government pharmaceutical expendituregrows throughout the sample.

The descriptive analysis of these three countries is a good summary of the behaviour observed in animportant number of the countries in our sample. However, we find some examples with moreheterogeneity. As an example, we have included the ratio private pharmaceutical expenditure/GDP forIceland in Figure 5, where non-continuous changes are observed, and the government pharmaceuticalexpenditure/GDP for Spain (Figure 6) that shows an initially important growth until 1973, a decreasefrom 1974 to the late 1980s and a new growth period in recent years.

From this descriptive perspective, we may conclude that the behaviour of total pharmaceuticalexpenditure in relation to Total HCE and to the GDP is not homogeneous in that of the countriesexamined. It is easy to find differences, both in the total evolution and in the individual components.This conclusion means that the idea that the growth of total pharmaceutical expenditure is higher than

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1960196119621963196419651966196719681969197019711972197319741975197619771978197919801981198219831984198519861987198819891990199119921993199419951996199719981999200020012002

USA Finland France Spain

Figure 6. Government pharmaceutical expenditure as percentage of GDP



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Total HCE (Lopez-Casanovas, 2004) holds only for specific countries and/or for specific periods oftime. In fact, we are not able to confirm that pharmaceutical expenditures have represented a growingproportion of Total HCE continuously. This phenomenon is observed in the last years of the sample forsome countries, but others, such as Finland, show a constant ratio.

This previous analysis clearly encourages research to discover a logical explanation for this behaviourand, undoubtedly, a database representing the evolution of the different components of Total HCEwould be very useful. We consider that our study would help other researchers interested in discoveringwhether the level of competition in the sector has increased or decreased, given that a process ofindustrial concentration that has made the market an oligopoly might explain some facts of theevolution that we have observed.

Another interesting point would be to explore whether there is demand for certain non-pharmaceutical health services that might have reached a threshold with the result that expenditureis devoted to other goods and services, such as pharmaceutical expenditure, which do not appear tohave any limit. As we pointed out in our introduction, pharmaceutical goods possess certaincharacteristics of consumer goods and the current tendency of individuals is the desire to possess moreand more of the product. Moreover, the introduction into the market of an important quantity ofpharmaceutical products related to aesthetic appearance, dieting or similar, might have had the effect ofchanging the concept of medication and, therefore, statistics from previous years are not comparablewith statistics on current health-care products.

Finally, we would like to point out that the way Total HCE is financed and/or belongs to asupranational economy, such as the European Union, is also a factor to be taken into account. Forinstance, the financing method could lead to bottlenecks in the system that might result in thegovernment having to make drastic cuts in pharmaceutical expenditure. In the same way, processes ofeconomic integration lead to convergence processes in budget policies that can lead to simultaneousassociated cuts in pharmaceutical expenditure.

In any event, we believe that this is an ongoing debate that goes beyond a partial view of the problem.When we speak of partial views, we are referring to the consideration of isolated countries where asuccession of measures based on differing ideologies could distort behaviour in the long term. We mustalso consider that private and government pharmaceutical expenditure are conditioned by differentfactors and are the results of decisions by different agents with very different motives.

PHARMACEUTICAL EXPENDITURE ANALYSIS: DATA, METHODOLOGY AND RESULTS

The aim of this section is the analysis of the evolution of pharmaceutical expenditure in OECDcountries. In particular, we are very interested in studying the relationship of this variable to changes inthe GDP of the OECD countries and in observing whether it shows a different pattern of behaviourwhen it is compared with that of Total HCE or when it is disaggregated into its public and privatecomponents. Prior to this analysis, we consider it of interest to introduce the database that we haveemployed. Later, we will present the econometric tools that we have used and, finally, discuss the resultsthat we have obtained from their application.

The database

The variables that we will use in our study are Total HCE, total pharmaceutical, total non-pharmaceutical, government pharmaceutical, and private pharmaceutical expenditures and GDP, theirvalues being supplied by the 2005 OECD Health Database. All of them are measured in per capita termsand in US$ PPP for reasons of comparability. The countries included in our sample are Australia,Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, the



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Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, the United Kingdom andthe USA.

The annual data should cover the period 1960–2003. However, the complete series for pharmaceuticaldata is only available for Canada, Finland, Iceland and the USA. For the other countries, the seriesexhibit numerous gaps.3 For this reason, it is extremely difficult to carry out a time series analysisbecause the dynamics of the series cannot be observed. We have, therefore, decided to fill all the gapsthat we have found in the database, treating all these values as missing values. We have followed themissing values treatment developed by the SEATS/TRAMO procedure,4 which is commonly employedto remove the distortion caused by the presence of outliers/missing values, although this is not the onlyoption. Nielsen (2004) has recently shown that this method works properly in a multivariate scenario,such as this case, and, even more significantly, that the particular method for removing the effect of themissing values is not as important as the strategy of filtering the data prior to the cointegration analysis.According to these results, we could have interpolated the missing values using a different filter (linearor non-linear) and the final results would be qualitatively unaltered, but we have chosen the SEATS/TRAMO because its results are widely accepted in the scientific community. In any event, we shouldpoint out that we have only used this method for interpolating the missing values and that we have notemployed it to predict data when they are not available at the beginning or at the end of the 1960–2003sample size. Thus, the beginning and the end of the sample size differ across countries.

The next step in the analysis is to decide the most appropriate econometric methodology to employ.In order to answer this question, we should first be aware of the time properties of the series. This is thegoal of the next subsection.

Time series properties of the variables

This subsection analyses the time series properties of our data set, especially with respect to determiningthe integration order of our variables. To this end, we could follow a single approach and apply theDickey–Fuller family of statistics (Dickey and Fuller, 1979) in order to test for the unit root nullhypothesis. However, given the characteristics of our sample, which can be considered as fairlyheterogeneous, we have chosen to focus the analysis from a panel data perspective.5 This approach ismore efficient than the use of the standard unit root tests, because it offers better size and powerproperties in scenarios such as those employed in this paper.

We can extend the traditional Dickey–Fuller specification in such a way that it adopts a panel dataversion:

Dyit ¼ aiyit�1 þ Z0itdi þXpii¼1

fiDyit�1 þ uit ð1Þ

where i ¼ 1; 2; . . . ;N; y is the variable and ai ¼ ri � 1: The parameter ai contains information about theautoregressive parameter ðriÞ that can vary between the different cross-sections of the sample, while Zit

reflects the deterministic elements. Given the characteristics of the variables included in our data set, allthe unit root statistics will be obtained from a specification that includes an intercept and a deterministictrend, except when testing for the Ið2Þ null hypotheses, in which case the specification only includes anintercept.

One statistic that can be employed is that proposed in Im et al. (2003) (IPS statistic), which allows usto test for the null hypothesis H0: ai ¼ 0 for i ¼ 1; 2; . . . ;N; against the alternative hypothesis of HA:

3See the Appendix for a more detailed description of the sample size employed.4This program can be freely downloaded from http://www.bde.es/servicio/software/econome.htm. See Gomez and Maravall(1994, 1996) for more details on the use of this program.

5We should note that the techniques that we will employ throughout this paper are more related to pooled regressions than to purepanel data analysis, as would be the case of the consideration of fixed effects, for example.



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ai ¼ 0 for i ¼ 1; 2; . . . ;N1 and ai50 for i ¼ N1 þ 1; N1 þ 2; . . . ;N: The starting point for the IPSstatistic is the Dickey–Fuller test estimation, increased by each of the N cross-sections. Let us denote thestandard Dickey–Fuller statistics by tiðpiÞ; where i ¼ 1; 2; . . . ;N and pi represents the number of lagsused in each equation.6 Im et al. (2003) propose, as the testing formula, the group mean of Dickey–Fuller statistics of the single series, which can be denoted as z: If no lags are included in the estimation ofthe Dickey–Fuller-type equations, then the critical values of this statistic are tabulated in the originalpaper for the different N and T values. Nevertheless, if the perturbations of the previous equationspresent some form of autocorrelation, these critical values are not valid. To resolve this problem, Imet al. (2003) offer an alternative, putting forward the following formula:

IPS ¼

ffiffiffiffiN

p½z�N�1

PNi¼1 EðtiðpiÞÞ�ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

N�1PN

i¼1 VarðtiðpiÞÞq ð2Þ

where the expressions for the mean and variance of tiðpiÞ are calculated in Im et al. (2003) for diversevalues of N and T : This statistic converges towards an Nð0; 1Þ distribution; hence, the critical values ofthis distribution may be used.

The results that we have obtained are reported in Table I.7 It can be seen that we cannot rejectthe unit root hypothesis in any of the cases considered, except when the Ið2Þ hypotheses is testedversus the Ið1Þ hypothesis. However, we should note that, as is pointed out in some earlier works,8

the existence of a certain degree of cross-sectional correlation in the perturbations of the model can leadto size distortions and low power. Consequently, it seems to be appropriate to check for the presence ofthis type of correlation. To this end, we can employ the CD statistic recently proposed in Pesaran(2004). This statistic is based on the average of the pairwise correlation coefficients of the OLS residuals

Table I. Testing for panel unit roots

TPE GPE PPE THCE TNPE GDP

Testing for the null hypothesis Ið1Þ versus Ið0ÞIPSGross database 13.69 13.74 3.20 12.29 6.23 2.04Cross-sectional correlation corrected 3.71 2.55 5.26 3.88 3.43 4.24CIPS �1.99 �1.83 �1.80 �1.78 �1.73 �1.43

Testing for the null hypothesis Ið2Þ versus Ið1ÞIPSGross database �2.81a �6.60a �10.70a �3.23a �5.02a �22.11a

Cross-sectional correlation corrected �12.67a �16.18a �7.40a �10.22a �11.41a �15.31a

CIPS �2.98a �3.30a �3.21a �3.40a �3.06a �2.66a

This table reports the statistics for testing the panel data unit root null hypothesis. IPS represents the Im et al. (2003) statistic,while CIPS is the IPS version of the test proposed in Pesaran (2006), both of them calculated from the use of an ADF-type statistic.The specification includes an intercept and a deterministic trend when testing for the Ið1Þ null hypothesis, while it only considers anintercept when the null hypothesis is Ið2Þ: TPE, total pharmaceutical expenditure; GPE, government pharmaceutical expenditure;PPE, private pharmaceutical expenditure; THCE, total health-care expenditure; TNPE, total non-pharmaceutical expenditure.aThis means the rejection of the null hypothesis for a given 1% significance level.

6The value of this parameter has been selected using the Schwarz BIC criterion. However, the use of alternative methods, such asthe Akaike AIC or the modified criterion proposed in Ng and Perron (2001), does not alter the conclusions.

7We have also considered the Phillips–Perron version of the panel data unit root tests. Given that its use does not alter theconclusions drawn, we have opted to omit the results.

8See Banerjee et al. (2004) and Breitung and Pesaran (2005).



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from the individual regressions in the panel and may be defined as follows:

CD ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2T

NðN � 1Þ

s XN�1i¼1

XNj¼iþ1

#rij ð3Þ

where #rij represents the pairwise correlation coefficient between the OLS residuals of models i and j:This statistic asymptotically converges towards an Nð0; 1Þ distribution. The values of this statistic formodel (1), where the deterministic elements are an intercept and a deterministic trend and the value ofthe parameter ri is equal to 1, are 2.06, 1.97, 0.25, 14.31 and 3.96, respectively, for the totalpharmaceutical expenditure, government pharmaceutical expenditure, private pharmaceutical expen-diture, Total HCE and total non-pharmaceutical expenditure.9 Thus, we can reject the non-cross-sectional dependence null hypothesis for a 5% significance level, except in the case of the privatepharmaceutical expenditure.10 The presence of this cross-sectional dependence can be understood if webear in mind that most of the countries included in our sample belong to the European Union, whereefforts have recently been made towards the harmonization of the different health policies.

The rejection of the non-cross-sectional dependence null hypothesis implies that we should take thisdependence into account when we test the unit root null hypothesis. To this end, we can adopt severalstrategies. A first option is to subtract the cross-sectional mean from our pool of data and re-calculatethe IPS statistics. This method, which is equivalent to admitting the presence of time effects in the panel,has been followed by Levin et al. (2002) and Im et al. (2003). We should note that this strategy workscorrectly only in those cases where the cross-sectional dependence is common and constant across all thecountries, which is not a non-trivial assumption.11 Thus, it is possible that this method does noteliminate all the cross-sectional correlation, although it can reduce it considerably, as is shown inLuintel (2001). Consequently, it seems to be appropriate to complement the use of these statistics withalternative statistics that account for the presence of cross-sectional dependence more adequately, as isthe case of the so-called second generation panel data unit root tests. In this case, we consider that thestatistic defined in Pesaran (2006) should work correctly, according to the Monte Carlo methods carriedout in that paper.

This statistic is based on the cross-sectional augmented version of model (1):

Dyit ¼ ai þ aiyit�1 þ ci %yt�1 þXpj¼0

dijD%yt�j þXpi¼1

dijDyit�1 þ uit ð4Þ

where %yt is the cross-sectional mean of yit: If we denote as tiðN;TÞ the t-ratio for testing the nullhypothesis H0: ai ¼ 0 in (4), then the generalization of the IPS statistic defined in Pesaran (2006) can berepresented as

CIPSðN;TÞ ¼ N�1XNi¼1

tiðN;TÞ ð5Þ

We should note, however, that the performance of these second generation panel data unit root tests forsmall, unbalanced samples such as ours is not known. In these circumstances, we have chosen to follow

9These values have been obtained from the application of the unbalanced version of the CD test, as defined in Pesaran (2004, p.17).

10This lack of evidence against the non-cross-sectional dependence null hypothesis should be interpreted with some cautionbecause, on the one hand, the CD test may exhibit small power for panel data such as that employed in this paper and, on theother hand, the use of alternative tests, such as the LM statistics proposed in Breusch and Pagan (1980), lead us to reject thishypothesis.

11We should note that this strategy can correctly take into account the presence of cross-sectional dependence whenever thisaffects all the countries ðNÞ in a similar way. If we consider the evident connections between the economies of the countriesincluded in the sample (most of them belong to the EU) this assumption does not seem to be inappropriate. Thus, we considerthat the procedure employed here is a reasonable approach.



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the first approach by re-calculating the IPS and the Fisher statistics once the cross-sectional mean hasbeen subtracted from the original data, in combination with the use of the statistic proposed in Pesaran(2006).

The results obtained with the new statistics do not modify the conclusion previously drawn and wecan again reject the existence of two unit roots in the pool of data, while we cannot reject the presence ofa single unit root. Consequently, we conclude that all the variables included in our data set are Ið1Þ: Thisfact has direct consequences on the estimation method to be applied, because it renders the use oftraditional methods unsuitable. Instead, we base our analysis on the use of cointegration techniques,which are presented in the next subsection.

Estimation of long-run relationships: panel data cointegration analysis

After coming to the conclusion that the variables considered in this study are Ið1Þ; the next question tobe examined is the existence of a cointegration or long-run structural relationship between the differentmeasures of pharmaceutical and health expenditure and the GDP of a particular country. Theestimation of this type of specification will enable us to obtain the income elasticity of each health-careexpenditure component. The initial model specification can be stated as follows:

log HEit ¼ b1i þ b2i log GDPi

t þ uit ð6Þ

where the endogenous variable is the logarithm of the different levels of pharmaceutical and healthexpenditure of country i in period t; while the GDP of the ith country in period t is the explanatoryvariable.

In order to confirm that this specification represents a valid long-run economic relationship, we testthe non-cointegration null hypothesis by analysing the presence of a unit root in the perturbation of thepreceding model. Again, we have adopted a panel data perspective because of the heterogeneous natureof our data sample. There is a relatively wide range of statistics available, although we prefer the testdefined in Pedroni (1999, 2004), which can be considered the panel data extension of the Engle–Grangerstatistic. Following this author, the statistics that we have used can be defined as

N�1=2 eZtN;T �YffiffiffiffiN

pffiffiffiffiC

p ) Nð0; 1Þ ð7Þ

where eZtN;T is the mean group t-type statistic for testing the null hypothesis that the perturbation of theith model exhibits a unit root. We should recall that we can follow a parametric or a non-parametricapproach in order to take into account the possible existence of autocorrelation in these perturbations.In the first case, we calculate the statistics of the Dickey–Fuller family, while the Phillips–Perronstatistics is obtained in the second (Phillips and Perron, 1988). The values of the parameters Y and Cdepend on the number of explanatory variables included in the model specification. Pedroni (2004)reports their asymptotic approximations in Corollary 1.

However, it is possible that the Pedroni specification is not enough to capture the relationshipbetween our pharmaceutical/health variables and GDP. Here, we are thinking in terms of the existenceof possible changes in the parameters of this model. For example, Clemente et al. (2004) offer evidenceagainst the no structural break hypothesis when the relationship between Total HCE and GDP isanalysed. Thus, it seems to be sensible to admit the possible existence of a break in model (6). We cantest the structural break hypothesis by adding some dummy variables to the previous specification,which can be finally stated as follows:


t þ b3iDit þ uit ð8Þ

when only one change in the intercept is considered or, more generally,


t þ b3iDit þ b4iðD

it log GDPi

tÞ þ uit ð9Þ



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when both the intercept and the slope change. In the previous equations, Dit means a dummy variable

that takes the value 1 for the ith variable when t > TBi; with TBi being the period when the breakchanges for the ith variable.

In a recent paper, Banerjee and Carrion (2006) have defined a new family of panel data cointegrationstatistics that test for the non-cointegration null hypothesis when the specification modeladmits the presence of a structural change, as is the case of models (8) and (9). Their proposal isbased on an extension of the statistics designed in Gregory and Hansen (1996) for single seriesto the case of panel data. Banerjee and Carrion (2006) propose a number of differentstatistics, following the papers of Pedroni (1999, 2004). However, we will use only those statisticsthat are based on mean group t-statistics, which can be defined in a similar way as that reported in (7),although the asymptotic values of parameters Y and C change and are reported in Table IV of Banerjeeand Carrion (2006).

Finally, we should again take into account the possible presence of some forms of cross-sectional dependence. In order to test for the non cross-sectional dependence null hypothesis, we havecalculated the CD test. The values of this statistic when model (7) is estimated are 17.61, 7.73,14.26, 13.32 and 9.57 for the total pharmaceutical expenditure, the government pharmaceuticalexpenditure, the private pharmaceutical expenditure, the total health expenditure and the totalnon-pharmaceutical expenditure, respectively. Consequently, there is a great amount of evidenceagainst this null hypothesis and we should take this into account in order to test for the non-cointegration null hypothesis. To do this, we could follow a similar strategy as that used for testingfor the unit root null hypothesis. However, we find serious difficulties in now employing a secondgeneration panel cointegration test that works correctly in a small sample size scenario. In this regard,we should note that Banerjee and Carrion (2006) propose a second generation test based onthe estimation of the unobserved common factor of the model. Of course, this is a very appealingstatistic to be employed here. However, we should note that this method works properly for reasonablyhigh sample sizes. In our paper the common sample sizes are very small for most of the variablesemployed, as can be understood by looking at the Appendix. This limits the reliability of the resultsobtained from the estimation of the unobserved common factor. Consequently, in the absence of abetter approach, we have opted to calculate the non-cointegration statistics for the variables measuredin differences with respect to the cross-sectional mean, instead of using second generation panel datastatistics.12

The results that we have obtained are presented in Tables II–V. These tables report the differentstatistics that we have employed for testing the non-cointegration hypothesis, as well as the estimationof the most appropriate model that captures the relationship between our different Pharmaceutical/health variables and GDP. Column EG reports the value of the Engle–Granger statistic for each singleseries (Engle and Granger, 1987). Similarly, GH-I and GH-III reflect the Gregory–Hansen statistics fortesting the non-cointegration null hypothesis for the level shift and for the changing slope cases,respectively, with TB1 being the estimation of the period where the break occurs when the change onlyaffects the intercept and TB2 when both the intercept and the slope vary. b1–b4 represent the fullymodified estimations of the parameters of model, with their respective standard errors, which serve toselect the best specification, reported in brackets.13 Finally, the group mean t-statistics are the Pedroniand the Banerjee–Carrion statistics for testing the panel data unit root null hypothesis, with the valuesin italics having been obtained after removing the cross-sectional mean in order to take into account thepossible correlation of the perturbations.

12We should note that this is the strategy adopted in, for example, Pedroni (2004).13The estimation of parameter b4 is not reported in those cases where the selected specification only considers a change in theintercept.



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Empirical results

The results obtained are presented in Tables II–V. The first important result that emerges is that thegroup mean t-statistic enables us to reject the non-cointegration hypothesis whenever a structural breakis considered in the empirical specification. Thus, if we take into account the presence of these changesin the parameters of the model, we may admit the existence of a long-term relationship between ourdifferent Pharmaceutical/Health variables and GDP. We should note, however, that the sample size isnot the same for all the series. Thus, the comparison of the estimation of the break points should bemade with some caution.

Once evidence in favour of the existence of the long-run relationship is found, the comparison of theestimation of the different models can be carried out from two perspectives, namely, the analysis of thebehaviour of the total pharmaceutical expenditure and its components, governmental and private, andthe study of the behaviour of Total HCE versus total pharmaceutical and non-pharmaceuticalexpenditure.

Table II. Total pharmaceutical expenditure

Countries EG GH-I TB1 GH-III TB2 b1 b2 b3 b4

Countries with insured finance systemAustralia 0.62 �2.39 1993 �5.45 1984 �1.75 (0.18) �14.38 (0.55) 0.63 (0.02) 1.52 (0.06)Belgium �0.67 �3.66 1992 �4.81 1988 �3.69 (0.20) �6.06 (1.04) 0.92 (0.02) 0.63 (0.11)Canada �0.58 �2.81 1973 �4.15 1986 �4.86 (0.22) �8.74 (0.94) 0.99 (0.03) 0.93 (0.09)France 0.43 �3.00 1996 �3.79 1990 �5.30 (0.18) �5.75 (1.45) 1.10 (0.02) 0.60 (0.15)Germany �0.48 �4.80 1999 �5.67 1989 �5.59 (0.21) �2.87 (0.87) 1.12 (0.02) 0.30 (0.09)Netherlands �0.30 �4.01 1977 �4.81 1982 �3.23 (0.40) �6.40 (2.00) 0.79 (0.04) 0.70 (0.21)Switzerland �1.36 �6.14 2001 �6.15 2001 �10.35 (1.27) 0.07 (0.05) 1.56 (0.13)USA �0.48 �2.03 1993 �3.98 1989 �5.37 (0.14) �10.25 (0.79) 1.07 (0.02) 1.03 (0.08)

Countries with tax finance systemDenmark 0.31 �3.36 1994 �3.74 1992 �8.64 (0.58) 0.05 (0.04) 1.37 (0.06)Finland �0.53 �3.98 1993 �4.14 1982 �5.62 (0.31) �5.81 (0.81) 1.08 (0.04) 0.60 (0.09)Greece �0.30 �4.00 1993 �5.07 1992 �2.08 (0.29) �2.76 (1.09) 0.74 (0.03) 0.33 (0.11)Ireland 0.94 �3.51 1977 �4.24 1982 �2.14 (0.37) �2.17 (0.43) 0.70 (0.04) 0.23 (0.05)Iceland �0.99 �3.95 1975 �4.00 1992 �7.05 (0.19) �6.05 (2.09) 1.26 (0.02) 0.60 (0.21)Italy �1.41 �4.35 1990 �4.28 1990 �7.44 (0.84) �0.09 (0.05) 1.35 (0.09)New Zealand �0.68 �4.15 1989 �4.11 1989 �6.90 (0.39) 0.27 (0.04) 1.21 (0.04)Norway �0.75 �4.43 1981 �5.03 1979 �6.25 (0.46) �1.84 (0.64) 1.07 (0.06) 0.24 (0.07)Portugal �0.47 �4.22 1975 �4.95 1981 �13.09 (0.85) 3.29 (1.05) 2.02 (0.10) �0.41 (0.12)Spain �0.56 �3.89 1985 �4.49 1985 �9.59 (0.40) �0.21 (0.04) 1.57 (0.04)Sweden 0.09 �4.29 1993 �5.16 1992 �5.98 (0.21) �5.12 (1.09) 1.10 (0.02) 0.56 (0.11)United Kingdom �0.03 �4.13 1993 �4.07 1993 �6.43 (0.11) 0.19 (0.02) 1.17 (0.01)

Group mean t: eZtN;T

Gross database 5.22 �2.13a �5.50a

Cross sec. corr.Corrected �0.97 �4.07a �3.47a

This table reports the different statistics that we have employed for testing the non-cointegration null hypothesis, as well as theestimation of the most appropriate model that captures the relationship between the health expenditure analysed in this tableversus the GDP. Column EG reports the value of the Engle–Granger statistic for each single series. Similarly, GH-I and GH-IIIare the Gregory–Hansen statistics for testing the non-cointegration null hypothesis for the level shift and for the changing slopecases, respectively, with TB being the estimation of the period where the break occurs. b1–b4 are the fully modified estimations ofthe parameters of model, with their respective standard errors reported in brackets. Finally, the group mean t-statistics are thePedroni and the Banerjee–Carrion statistics for testing the panel data unit root null hypothesis, with the values in italics havingbeen obtained after removing the cross-sectional mean in order to take into account the possible correlation of the perturbations.a ;bThis means the rejection of the null hypothesis for a given 1 and 5% significance level, respectively. The estimation of theparameter b4 has not been included in the cases where it was not significant.



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If we compare the evolution of the total pharmaceutical expenditure with respect to its government orprivate components (Tables II–IV), we can obtain the following insights. First, we observe that theincome elasticity of total pharmaceutical expenditure has increased in an important number of countries(13 out of 20), as the estimation of b4 shows in the last column of Table II. The elasticity only decreasesin Portugal, while we cannot find a change in the income elasticity for the rest of the countries.Additionally, we can see that the value of the income elasticity is almost unanimously greater than 1 atthe end of the sample. Consequently, pharmaceutical goods can be considered as luxury goodsnowadays. This result can be explained from different perspectives. For example, we should considerthat new pharmaceutical goods are relatively qualified labour commodities, with their prices growingfaster than average. Furthermore, it is also true that the dependence ratio (population over 65) is higherand, therefore, the demand for pharmaceutical goods grows faster than income and faster than someyears ago. Another possible explanation is that the basic services are satisfied and the new medicinescover a ‘marginal’ demand because they are less effective for increasing longevity; therefore, the pricethat consumers have to pay to increase it is higher. Finally, the current demand in OECD countries maybe changing from ‘cure’ to ‘care’ medicine, which implies that the new medicines can behave as luxurygoods.

Tables III and IV allow us to analyse the causes of the behaviour of total pharmaceutical expenditure.According to Table III, the income elasticity of government pharmaceutical expendituretends to decrease in those countries that exhibit a greater value of income elasticity beforethe appearance of the structural break (Canada, Ireland, New Zealand, Norway, Portugal, Spainand the United Kingdom). By contrast, the information reported in Table IV goes in the opposite

Table III. Government pharmaceutical expenditure


Countries with insurance finance systemAustralia �0.03 �3.03 1994 �3.93 1977 �5.02 (0.71) �9.38 (0.94) 0.93 (0.09) 0.98 (0.11)Belgium �1.35 �4.30 1996 �4.30 1980 �4.18 (0.21) 0.12 (0.04) 0.90 (0.03)Canada 0.88 �4.48 1975 �4.87 1981 �27.26 (1.10) 9.04 (1.45) 3.26 (0.12) �0.99 (0.16)France 0.21 �3.17 1996 �4.07 1989 �5.95 (0.24) �7.98 (1.64) 1.13 (0.03) 0.81 (0.16)Germany �0.52 �4.89 1979 �5.39 1995 �6.41 (0.24) �9.31 (2.59) 1.18 (0.03) 0.93 (0.26)Netherlands �1.08 �4.38 1989 �4.47 1989 �4.87 (0.78) 0.42 (0.09) 0.94 (0.08)SwitzerlandUSA �0.73 �3.38 1977 �3.67 1980 �14.88 (0.83) �8.89 (1.24) 1.83 (0.10) 0.88 (0.13)

Countries with tax finance systemDenmark �0.48 �5.09 1992 �5.41 1992 �5.93 (1.17) �2.90 (1.76) 1.00 (0.12) 0.32 (0.18)Finland �0.28 �9.31 1980 �8.74 1992 �11.46 (0.43) �0.60 (0.08) 1.67 (0.05)Greece �0.28 �3.59 1996 �4.39 1992 �2.39 (0.34) �3.97 (1.27) 0.71 (0.04) 0.48 (0.48)Ireland 1.29 �5.18 1978 �7.09 1978 �17.46 (1.83) 10.75 (1.85) 2.44 (0.22) �1.29 (0.22)Iceland �1.53 �5.66 1981 �5.68 1983 �9.01 (0.38) 0.27 (0.08) 1.39 (0.03)Italy �2.71 �3.45 2001 �4.11 2000 10.39 (3.14) �33.15 (28.1) �0.54 (0.32) 3.31 (2.76)New Zealand �1.52 �4.99 1987 �5.51 1987 �7.53 (0.34) 2.87 (1.39) 1.25 (0.04) �0.28 (0.14)Norway �1.15 �3.64 1987 �4.75 1989 �11.51 (0.26) 4.31 (1.00) 1.59 (0.03) �0.41 (0.10)Portugal �1.22 �4.19 1974 �4.52 1979 �15.16 (1.54) 5.24 (1.89) 2.24 (0.19) �0.66 (0.21)Spain �2.09 �4.68 1972 �4.22 1975 �22.48 (1.08) 14.59 (1.29) 3.21 (0.14) �1.87 (0.16)Sweden �0.27 �4.81 1993 �5.03 1992 �7.22 (0.30) �2.95 (1.56) 1.19 (0.03) 0.34 (0.10)United Kingdom �1.00 �5.91 1969 �6.36 1971 �19.18 (2.08) 11.37 (2.13) 2.69 (0.26) �1.42 (0.27)

Group mean tGross database 3.26 �7.52a �8.51a

Cross sec. corr.Corrected �3.73a �6.13a �7.60a

See note in Table II.



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direction, because it shows that the income elasticity of private pharmaceutical expenditurehas increased in 12 countries. This result can help us to better understand the growthof total pharmaceutical expenditure income elasticity. In fact, we find that certain governmentsof the countries included in the sample have made some effort to reduce pharmaceutical expenditure,especially in the countries where the elasticity was important and where the health system is financed bytax, but this reduction has affected the decisions of consumers who have increased the share of theirincome devoted to buying medicine, and the elasticity of private pharmaceutical expenditure hasincreased.

Let us now turn to comparing the evolution of the Total HCE. We should first recall that Clementeet al. (2004) found evidence in favour of a reduction in the elasticity of Total HCE in some countries,with this reduction being driven by the evolution of government health expenditure. However, thereduction of Total HCE has been modest because private health expenditure elasticity increased. Thus,it seems appropriate to carry out a similar analysis but paying attention now to its pharmaceutical andnon-pharmaceutical elements.

If we take into account the results reported in Tables II, V and VI, we find that there is a reductionin the income elasticity of Total HCE. This reduction is clearly due to the behaviour ofnon-pharmaceutical expenditure. This leads us to affirm that the composition of Total HCE ischanging in the OECD countries from a service to a consumer commodity and, as a consequence, areduction in government health expenditure is the origin of a change in preferences in health products,from a cure system (covered) to a care and longevity-seeking system, where medicines play a moreimportant role.

Table IV. Private pharmaceutical expenditure


Countries with insurance finance systemAustralia 0.07 �3.24 1992 �4.81 1989 �4.40 (0.30) �8.77 (0.88) 0.85 (0.03) 0.93 (0.09)Belgium �0.38 �4.47 1990 �4.65 1990 �5.35 (0.36) �8.69 (3.41) 1.01 (0.04) 0.91 (0.04)Canada �0.54 �2.99 1975 �3.72 1982 �2.52 (0.30) �10.65 (0.47) 0.70 (0.02) 1.14 (0.05)France �0.69 �3.19 1989 �3.97 1984 �5.28 (0.15) �5.60 (0.47) 0.97 (0.02) 0.60 (0.05)Germany �1.11 �4.19 1982 �4.05 1982 �5.65 (0.22) 0.18 (0.03) 0.99 (0.02)Netherlands �1.02 �3.98 1999 �4.66 1993 �5.47 (1.66) 0.81 (0.24) 0.93 (0.17)SwitzerlandUSA 0.06 �2.10 1993 �2.83 1989 �5.01 (0.14) �8.66 (0.80) 1.02 (0.02) 0.87 (0.08)

Countries with tax finance systemDenmark 0.04 �5.91 1977 �6.32 1977 �7.29 (0.65) �1.91 (0.70) 1.16 (0.08) 0.20 (0.08)Finland �0.01 �3.84 1991 �4.36 1989 �3.26 (0.19) �3.07 (1.20) 0.75 (0.02) 0.35 (0.12)Greece �1.62 �4.15 1992 �4.16 1990 �2.49 (0.24) 0.07 (0.03) 0.68 (0.03)Ireland �2.89 �3.60 1986 �3.81 1985 3.20 (0.59) 0.30 (0.10) �0.01 (0.07)Iceland �1.25 �3.46 1984 �5.60 1989 �5.88 (0.32) �26.75 (2.55) 1.05 (0.04) 2.62 (0.25)Italy �2.28 �3.49 2001 �5.11 2000 �17.71 (1.34) �0.30 (0.05) 2.30 (0.13)New Zealand �0.47 �5.04 1991 �4.80 1991 �6.93 (0.69) 0.78 (0.07) 1.04 (0.08)Norway �1.38 �3.93 1981 �4.19 1979 �2.02 (1.83) �5.12 (2.27) 0.49 (0.22) 0.64 (0.26)Portugal �1.11 �6.47 1988 �5.29 1988 �10.42 (1.44) 0.07 (0.11) 1.57 (0.16)Spain �0.77 �4.34 1987 �4.99 1992 �0.95 (1.17) �6.96 (1.85) 0.49 (0.13) 0.75 (0.19)Sweden �0.97 �3.82 1994 �5.03 1992 �5.40 (0.30) �10.16 (1.52) 0.91 (0.03) 1.07 (0.15)United Kingdom �1.73 �4.32 1975 �4.95 1977 �5.31 (1.04) �6.37 (1.27) 0.91 (0.13) 0.70 (0.15)






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CONCLUSIONS

The debate on pharmaceutical expenditure has generated a wide range of literature. Nevertheless, froman aggregate perspective, there are very few studies that deal with health expenditure and this papercontributes to the debate. We have looked at the relationship between pharmaceutical expenditure andGDP with the intention of determining income elasticity.

The objective of this paper has been to analyse the behaviour of total pharmaceutical expenditure andits components, government and private pharmaceutical expenditure, and to estimate the long-termincome elasticity of total, pharmaceutical and non-pharmaceutical health expenditure. Estimationunder the panel data approach with structural breaks enables us to conclude that total pharmaceuticalexpenditure has tended to increase in the past decade in 13 of the 20 countries of the sample. However,government pharmaceutical expenditure income elasticity is constant or has been reduced in 11countries. This suggests that governments have applied policies to maintain or reduce pharmaceuticalexpenditure. However, private pharmaceutical expenditure evolution has been higher than governmentpharmaceutical expenditure and the final effect has been the growth of total pharmaceuticalexpenditure. Moreover, the results of the analysis of pharmaceutical and non-pharmaceutical healthexpenditure indicate that the reduction in Total HCE found in Clemente et al. (2004) is due to non-pharmaceutical health expenditure.

Furthermore, the income elasticity of pharmaceutical health expenditure differs as much betweencountries as between its government or private components. This is a warning about the dangers ofadopting homogeneous policies or policies previously adopted in other countries with differentcharacteristics. By way of a conclusion, we would say that the same overall policy can have very

Table V. Total health-care expenditure


Countries with insurance finance systemAustralia �0.85 �4.47 1972 �4.80 1981 �7.02 (0.20) 1.58 (0.38) 1.49 (0.02) �0.19 (0.04)Belgium �1.17 �4.72 1976 �5.13 1976 �9.02 (0.86) 4.08 (0.87) 1.70 (0.10) �0.46 (0.10)Canada �0.67 �4.04 1976 �4.01 1976 �4.66 (0.15) �0.09 (0.03) 1.23 (0.02)France �0.67 �6.20 1972 �6.31 1992 �5.32 (0.06) 1.95 (0.63) 1.29 (0.01) �0.19 (0.06)Germany �0.70 �4.42 1992 �4.39 1992 �3.93 (0.22) 0.09 (0.03) 1.16 (0.02)Netherlands �0.82 �4.26 1983 �4.42 1984 �4.45 (0.17) �0.08 (0.02) 1.20 (0.02)Switzerland �1.27 �3.79 1994 �4.57 1985 �6.34 (0.16) �3.03 (0.68) 1.39 (0.02) 0.30 (0.07)USA �0.90 �4.28 1977 �4.43 1979 �6.54 (0.13) �0.10 (0.03) 1.45 (0.02)

Countries with tax finance systemDenmark �1.59 �4.20 1986 �4.44 1986 �3.00 (0.20) �0.09 (0.02) 1.06 (0.02)Finland �1.85 �4.25 1997 �4.34 1987 �4.89 (0.17) 4.68 (0.89) 1.24 (0.02) �0.48 (0.09)Greece �0.42 �4.08 1993 �4.44 1989 �2.33 (0.32) �5.11 (0.87) 0.96 (0.04) 0.56 (0.09)Ireland �1.27 �3.57 1969 �3.91 1978 �7.81 (0.29) 6.23 (0.36) 1.63 (0.04) �0.74 (0.04)Iceland �0.66 �4.65 1967 �5.24 1973 �8.95 (0.60) 2.79 (0.64) 1.72 (0.08) �0.35 (0.08)Italy �1.64 �2.91 2002 �5.13 1999 �0.44 (0.76) �8.64 (3.01) 0.79 (0.08) 0.86 (0.30)New Zealand �1.07 �3.98 1979 �3.79 1982 �5.25 (0.85) �2.92 (1.09) 1.28 (0.10) 0.29 (0.12)Norway 0.09 �4.56 1971 �4.45 1979 �7.88 (0.20) 3.25 (0.29) 1.59 (0.02) �0.38 (0.03)Portugal 1.22 �4.25 1985 �4.69 1983 �7.15 (0.40) �0.10 (0.06) 1.49 (0.05)Spain �0.31 �4.50 1970 �3.85 1971 �11.51 (0.50) 5.97 (0.52) 2.04 (0.07) �0.75 (0.07)Sweden �1.38 �3.36 1990 �3.33 1985 �5.01 (0.16) 1.85 (0.68) 1.28 (0.02) �0.21 (0.07)United Kingdom �0.58 �4.55 1982 �4.68 1984 �5.37 (0.12) �0.08 (0.02) 1.28 (0.01)






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different effects depending on the context in which it is implemented. For example, a reduction ingovernment health expenditure could contribute to an increase in private health expenditure with anindeterminate effect on the total.

Finally, we would like to put forward ideas for future research. The first is concerned with theexplanation of the periods where the structural breaks are found; for example, the possibility of changesin the behaviour of the agents such as modifications in pharmaceutical policies or the evolution of thiseconomic sector. Second, it would be interesting to develop a theoretical model that would illustrate thecauses of the structural breaks. The analysis of these topics is clearly beyond the scope of this paper and,consequently, is left for possible future research.

APPENDIX A

The sample size of the different variables employed in the present paper is as follows:

Country TPE GPE PPE THCE

Australia 1960, 1963, 1966,1969 and1971–2001

1960, 1963, 1966,1969 and1971–2001

1971–2001 1960, 1963, 1966,1969 and1971–2003

Belgium 1970–1997 1970–1999 1970–1997 1970–2003Canada 1960–2003 1969–2003 1960–2003 1960–2003

Table VI. Non-pharmaceutical health expenditure


Countries with insurance finance systemAustralia �1.27 �4.47 1972 �5.76 1973 �7.57 (0.44) 3.43 (0.47) 1.53 (0.05) �0.38 (0.06)Belgium �2.07 �4.72 1976 �6.65 1976 �10.75 (0.92) 5.92 (0.94) 1.87 (0.11) �0.65 (0.11)Canada �0.83 �4.04 1997 �4.01 1997 �4.51 (0.09) �0.09 (0.02) 1.20 (0.01)France �1.30 �6.20 1972 �5.25 1972 �5.30 (0.10) 0.12 (0.02) 1.26 (0.01)Germany �0.71 �4.42 1992 �4.19 1992 �4.13 (0.23) 0.10 (0.03) 1.16 (0.03)Netherlands �0.99 �4.26 1984 �4.61 1984 �4.46 (0.18) �0.09 (0.02) 1.19 (0.02)Switzerland 0.07 �3.79 1995 �6.02 1995 �7.87 (0.56) 0.06 (0.02) 1.53 (0.06)USA �1.07 �4.28 1979 �4.10 1979 �7.24 (0.24) 0.96 (0.35) 1.51 (0.03) �0.11 (0.04)

Countries with tax finance systemDenmark �0.27 �4.20 1999 �4.68 1999 �0.69 (0.18) �6.21 (1.88) 0.81 (0.02) 0.61 (0.18)Finland �2.04 �4.25 1997 �4.51 1987 �5.30 (0.19) 6.24 (0.94) 1.27 (0.02) �0.64 (0.10)Greece �1.97 �4.08 1993 �4.84 1993 �3.43 (0.19) 0.28 (0.02) 1.07 (0.02)Ireland �0.92 �3.57 1988 �4.11 1987 �5.12 (0.30) 2.06 (0.49) 1.28 (0.03) �0.25 (0.05)Iceland �0.67 �4.65 1967 �4.98 1973 �9.41 (0.62) 3.03 (0.66) 1.75 (0.08) �0.38 (0.08)Italy �1.62 �2.91 2000 �4.53 1999 �0.39 (0.66) 0.11 (0.02) 0.76 (0.07)New Zealand �0.27 �3.98 1979 �3.83 1982 �4.81 (1.14) �3.94 (1.71) 1.22 (0.13) 0.40 (0.19)Norway �0.56 �4.56 1971 �4.48 1971 �7.73 (0.83) 3.18 (0.84) 1.55 (0.11) �0.36 (0.11)Portugal 1.01 �4.25 1974 �4.23 1977 �13.75 (1.62) 7.70 (1.66) 2.30 (0.20) �0.97 (0.21)Spain �0.76 �4.50 1988 �4.49 1997 �7.21 (0.34) 4.15 (1.34) 1.45 (0.04) �0.43 (0.14)Sweden �2.00 �3.36 1990 �3.32 1990 �3.90 (0.30) �0.17 (0.04) 1.15 (0.03)United Kingdom �1.13 �4.55 1984 �4.46 1984 �5.58 (0.15) �0.10 (0.03) 1.29 (0.02)


Cross sec. corr.Corrected �1.78b �4.06a �4.77a




DOI: 10.1002/hec

Denmark 1980–2003 1980–2003 1966–2003 1971–2003Finland 1960–2003 1964–2003 1960–2003 1960–2003France 1960–1985

quinquennialand 1990–2003

1960–1985quinquennialand 1990–2003



Germany 1970–1990,1992–2003

1970–1990,1992–2003

1970–1990,1992–2003

1970–1990,1992–2003

Greece 1970–2003 1970, 1980,1988–2003

1970, 1980,1988–2003

1970, 1980,1987–2003

Ireland 1970 and1972–2002

1972–2002 1972–2002 1960–2002

Island 1960–2003 1960–2003 1960–2003 1960–2003Italy 1988–2003 1988–2003 1988–2003 1988–2003Netherlands 1972–2003 1972–2003 1972–2003 1972–2003New Zealand 1971, 1976 and

1980–19971970–1997 1971, 1976 and

1980–19971970–2003

Norway 1962–1997 and2001–2002

1965–1990,2001–2002

1965–2002 1960–2003

Portugal 1970–1998 1970–1980(biannual)1982–1997

1978, 1980,1982–1997

1970–2003

Spain 1980–1990 and1995–2003

1960–2003 1980–1990,1995–2003

1960–2003

Sweden 1970–2002 1970–2002 1970–2002 1970–2002Switzerland 1985–2003 } } 1960–2003United Kingdom 1963–1997 1960–1997 1963–1997 1960–2002USA 1960–2003 1960–2003 1960–2003 1960–2003

TPE, GPE, PPE and THCE mean total pharmaceutical expenditure, government pharmaceuticalexpenditure, private pharmaceutical expenditure and total health-care expenditure, respectively. TheGDP values cover the sample 1960–2003 for all the countries considered. All the values have beenobtained from the 2005 OECD Health Database.

In those cases where some intermediate values are unknown, we have treated these values asmissing and we have estimated them by way of the SEATS/TRAMO methodology. For example,in the case of the total pharmaceutical expenditure for Australia, we have values for the years 1960and 1963. We have considered those of periods 1961 and 1962 as missing values, replacing themwith the results obtained from the application of the above-mentioned SEATS/TRAMOmethodology.

ACKNOWLEDGEMENTS

The authors would like to express their thanks to two anonymous referees and to the co-editor of thejournal for their helpful comments and observations on an earlier version of this paper. DGES andDGA financial support under project BEC2001-2561, DGA/Excellence Groups ADETRE and GESESDGA groups, respectively, is also acknowledged.

Country TPE GPE PPE THCE



DOI: 10.1002/hec

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Pharmaceutical expenditure, total health-care expenditure and GDP