Health expenses and economic growth: convergence dynamics across the Indian States

Int J Health Care Finance Econ (2013) 13:261–277DOI 10.1007/s10754-013-9130-9

Health expenses and economic growth: convergencedynamics across the Indian States

Nicholas Apergis · Puja Padhi

Received: 25 May 2012 / Accepted: 27 August 2013 / Published online: 14 September 2013© Springer Science+Business Media New York 2013

Abstract In this paper we explore convergence of real per capita output and health expensesacross the Indian States. The new panel convergence methodology, developed by Phillipsand Sul (Econometrica 75:1771–1855, 2007), is employed. The empirical findings suggestthat these States form distinct convergent clubs, exhibiting considerable heterogeneity in theunderlying growth and health expenses factors. These findings should help policy makers indesigning appropriate growth-oriented and/or health sector programs and setting prioritiesin their implementation.

Keywords Growth convergence · Health expenses convergence · Indian States · Logt test

Introduction

The concept of economic convergence is related to the reduction of inequality between coun-tries, states, sectors or regions. In an aggregate production function setting, two differenttheories have been developed that reach different predictions on real convergence. The firstis the neoclassical growth theory, introduced by Solow (1956) and extended by Mankiw et al.(1992). It states that per capita income convergence to its steady-state is mainly attributed tothe association between social increasing returns and both physical and human capital (Barro1991; Barro and Sala-i-Martin 1992). The second is the endogenous growth theory, developedby Romer (1986, 1990), Lucas (1988), and Rebelo (1991). According to this, convergencedepends on the growth rates of the accumulation of labour, capital and/or technological inno-

Disclaimer The views and results expressed in this paper are those of the authors.

N. Apergis (B)Department of Banking and Financial Management, University of Piraeus, Piraeus, Greecee-mail: [email protected]

P. PadhiDepartment of Humanities and Social Sciences, IIT Bombay, Powai, Mumbai 400076, Indiae-mail: [email protected]

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262 N. Apergis, P. Padhi

vation. On the empirical front, research has shown that cross-country differences in economicperformance are driven by both total factor productivity (TFP) and factor accumulation (e.g.,Hall and Jones 1999; Easterly and Levine 2001).

According to the health-led growth hypothesis, health expenses are considered a proxyof capital; thus, as an input of production is expected to lead to income increases. At thesame time, in many countries around the world, the concept of health expenses becomesincreasingly important, especially for economic growth issues. The role of such expensesin the process of economic growth is easily understood, since a healthier population actsas a by-product of total factor productivity; a healthier population can work longer, canbe more productive, can secure higher earnings, can have higher learning abilities and, ingeneral, can enhance the efficiency of the economy’s human capital (Schultz 1999). How-ever, the bulk of the research has focused on examining the role of health expenses forboth economic growth and for financing issues in developed countries where health careexpenditures have been rising rapidly in the recent years. This rise constitutes a major con-cern for health policy makers. Alcande-Unzu et al. (2008) address the issue of cross OECDcountry disparities in health care expenditures. Their findings document that the main deter-minants of cross country dispersion in per capita health care expenditures are health careexpenditures over GDP and labor productivity. Therefore, the role of health expenditures inthe ‘catch-up’ process has received extensive empirical support for the case of developedeconomies.

Certain empirical studies have displayed the upward trend in health expenses as well astheir contribution to economic growth, through certain channels, such as the demographicpopulation growth (Felder et al. 2000), the augmented technology hypothesis (Okunadeand Murthy 2002), the health’s sector infrastructure (Dritsakis 2003) and the health systemplanning (Russe 2001). Moreover, Dritsakis (2005) investigates the association betweenhealth expenses and economic growth for 15 members of the European Union (EU) andhis results show that health expenses exert a positive and statistically significant effect oneconomic growth. At the same time, the underdevelopment of the Indian insurance systemmanifests itself in its small size relative to other types of insurance, absence of well-developedpricing and funding policies, and the paucity of long-term financial instruments throughwhich to match assets and liabilities. Public health care is largely the responsibility of stategovernments, and financing varies from state to state.

However, despite its potential importance and usefulness, the analysis of health expendi-ture disparities in this context and across emerging economies has not received any seriousattention as very few studies focus on health care convergence and given the importance ofthe issue for the growth future of such economies. Therefore, it is necessary the relevantliterature to provide empirical information on the convergence of health expenditures acrosseither countries or states, given the importance of such a piece of information for a number ofreasons such as: (i) improved projections for health care spending trends that better take intoaccount the features of the health care system as well as pressures arising from technologicalprogress and aging, (ii) stabilizing age-related public spending in relation to GDP, includingcontaining the growth in public health spending, could constitute an important pillar of thefiscal consolidation strategy required to reduce the high public debt ratios accumulated, espe-cially, in the wake of the recent global financial crisis, and, most importantly, (iii) becausethe majority of emerging economies, such as India, have lower coverage levels and morescope to expand spending. In order to maintain fiscal sustainability, it is essential to restrictthe benefit package to the most essential health services, until the capacity to finance higherpublic health spending increases. There is scope in emerging economies for reforms thatprovide greater financial incentives for the provision of cost-effective health care—such as

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Health expenses and economic growth 263

primary and preventive care—and to shift the composition of spending toward the preventionof infectious diseases and to activities that benefit poorer rural areas.

The objective of this paper is to empirically investigate for the first time convergence ofreal per capita output and public health expenses for the case of an emerging economy and,particularly, across the 26 Indian States. Most of India’s estimated 1.2 billion people haveto pay for medical treatment out of their own pockets, while they do not have full accessto quality health care. As a percentage of GDP the overall health expenses in India are low(approximately 5 %), thus, out-of-pocket payments are the dominant source of health financ-ing. At the same time, the gap between the health of the richest and poorest continues towiden (Baru et al. 2010), while high costs exist in private health because of a lack of regula-tion (Sengupta and Nundy 2005). In addition to the lack of overall healthcare infrastructure,another important characteristic of the country’s healthcare industry is the lack of a medicallyinsured population and the small ratio of inhabitants covered by health insurance policies,while the majority of the population have extremely limited access to modern medical treat-ments and depend more on traditional treatment methodologies. The findings will have highimportance in relation to the designing of efficient health sector policies, while they will bethe basis on more realistic policy recommendations that could be put forward, in an effortto eliminate such differences across the Indian regions. In addition, if the picture emergingfrom the two variables under investigation is very similar, this will call for increasing theshare of public health expenses in total health expenses as well as the distribution of suchfunding across states, since the under-funding of certain States signals a severe signaling forpoverty and income inequality issues. The empirical findings are expected to shed more lighton the policy dilemma of how to balance between the articulate middle upper class demandfor more access to technologically advanced and subsidized clinical services and the morepressing needs of the poor for coverage of basic disease control interventions. We do hopethat the empirical results of this study will lay out an agenda for future empirical research andpolicy analysis that will enable Indian policy makers to efficiently design strategic policiesin both the insurance and the health systems to efficiently play the important role they arelikely to have in the country’s future reforms.

The novelty of this paper stems from the implementation of the new methodology ofpanel convergence testing, recommended by Phillips and Sul (2007). The philosophy of themethodological approach is based on the club convergence hypothesis, suggested by Fis-cher and Stirbock (2004), which considers that certain countries, states, sectors or regionsthat belong to a club move from a disequilibrium position to its club-specific steady-stateposition. This methodology has several appealing characteristics. To begin with, no specificassumptions concerning the stationarity of the variable of interest and/or the existence ofcommon factors are necessary. Nevertheless, this convergence test could be interpreted asan asymptotic cointegration test without suffering from the small sample problems in unitroot and cointegration testing. The methodology is also based on a quite general form ofa nonlinear time varying factor model. More importantly, it takes into account that Statesexperience transitional dynamics, while it abstains from the hypothesis of homogeneous tech-nological progress, an assumption extensively employed in the majority of growth studies.This is crucial, since under technological heterogeneity, the examination of either growthconvergence or growth determinants by standard panel stationarity tests is not valid (Phillipsand Sul 2009).

The rest of the paper is organized as follows. The following Section reviews the recentempirical literature on both economic growth convergence and the role of health expensesin the process of economic growth, while the next Section presents a brief discussion aboutthe structure of health care across Indian States. The following Section presents the new

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methodology employed, while the next Section discusses the results of the empirical analysis.The final Section summarizes the paper and offers some policy implications.

Literature review

Income convergence

Initial empirical tests of the convergence hypothesis fell into the category of β-convergencetests, where researchers estimate the growth rate of per capita real income on an initial valueof real per capita income with or without other conditioning variables. Without other vari-ables, the tests consider absolute convergence, whereas with conditioning variables, the testsexamine conditional convergence. Growth-initial level regressions used to test for beta con-vergence are, generally speaking, the log-linearized solution of a non-stochastic neoclassicalgrowth model (or its augmented versions) with an error term added. Since in neoclassicalgrowth theories convergence is the tendency of a specific country’s output to converge to itsown steady state under specific assumptions, beta convergence is a suitable methodology totest convergence within an economy.

Lee et al. (1997) develop a stochastic Solow-Swan growth model to test for the pres-ence of convergence for per capita income with data from 102 countries, developing fourdifferent methods for testing β-convergence. When they relax the homogeneity assumptionsinvolved in the traditional cross-section and panel-data estimation approaches, they find thatthe speed of convergence increases dramatically, but the precision of the estimates deteri-orates significantly. Their results imply significant differences for the steady state growthrates across countries. Additionally, they argue that researches should explicitly considerthe heterogeneity across countries in steady state to avoid biases in the estimations for con-vergence. But as just mentioned, the precision of their estimates deteriorates dramaticallywhen considering such heterogeneities. Nonetheless, the authors conclude that technologicalgrowth differs across countries, although OECD countries experience, on average, such highertechnological growth with lower dispersion. Differences in technological growth patterns,given the cross-country heterogeneity, imply that countries are diverging, not converging.Binder and Pesaran (1999) question the adequacy of growth-initial level regressions. Theyshow that beta convergence, even when used to study the growth path of a given econ-omy towards its own steady state, can collapse in the case of a stochastic technologicalprogress. Durlauf et al. (2005) point out that a negative coefficient (beta) on initial income ina cross-section framework could simply imply that economies converge to their own differ-ent steady states. Finally, Pesaran (2006) argues that by definition, beta convergence refersto convergence within an economy. Despite this fact, most of the empirical studies ana-lyze the cross-country output dynamics by testing for beta convergence using cross-sectiondata.

According to the approach of σ -convergence, a group of economies converges if thecross section variance of the per capita output declines across time. According to Bliss(1999, 2000), the underlying assumption of an evolving data distribution introduces dif-ficulties in the interpretation of the test distribution under the null. Moreover, the rejec-tion of the σ -convergence hypothesis does not necessarily mean that economies are notconverging; the presence of transitional dynamics in the data could lead to the rejectionof the null hypothesis of σ -convergence. If countries converge to a common equilibriumwith shared global technologies and identical internal structures, then the dispersion ofincome should disappear in the long-run as all countries converge to the same real per

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capita income. If, however, countries converge to convergence clubs or to their own uniqueequilibrium, the dispersion of real per capita income will not approach zero. Moreover,in the latter case of country specific equilibrium, the movements of the dispersion willdepend on the initial distribution of real per capita incomes relative to their final long-runoutcomes.

Canova (2004) proposes a new technique for grouping converging countries in terms ofreal per capita income. His methodology implies that countries exhibit multiple steady statesin terms of real per capita income. He empirically tests convergence across two samples—datafrom 144 European regions and 21 OECD countries. He finds that the steady state distributionincome for the European regions clusters around four different poles, while that for the OECDcountries clusters around two different poles. Pesaran (2006) proposes a general probabilisticdefinition of convergence and uses a pair-wise approach to test for output convergence acrosscountries, using output data from the Penn World Tables. His method indicates the absence ofconvergence in terms of real per capita income. By contrast, he finds evidence of convergencein real per capita income growth rates. He argues that these results may reflect the following:although technology spreads widely across countries, other important country specific factorsexist that prevent output between countries from converging.

Grier and Grier (2007) attempt to determine which factors lead per capita income todiverge. Their sample employs data from 90 countries, while their results provide strongevidence of income divergence across countries. They also argue that researchers shouldinclude new additional possible determinants of income divergence, as the traditional fac-tors from the neoclassical growth model cannot explain real per capita income divergence,because these traditional factors converge across countries. Phillips and Sul (2003) argue thatcross-section divergence is possibly a transient phenomenon since economies may exhibittransitional divergence in their way towards a common steady state. They make use of anew methodology to test for club convergence. They examine three different samples forconvergence in per capita income. The first sample employs data from 48 US states, thesecond sample data from 18 western OECD countries and the third sample data from 152countries reported in the Penn World Tables. Their results for the US sample indicate that thetransition paths for every state appear to converge. The results for the OECD sample indicatedivergence in terms of per capita income until World War II. Around 1950, however, thispattern changed and the transition paths of per capita income appear to converge. Finally, theresults from the Penn World Tables sample indicate that although per capita income divergesacross countries, strong evidence exists for the presence of converging subgroups (i.e., clubconvergence).

In the empirical growth literature, the majority of empirical studies refer to convergenceas the tendency of narrowing the output gap across countries. In this line, Evans (1996)applies an alternative method that uses the cross-country variances of per capita real incomeand a sample of 15 countries. His results indicate that income reverts to a common trend.The use of cointegration and unit root tests for testing output convergence are subject to anumber of serious drawbacks. First, these tests fail to detect convergence when more than oneequilibria exist. In the growth literature there is theoretical as well as empirical evidence ofthe possibility of club convergence. Hobijn and Frances (2000) provide empirical evidencein favor of the presence of converging clubs across countries. Second, if the countries tendto converge but the data available to the econometrician are from a time period in whichtransitional dynamics prevail, cointegration and unit root tests are not capable of ‘catching’the tendency to converge. Third, suppose that two countries are converging to the samesteady state and they are also near the steady state. If output data available to the researcherare a combination of steady state and transitional data, then empirical testing for convergence

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using cointegration and unit roots tests may lead to misleading results. Given the sample sizesusually used in empirical studies, it is unlikely that they contain data only from countrieswhich are near their steady states. In other words, to study the issue of convergence, bothtransitional dynamics and long run behavior have to be modeled together in a consistentframework. Unfortunately, standard existing testing methodologies for output convergencefail to account for both regularities and, thus, they are not suitable for testing real economicconvergence. Durlauf et al. (2005), in a seminal survey, argue that growth econometrics asan area of research is still in its infancy and they point out the need for researchers to developnew econometric methodologies for testing the convergence hypothesis. Some new methodsare needed to evaluate the growth paths over time as well as the long run convergence acrosseconomies.

Finally, within the area of the EU, Tsionas (2000) argues that EU convergence is an issuefrom which no clear cut results can be obtained. On the contrary, Crespo-Cuaresma et al.(2008) show that EU membership enhances the degree of integration and thus, has positiveand lengthy effects on real convergence. More recently, Salinas-Jimenez et al. (2006) finda slight tendency towards convergence, attributing this effect mainly to capital and humanaccumulation as well as to convergence in certain categories of expenses, such as schooling,research and health.

Health expenses and growth

The majority of countries attempting to identify the nexus between health expenses andeconomic growth focus on either OECD or the developed countries (Blomqvist and Carter1997; Hartwig 2008). A certain strand of the empirical literature identifies the presence ofstrong causal association running from health expenses to economic growth. In particular,Bloom et al. (2004) show that, worldwide, health expenses have a positive and statisticallysignificant effect on per capita economic growth, while a vice versa effect is also present,implying that higher levels of growth can stimulate higher health expenses, since they permiteconomies to afford a better health care. The same results are also supported by Bhargava etal. (2001). They also support the evidence that the causal relationship running from healthexpenses to economic growth is much stronger. Finally, Panopoulou and Pantelidis (2012)make use of the Phillips and Sul (2007) convergence procedure, as we do, to examine thedegree of convergence in health care expenditures across the US states. Their empiricalfindings document that the US states form two clubs with specific geographical characteristicsthat converge to different equilibria.

In another strand of literature, researchers focus on larger samples that include not onlydeveloped but also emerging economies. In particular, Barro (1997), Weil (2001) and Barroand Sala-i-Martin (2004) examine the link between economic growth and a number of fac-tors, in which health plays a predominant role, for a large sample of countries. Their findingsdisplay that health expenses have a positive and statistically significant effect on the growthrate for all countries included in their sample. Gyimah-Brempong and Wislon (2004) alsoprovide evidence that virtually 30 % of the growth rate of per capita income in sub-SaharanAfrican countries can be easily attributed to health expenses. At the same time, Straussand Thomas (1998) find that there exists a reverse causality running from income to healthexpenses. Samudram et al. (2009) and Tang (2009) investigate the association under studyfor the case of Malaysia; their empirical findings, though they suffer from certain method-ological deficiencies, provide support for the impact of health expenses on economic growth.Cole and Neumayer (2006) show that poor health leads to lower total factor productiv-ity and this is the critical factor that has led certain regions in our world to be underde-

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veloped and in a bad shape. Finally, a different group of studies use the methodology ofpanel data to provide empirical evidence about the role of health in the process of economicgrowth (Gerdtham and Lothgren 2000; Wang and Rettenmaier 2007; Hartwig 2008). How-ever, their results cannot be used to formulate effective country-specific growth and healthpolicies.

For the case of India, Kannan et al. (1991) reach the same as above results for theState of Kerala, while Vaidyanathan (1995), Duraisamy (1995), Gumber (1997) claim infavour of a link between health expenses and economic growth, but their methodolog-ical approach is purely descriptive. By contrast, Gupta and Mitra (2003) examine thelink between health expenses and economic growth for 15 Indian States using econo-metric approaches, which, however, do not shed full light on the link under study. Inother, more indirect, studies, Deolalikar (1988) finds that health expenses play a domi-nant role for labor productivity, while Duraisamy and Sathiyanan (1998) show that suchexpenses lead to strong increases in wage rates and labour supply across the IndianStates.

The structure of health expenses across the Indian States: a descriptive picture

According to economic theory, the concept of health care entails a great likelihood of ‘marketfailure’ with two main characteristics: externalities and information asymmetries. Both ofthese characteristics make mandatory the intervention from the state. Moreover, the exter-nalities are related to deficiencies associated with nutrition and sanitation as well as certaindiseases which can pass either directly to humans or indirectly through the physical envi-ronment; therefore, state intervention is needed in the form of price subsidies to stimulatehealth care services or in the form of direct provision of these services. By contrast, infor-mation asymmetries lead to the disruption of the health care system as well as of the systemassociated with insurance markets. In this case, state intervention takes the form of regula-tion, i.e. the provision of licenses to health services providers and the adoption of certainprofessional norms that maintain the quality of health care services. The above exemplify theimportance of public expenses in the health sector, without ignoring that under the proper reg-ulation and supervision, private expenses in the same sector are not capable of performing thejob.

According to the National Health Accounts of India and spanning the period up to 2005,households account for more than two-thirds of health spending, while the business sectoraccount for only 5 %. The overall health budgetary allocation consists of expenses on themedical, public health and on family welfare. In addition, central government’s health spend-ing has been ranging from 22 to 37 % with respect to the period under examination, while theState with the highest per capital spending rate in health was Goa and it was seven times thatof per capita spending in the State with the lowest per capita spending that was Meghalaya. Inaddition, there are many states in which households undertake more than 80 % of all healthspending, denoting first, a substantially high burden for them and second, that even poorhouseholds are willing to spend on health care to ensure minimal health services. In termsof health provider, real per capital spending by the central government has been growingslightly through 2005, but it is expected to increase further after that year due to anticipatedchanges in the National Rural Health Mission, an ambitious central health program, initiatedin the mid-1990s.

Over the period 1981–2005, increases in public (central government plus state gov-ernment) health expenses have been below the growth rate of real GDP for the majorityof the Indian States. Only in 4 States these expenses grew at an annual rate above 7 %,

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Fig. 1 Per capita GDP and the public health expenses to GDP ratio across the Indian states at 2005

i.e. Andhra Pradesh, Karnataka, Punjab and West Bengal, while in three States the declinewas significant, i.e. Assam, Madhya Pradesh and Uttar Pradesh. In these three States thedecline was very sharp, leading to the worsening picture of health spending, since theseStates were already characterized by inadequate quantities and qualities of health facilities.The overall poor picture of health spending could be a crucial factor for the slow improve-ment in a range of basic health indicators, such as life expectancy at birth, infant mortalityand maternal mortality. It is finally worth pointing out that even States with high per capitareal income have poor per capita health expenses, i.e. Gujarat, Haryana and Punjab, whileothers, with low per capita real income, indicate high per capita public spending on health,i.e. Rajasthan. A picture about the structure of per capita income and health expenses acrossthe Indian States at the year 2005 can be seen in Fig. 1. The picture confirms much of thedescriptions mentioned above.

Econometric methodology

In this section, we outline the methodology proposed by Phillips and Sul (2007) (henceforthPS) to test for convergence in a panel of States. We also briefly discuss the clustering algorithmthat allows us to classify States into convergent clubs.

Testing for convergence

Let us have panel data for a variableXit , where i = 1, . . . N and t = 1, . . . T , with N , Tthe number of States and the sample size, respectively. Often Xit is decomposed into twocomponents, one systematic,git , and one transitory ait

Xit = git + ait (1)

PS transform (1) in a way that common and idiosyncratic components in the panel areseparated. Specifically,

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Xit =(

git + ait

μt

)μt = δi tμt , for all i, t (2)

In this way, the variable of interest, Xit , is decomposed in two components, one common, μt ,and one idiosyncratic, δi t , both of which are time varying. This formulation enables testingfor convergence by testing whether the factor loadings δi t converge. To do so, PS defines therelative transition parameter, hit , as:

hit = Xit1N

∑Ni=1 Xit

= δi t1N

∑Ni=1 δi t

(3)

which measures the loading coefficient δi t in relation to the panel and, as such, the transitionpath for the region i relative to the panel average. The relative transition curves depict therelative transition coefficients hit , calculated from Eq. (3).

In the context of the long-run behaviour, we first remove the business cycle componentof Xit by employing the Hodrick and Prescott (1997) filter. The only input required is asmoothing parameter determined mainly by the frequency of the data.1 Having extracted thetrend component from the series denoted as Xi t , we calculate the estimated transition paths

as hi t = Xi t1N

∑Ni=1 Xi t

. Next we construct the cross-sectional variation ratio H1/

Ht where:

Ht = 1

N

N∑i=1

(hi t − 1)2

PS show that the transition distance Ht has a limiting form of

Ht ∼ A

L(t)2t2αas t → ∞

where A is a positive constant, L(t) is a slowly varying function, such as log(t + 1) and α

denotes the speed of convergence. To test for the null hypothesis of convergence,

H0 : δi = δ and α ≥ 0

against the alternative

HA : δi �= δ for all i, or α ≺ 0

PS run the following log t regression:

log(H1

/Ht

) − 2 log L(t) = c + b log t + ut (4)

where L(t) = log(t + 1). The standard errors of the estimates are calculated using a het-eroskedasticity and autocorrelation consistent (HAC) estimator for the long-run varianceof the residuals. In this study, we employ the quadratic spectral kernel and determine thebandwidth by means of the Andrews (1991) data-dependent procedure. By employing theconventional t-statistic tb, the null hypothesis of convergence is rejected if tb < −1.65. Inpractice, this regression is run after a fraction of the sample is removed. PS recommendstarting the regression at some point t = [rT ], where [rT ] is the integer part of rT , andr = 0.3.2

1 In our application with annual data, the smoothing parameter λ, is set equal to 100.2 Extensive Monte Carlo simulations conducted by Phillips and Sul (2007) show that r = 0.3 is satisfactoryin terms of both size and power.

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This null hypothesis implies relative convergence (conditional convergence) rather thanabsolute convergence (convergence in levels). If we change the null hypothesis to α ≥ 1,which is equivalent to b ≥ 2, we can test for absolute convergence. Given that rejection ofthe null for the panel as a whole does not imply the absence of club convergence, PS go onestep beyond and develop an algorithm for club convergence. We next briefly outline the basicsteps of the respective algorithm.

Club convergence algorithm

Step 1 (Ordering): Order the members of the panel according to the last observation.Step 2 (Core Group formation): Calculate the convergence t-statistic, tk, for sequential log tregressions based on the k highest members (Step 1) with 2 ≤ k ≤ N . The core group sizeis chosen on the basis of the maximum of tk with tk > −1.65.

Step 3 (Club Membership): Select States for membership in the core group (Step 2) by addingone at a time. Include the new State (member) if the associated t-statistic is greater than zero(conservative choice). Make sure that the club satisfies the criterion for convergence.Step 4 (Recursion and Stopping): The States not selected in the club formed in step 3, form acomplement group. Run the logt regression for this set of regions. If it converges, then theseregions form a second club. If not, Steps 1 to 3 should be repeated, in order to reveal somesub-convergent clusters. If no core group can be found (Step 2), then these States display adivergent behavior.

Empirical analysis

Data

State-level data on annual basis come from the 26 Indian States spanning the period 1981–2005. The Indian States included in the sample are: Andhra Pradesh, Arunachal Pradesh,Assam, Bihar, Goa–Daman–Diu, Gujarat, Haryana, Himachal Pradesh, Jammu & Kashmir,Karnataka, Kerala, Madhya Pradesh, Maharastra, Manipur, Meghalaya, Mizoram, Nagaland,Orissa, Pondicherry, Punjab, Rajasthan, Sikkim, Tamil Nadu, Tripura, Uttar Pradesh andWest Bengal. Data on GDP per capita as well as on public health expenses to GDP ratioare both expressed in 1990 Rupee prices, i.e. converting nominal values into real values byusing the GDP deflator. They were obtained from the Central Statistical Organization of theGovernment of India.

GDP per capita convergence

Table 1 reports the results of the panel convergence methodology for real per capita GDP.The first row reports the result of testing full convergence (i.e., convergence among allsample Indian States), while rows 2–4 display the results of the club clustering proce-dure. The results of the convergence test for per capita income reject the null hypoth-esis of income convergence, since the point estimate of the log(t) statistic is −165.265(with critical value equal to −1.67). Nevertheless, the formation of the three differ-ent convergence clubs, shows that there exist three groups of States, each with 9, 12and 5 States, respectively, apparently characterized not only by different factor endow-ments but also by different productivities (but this goes beyond the research target of thispaper).

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Table 1 Club convergence: percapita GDP

Subgroup Countries t-statistic

Full sample −165.265

1st subgroup Arunachal Pradesh, Assam,Bihar, Goa, Gujarat,Himachal Pradesh,Manipur, Punjab, TamilNadu

7.153

2nd subgroup Andrha Pradesh, Haryana,Jammu-Kashmir,Karnataka, Kerala, MadhyaPradesh, Mizoram,Nagaland, Pondicherry,Sikkim, Tripura, WestBengal

1.875

3rd subgroup Maharastra, Meghalaya,Orissa, Rajasthan, UttarPradesh

−0.365

Table 2 Club convergence:public health expenses to GDPratio

Subgroup Countries t-statistic

Full sample −48.178

1st subgroup Andrha Pradesh, Karnataka,Kerala, Orissa, Pondicherry,Punjab, Rajasthan, TamilNadu

−1.309

2nd subgroup Bihar, Jammu-Kashmir,Maharastra

1.415

3rd subgroup Gujarat, Uttar Pradesh, WestBengal

−1.213

4th subgroup Arunachal Pradesh, Assam,Goa, Haryana, HimachalPradesh, Madhya Pradesh,Manipur, Meghalaya,Mizoram, Nagaland,Sikkim, Tripura

0.784

Health expenses convergence

We next examine the structure of public health expenses to GDP ratio to gain insight onthe relative importance of these expenses for each State. Table 2 presents the results of theanalysis for this variable. As we can observe, Table 2 does not have the same structure asTable 1. In particular, Table 2 documents that the null hypothesis of full convergence isrejected at the 5 % level, since the t-statistic estimate is −48.178. The results of the clubconvergence algorithm indicate the presence of four convergent clubs of States under theperiod under consideration. Moreover, the results are differentiated not only in terms of thenumber of clubs, but also in terms of the contents’ of clubs structure vis-à-vis those reportedin Table 1. These empirical findings confirm that public expenses in India, under their presentfigures and structure, cannot be the driving force for leading the Indian States to converge interms of economic growth.

Based on a suggestion by a referee, Fig. 2 provides a map that highlights the members ofeach convergence club after using the same grayscale color for States in the same club, thus

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Fig. 2 A geographical presentation of health expenses ratios clubs across the Indian states

displaying a geographical distribution of the clubs. The map highlights that the heterogeneityof health results is not primarily based on an exact geographical dispersion of health expenses.The funding distribution—based on governmental decisions—is not concentrated in specificgeographical areas, i.e. exclusively in the Southern or in the Northern States, but unveilsan interesting pattern concerning the issue of how fiscal authorities distribute annual healthfunding across States.

Finally, Fig. 3 depicts the relative transition curves for state public health expenses toGDP ratio of each convergence club. Visual inspection of these curves enables us to gain

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0.8

0.85

0.9

0.95

1

1.05

1.1

1981 1985 1990 1995 2000 2005

Club 1

Club 2

Club 3

Club 4

Fig. 3 Health expenses to GDP ratios: relative transition curves of convergence clubs. Notes The transitioncurves of the four convergence clubs indicate that these clubs display a tendency to convergence, but the exactperiod at which this will occur is beyond our studied time horizon

some insight on the outcomes of the testing methodology and monitor the convergence ofhealth expenses to GDP ratios for each club, relatively to the sample average. In particular,the transition curves report a graphical picture about the tendency of the cluster partici-pants (or groups of clusters) to converge or diverge from above or below 1, which is theconvergence path reference point during the period under study. All four transition curvesshow that at 2005 the tendency is towards convergence, though curves 1 and 2 seem toapproach convergence from above and curves 3 and 4 seem to approach convergence frombelow.

Robustness tests

Phillips and Sul (2009) argue that their convergence club methodology tends to overesti-mate the number of clubs than their true number. To avoid this overdetermination, theyrun the algorithm across the sub-clubs to assess whether any evidence exists in supportof merging clubs into larger clubs. The results of the new converging tests are reportedin Table 3. Following Phillips and Sul (2009) we consider adjacent sub-clubs and the col-umn ‘tests of club merging’ reports the fitted regression coefficient. The empirical find-ings display that for all sub-clubs there is no evidence to support mergers of the originalclubs.

Table 3 Convergence clubclassification

a Denotes statistical significant atthe 5 % level, while it rejects thenull hypothesis of convergence.Figures in parentheses denotet-statistics

Club Tests of club merging

1 Club 1 + 2 = −0.098a

(−5.24)

2 Club 2 + 3 = −0.126a

(−7.61)

3 Club 3 + 4 = −0.114a

(−5.28)

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Conclusions and policy implications

This paper tested the role of public health expenses for economic convergence across the 26Indian States spanning the period 1981–2005. To serve this objective, the novel methodologyof Phillips and Sul (2007) was used. This methodology used a non-linear factor model witha common and an idiosyncratic component—both time-varying, which allow for technicalprogress heterogeneity across States. In terms of per capita income, the empirical findingssuggest that the 26 Indian States do not form a homogeneous convergence club. A similarpicture is shown with respect to health expenses, implying a potential factor that could beresponsible for such a divergence behavior. In addition, there was not a clear and directassociation between public health spending and economic growth.

The implications of our findings are that increasing investment and spending in health isrequired either through direct intervention policies in the health sector or by increasing theeconomy’s income across States. At the same time, the divergence pattern in both variablesunder study recommends that governmental policies are a necessity to decrease any diver-gence patterns across States, by investing and spending more wherever is necessary, i.e. tominimize the gap of inequality distribution of health expenses among the Indian States. Addi-tionally, the presence of divergence indicates inadequate priorities to public health, probablydue to funding difficulties arising from huge fiscal deficits and/or from asymmetric distribu-tion of public funds across states. Thus, within such conditions it is more than apparent theengagement of the private sector as an additional instrument or a partner for achieving sharedpublic health outcomes. In this manner, both schemes will be running in a complementarymanner by strengthening the link between the consumer and the provider of health servicesas well as the link between the physician and the patient. Nevertheless, the government hasalso to develop the appropriate legislative and regulatory framework to formulate proceduresand regulations that avoid documented market failures.

The achievement of real convergence implies less costly adjustment processes as wellas lower fiscal costs. To this end, since convergence is not an automatic procedure, thelagged States should open-up their economy more heavily to knowledge-based productiontechnologies, to strategies that enhance the investment efforts in physical capital, to humancapital and infrastructural enlargement, as well as to regulatory and institutional changes. Allof these actions will definitely contribute to faster convergence process. The results couldalso imply that the uncertainties associated with the convergence process raise the issue of theefficiency of policies exercised either by the local governments or by the central government.A micro-economic analysis could reach higher-level conclusions.

The empirical findings have also critical implications for both the Indian insurance andhealth system. In particular, the results exemplify the merit of the institutional mechanismthat govern the post-retirement pension scheme by pointing to the need of accumulatingand administering the retirement-targeted savings and delivering the pension income onretirement, while reducing the cost and ease of access during pre-retirement life as well asthe quality of post-retirement services. The efficiency of handling that system will tend tofree resources necessary to be spent on the growth plan of the country.

At the same time, since the health expenses are measured as government expenses, ser-vices available from public health centers cannot be easily assessed vis-à-vis certain qualitystandards usually met by private health centers, yielding limited accessibility to public healthplaces with adverse effects on growth plans. To this end, a competitive and contestable pen-sion fund management and service industry are needed to improve both social awarenessabout savings for retirement and the access of large segments of population to professionaland qualitative health services. However, the ability to meet future health needs is signifi-

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cantly limited by the inherent constraints imposed on public entities to undertake portfoliomanagement activities and methods that enable private insurance schemes to achieve themarket returns that provide their advantages over the public systems. Therefore, the lastargument holds as a necessary condition certain developments in the capital market, i.e. theemergence of private debt markets along with their supportive infrastructure, the developmentof the equity market that could provide the potential for professional portfolio managementand diversification as well as the development of risk management techniques that wouldsupport the emergence of private funds. Moreover, the introduction of private participation toexpand coverage is expected to increase returns and improve efficiency with further positivespillovers to economic growth. A main challenge ahead will be to adapt and extend such anew financial landscape to enable a private system to flourish by improving the quality ofits products, by developing an efficient pricing model, by using different channels to reachout to the community and, finally, by regulating the services so that both high quality andreduced costs are ensured.

Overall, the country needs an organization that can understand the current healthcare sys-tem and can come up with practical solutions in order to generate quite a worthwhile venture,while a substantial reform should include the integration of private and public healthcareservices delivery as well as the establishment of a universal healthcare fund and the signifi-cant reduction in healthcare costs. The policy makers need to stratify the States into variouscategories and design healthcare schemes that are specific for each category, i.e. mandatorysocial health insurance, private health insurance, formation of medical savings accounts,among others.

Finally, this study points to a number of future research avenues involving exploring thedisaggregated pattern of health spending between rural and urban areas, between male andfemale population and across disadvantaged communities and regions.

Acknowledgments We thank Donggyu Sul for making the Gauss code available to us. A sample code can bedownloaded from Donggyu Sul’s homepage: http://homes.eco.auckland.ac.nz/dsul013/. The usual disclaimerapplies. The authors also wish to thank both a referee and the Editor of this journal for their constructivecomments and suggestions that enhanced the quality of the paper. Needless to say, the usual disclaimerapplies.

Conflict of interest None declared.

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Documents

Health expenses and economic growth: convergence dynamics across the Indian States