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Special article Economic & Political Weekly EPW august 2, 2008 41 Halth inqualty n inda: evdn from NFHS 3 William Joe, U S Mishra, K Navaneetham R ecent research has witnessed considerable engagement with the task o comprehending the crucial determinantso health outcomes. It is observed that the burden o illhealth is borne disproportionately by dierent populationsubgroups and that people o lower socio-economic status consist-ently experience poor health outcomes [Macinko et al 2003].Several empirical studies have also acknowledged such income-related inequalities in health, propounded as the absolute incomehypothesis [Kakwani et al 1997; van Doorslaer et al 1997:Humphries and van Doorslaer 2000]. In view o such ndings,health promotion o the poor has emerged worldwide as a vitalarea or policy research and action. Policy initiatives andprogrammes strongly perceive that inequalities in the healthoutcomes o dierent population subgroups are characterised by certain systematic deprivations (such as poverty). Apparently, some o the Indian health policies and program-mes also attempt to eliminate deprivation in the provisioning o healthcare and achieve the objective o health equity. 1 In order toachieve this objective, it is important to steer policymakingthrough timely and systematic assessment o prevailing healthinequality, 2 a task that so ar does not seem to have receivedserious attention. Although a ew studies have presented region-specic or population-subgroup-related health proles or India,they are at best able only to refect on disparities and not inequa-lities. While disparities are evaluated based on the positioningaround aggregate outcome, inequalities have to be adjudgedaccording to specic ethical or economic ideals. Moreover, orensuring equitable and ecient allocation o public healthresources, it is imperative to unravel the depth and the varieddimensions o health outcomes, especially through measuressensitised or equity concerns. Apart rom these considerations, itis also o analytical interest to examine whether income inequa-lity itsel poses as a public health hazard. This question hasgained much academic attention but most o the ndings o studies 3 on the topic have remained inconclusive. The literatureon health economics, which identies this question as the relativeincome hypothesis states that the distribution o income in asociety has a larger impact on population’s health than absoluteincome. Since most o the studies on relative income hypothesisare undertaken in the context o developed countries, it would be worthwhile to gather some insights rom the Indian experienceto urther our understanding o the income-health nexus.In this article, we employ widely accepted measurementtechniques to assess inequities in child health across dierentIndian states and draw some interesting conclusions on therelationship between income inequality and health inequality inthe country. As we all know, health o children assumes The authors appreciate the editorial eort o P R G Nair towards makingthe manuscript read better.William Joe (
[email protected] ), U S Mishra (
[email protected] ) andK Navaneetham (
[email protected] ) are at the Centre or DevelopmentStudies, Thiruvananthapuram, Kerala. This article utilises the National Family Health Survey-3data and presents an empirical assessment of income-related health inequality in India. It undertakesa state-level analysis of inequities in child healthby employing the widely accepted measures of concentration curves and concentration indices. Itfinds that the poorer sections of the population arebeleaguered with ill health whether in the quest forchild survival or due to anxieties pertaining to childnutrition. Further, an attempt is made to comprehendthe relationship between income inequality andhealth status in the Indian context. The analysis revealsthat the degree of health inequalities escalates whenthe rising average income levels of the populationare accompanied by rising income inequalities. Theincome-poor sections have different needs andtherefore, planning and intervention necessitatesan understanding of the sources of inequality andrecognition of the vulnerable groups to arrive at efficientresource allocation and policy decisions. Special article august 2, 2008 EPW Economic & Political Weekly 42 signicance or human and economic development o any country; but what is more important is to regard it as their rightto survival, protection, participation and development as ratiedby the government o India in 1989 through the Convention onthe Rights o the Child ( CRC ) drated by the United NationsCommission on Human Rights. Unortunately, most o the deathsrampant among children in India arepreventable and are caused by a combi-nation o under-nourishment and onslau-ght o inectious diseases. Although,child health and welare has been aprime item in the agenda o the centraland the state governments, their intentcannot proceed very ar in the absenceo a prior assessment o the magnitudeand varied dimensions o the problem.With the motivation to ll this void, therest o the paper is organised as ollows.Section 1 discusses the measurementtechniques employed to measure the magnitude o disparities inchild health status. Section 2 bries about the data sources andthe variables identied or the analysis. Section 3 presents theempirical ndings obtained or the income-related dimension o health inequality and section 4 attempts to interpret the resultstheoretically. Section 5 concludes the discussion and presents aew policy suggestions. 1 Mthods As we already argued, the assessment o health status could beimproved by adopting certain distribution-sensitised measuresalong the identied dimensions. In order to examine income-related inequality, we adopt the standard technique o employingconcentration curves and concentration indices. Underlying thistechnique is a simple but interesting principle o dening equity.The principle involved stipulates that the cumulative proportionso ill health must match with the cumulative population sharesand any mismatch between the two sets is dened as inequity.The concentration curve ( CC ) and concentration index ( CI ) havecertain attractive properties compared to certain other measureso health disparities [Wagsta et al 1991, Kakwani et al 1997] andare employed here as a means or quantiying the degree o income-related inequality in certain specic health variables.The CC plots the cumulative proportions o the population (begin-ning with the most disadvantaged in terms o income and ending with the least disadvantaged) along the x-axis against thecumulative proportions o ill healthplotted on the y-axis. For interpretativepurposes, i the burden o ill health wereequally distributed across socio-economicgroups, the CC would coincide with thediagonal. I poor health is concentratedin the lower socio-economic groups, thehealth CC would lie above the diagonal andthe arther the CC lies rom the diagonal,the greater would be the degree o inequality. The CI is dened as twice thearea between the CC and the diagonal.The CI provides a measure o the extento inequalities in health status that are systematically associated with socio-economic status. The CI can be easily computed by making use o the convenient covariance result [Kakwani 1980;Jenkins 1988; Lerman and Yitzhaki 1989] as ollows: CI = 2 cov ( H i , R i )/μ, where H is the health variable whose inequality is being measu-red, μ is its mean, R i is the i th individual’s ractional rank in thesocio-economic distribution and cov( H i , R i ,.) is the covariance.The CI ranges between +1 and -1 and takes negative values whenthe CC lies above the line o equality, indicating disproportionateconcentration o the ill health outcome among the poor. 2 Data and Varabls Notwithstanding the measurement techniques, availability o health inormation disaggregated by population groups becomescrucial in evaluating health inequities. Prior to the advent o theNational Family Health Surveys ( NFHS ), health inormation wasrestricted to aggregate measures and it had been dicult to study the distribution pattern o health in the population. The genesiso the NFHS , its wide coverage and the nature o inormationcollected oer an exclusive opportunity to employ robustmeasures to comprehend health disparities across the Indianunion. In order to portray the status o health deprivation in Indiaand its spatial and group related dispersal, we utilise the unitlevel records o the third and the latest round o NFHS (2005-06),conducted by the International Institute or Population Sciencesand ORC Macro. To retain the sensitivity o the health outcomeindicators, the domain o child health has been taken as a majorcriterion as it allows or better interventions right rom thepreliminary stages o lie. Thereore, in our analysis we engageourselves with child health outcome variables, across dierentstates o the Indian union. As the key indicators o child health, this paper employs theinormation available on under-ve mortalities, immunisationstatus and nutritional perormance (stunting and underweight)o the child population o the dierent states. For measuring theinequities in child undernutrition, we use the NFHS 3 inormation tabe 1: Defnons of chd Heah indaos Used Under-five mortality rate (U5MR) The number of deaths to children under five yearsof age per 1,000 live births. Figures are based onbirths during the five years preceding the survey.Stunting (H/A) Children whose height-for age is below minustwo standard deviations (-2 SD) from the medianof the reference population, are considered shortfor their age, or stunted.Underweight (W/A) Children whose weight-for-age measures arebelow minus two standard deviations (-2 SD)from the median of the reference population areunderweight for their age.Prevalence of anaemia(ANE) Children between six months and 59 monthsare classified as anaemic if the haemoglobinconcentration in them is found to be lower than11.0 g/dl.Not fully immunised (NFI) A child (12-23 months) is considered not fullyimmunised if the child has not received anyone of the following vaccinations, namely; BCG, ameasles vaccination, three DPT vaccinations, andthree polio vaccinations. Cumulative % of births ranked by economic status Fgue 1: Unde-Fve Moay conenaon cuves C u m u l a t i v e % o f u n d e r - f i v e m o r t a l i t i e s 1008060402000 20 40 60 80 100All IndiaMaharashtraGujaratMadhya PradeshEgalitarian Line Special article Economic & Political Weekly EPW august 2, 2008 43 provided on the basis o the new international reerence popula-tion released by World Health Organisation ( WHO ) in April 2006[ WHO Multicentre Growth Reerence Study Group 2006] andaccepted by the government o India [ IIPS and ORC Macro 2007]. All these variables are specically dened in Table 1 (p 42). Toocus attention on issues o association and causation, we haveobtained inormation also on three other economic variables:One, the state-wise net state domestic product ( NSDP ) 2004-05 atactor cost, which is obtained through the statistics published by Central Statistics Organisation ( CSO ). The second is the inormationon public spending on health as a share o total health spending, which is taken rom Rao et al (2005). In addition to these varia-bles we also required inormation on the income inequality levelsacross dierent Indian states. For this purpose we have used theunit level records o National Sample Survey’s ( NSS ) 61st roundon consumer expenditure. Here, the consumption expenditure o the households is taken as a proxy or income and we havecomputed the Gini coecient o inequality in per capita monthly consumption expenditure or all the states o India. 3 intrstat Dffrns n Halth inqualts In this section, we examine the magnitude o income-relatedinequalities in health, across the dierent Indian states. For thispurpose, we have computed the CI or the selected indicators o child health across all the Indian states (Table 2). The CI valuesor a range o child health indicators or the country as a wholeare negative, conrming the prevalence o income-related healthinequalities that are maniest primarily among the poor. Oncomparison o these inequalities across varied indicators o childhealth, inequalities are more pronounced in the case o the under-ve mortalities, in undernutrition and the receipt o basic vacci-nations or immunisation. The under-ve mortality CC or all-In-dia as well as or three other major states (Maharashtra, Gujaratand Madhya Pradesh) with higher health inequality levels areshown in Figure 1 (p 42). All these CC s lie above the diagonal andthus, indicate a greater concentration o health eventualitiesamong the poorer groups.While the CI value or under-ve mortality at the national levelis computed to be (-0.1582), it presents a reasonably wide rangeacross various states with the minimum o (-0.0388) in West Bengaland maximum o (-0.4107) in Uttaranchal. Among the othermajor states, Maharashtra, Madhya Pradesh, Gujarat, Tamil Naduand Punjab experience greater income-related inequalities in under-ve mortality as against the states o Uttar Pradesh, Rajasthanand Bihar, which show much lower levels o inequalities. Apartrom the dierences in the magnitude o inequalities across theboard, the negative values indicate vulnerabilities among the poor.Other than under-ve mortality, similar inequality is assessedor a set o child health indicators, which include nutritionalmake-up, anaemia and child immunisation. As regards nutritio-nal make-up, the two dimensions namely stunting (low height-or-age) and the underweight (low weight-or-age) maniestinequalities at the all-India level ranging between (-0.1249) and(-0.1600). The all-India level inequality in weight-or-age basednutritional assessment being the largest depicts a similar patternacross states as well. Compared with stunting, the inequality innutrition according to the underweight criterion has a widerrange between (-0.0835) in Madhya Pradesh and (-0.3063) inGoa. The level o overall prevalence o the same could also condi-tion a moderate range o inequality across states or the alterna-tive nutritional measures. This is obvious rom the act thatprevalence o undernutrition according to the underweight crite-rion is by ar the largest when contrasted with the same evalua-ted on an alternative criterion like stunting. Further, weight-or-age in its own construct has a propensity or larger variationduring childhood. As regards stunting the all-India concentrationindex value is (-0.1249) with a variation range o (-0.0325) inMeghalaya and (-0.2867) in Goa. Not only is the range o varia-tion in this inequality measure relatively lower compared to thesame according to the underweight criterion but also the highinequality magnitudes are lesser in this case.For the indicator o anaemia the all-India CI value o (-0.0518)is observably lower and could be due to the widespread preva-lence o anaemia across the population but still the poorersections are ound to remain at a higher disadvantage. Theinequities in child-anaemia do not vary signicantly across themajor states. However, the states o Mizoram (-0.1,363), Goa(-0.1,126), West Bengal (-0.0919) and Orissa (-0.0851) are oundto be more inequitable. In addition to these health outcome tabe 2: ci fo inequaes n chd Heah indaos States CIU5MR CIANE CIH/A CIW/A CINFI Andhra Pradesh -0.0704 -0.0367 -0.1311 -0.1650 -0.0963Arunachal Pradesh -0.1401 -0.0587 -0.1167 -0.1816 -0.1296Assam -0.0541 -0.0581 -0.1302 -0.1373 -0.1079Bihar -0.0882 -0.0389 -0.0861 -0.0962 -0.1340Chhattisgarh -0.0764 -0.0389 -0.0669 -0.1133 -0.1443Delhi -0.1835 -0.0666 -0.1313 -0.1410 -0.2079Goa -0.1282 -0.1126 -0.2867 -0.3063 -0.2893Gujarat -0.2198 -0.0658 -0.1127 -0.1432 -0.1542Haryana -0.1304 -0.0524 -0.1408 -0.1260 -0.3341Himachal Pradesh -0.2186 -0.0406 -0.1305 -0.1323 -0.1589Jammu and Kashmir -0.1656 -0.0169 -0.1690 -0.2258 -0.2341Jharkhand -0.0546 -0.0624 -0.0803 -0.0876 -0.1131Karnataka -0.1325 -0.0339 -0.1284 -0.1648 -0.1823Kerala -0.1274 -0.0314 -0.1628 -0.2026 -0.2719Madhya Pradesh -0.2081 -0.0406 -0.0683 -0.0835 -0.1810Maharashtra -0.2481 -0.0444 -0.1427 -0.1796 -0.1795Manipur -0.3458 -0.0097 -0.1409 -0.1805 -0.1975Meghalaya -0.1152 -0.0525 -0.0325 -0.0811 -0.0957Mizoram -0.1942 -0.1363 -0.1606 -0.2400 -0.2130Nagaland -0.1646 NA -0.1328 -0.1645 -0.1113Orissa -0.0844 -0.0827 -0.1865 -0.1811 -0.1328Punjab -0.1688 -0.0331 -0.2082 -0.2597 -0.2505Rajasthan -0.0801 -0.0198 -0.1043 -0.1337 -0.0898Sikkim -0.0581 -0.0171 -0.0848 0.0200 -0.0725Tamil Nadu -0.1749 -0.0346 -0.1463 -0.1936 -0.0523Tripura -0.2251 -0.0381 -0.1113 -0.1421 -0.2306Uttar Pradesh -0.0960 -0.0271 -0.0885 -0.1181 -0.0754Uttaranchal -0.4107 -0.0710 -0.1924 -0.1997 -0.2302West Bengal -0.0388 -0.0919 -0.1716 -0.1660 -0.1231All India -0.1582 -0.0518 -0.1249 -0.1600 -0.1595 The CI ranges between +1 and -1 and takes negative (positive) values when the ill healthoutcomes are concentrated among the poor (rich).CIU5MR- (CI) for under-five mortality, CIANE- CI for anaemia, CIH/A- CI for stunting, CIW/A- CI forunderweight and CINFI- CI for not fully immunised.Source: Computed by authors using NFHS 3 (2005-06) unit level records. Special article august 2, 2008 EPW Economic & Political Weekly 44 indicators, we have also tried to examine whether income-relatedinequities are present in the attainment o basic vaccinations, which is provided through the public health machinery. Apartrom the problem o lower rates o complete immunisation, thereare evidently higher income-related inequities inherent in thedistribution o non-immunised children across dierent states.Even in states with better coverage o primary healthcare (likeKerala), children belonging to poorer sections o the populationare at a greater disadvantage as the concentration o these incom-plete immunisations is higher among them. Undoubtedly, apartrom income, inequality in such outcomes is arising due to theinterplay o several actors including education and healthawareness and it would be an important and challenging task toprobe into inequalities obtained due to reasons other than theelementary issue o income deprivation. Ater providing a preliminary account o income-related childhealth inequality in India, we now turn to discuss the relationshipbetween health inequalities and income across dierent Indianstates. To acilitate the discussion, we have classied the die-rent states into our categories (Table 3). Employing the all-Indiagures o per capita NSDP (or income) and CI or under-vemortalities (or health inequality) 4 as a cut-o level, the statesare classied into “low income – low health inequality”, “lowincome – high health inequality”, “high income – low healthinequality” and “high income – high health inequality” onesdepending on whether their respective values exceed or all shorto the cut-o level. On this basis we are able to obtain vitalinsights into the relationship between themagnitudes o inequalities in health andthe state’s income prole. States like Punjab,Maharashtra and Gujarat demonstrate thecoexistence o higher levels o incomealong with higher levels o inequalities inunder-ve mortalities and states such asUttar Pradesh, Bihar and Orissa which havelower income levels are also ound to havelower levels o inequalities in under-vemortalities. These states suggest, that thereis a straightorward relationship betweenincome levels and health inequality. Butthere are other states such as MadhyaPradesh, Karnataka and Kerala, which are exceptions to such arelationship and suggest there is no such clear-cut relation.To probe urther, we bring in the element o income inequalitiesto comprehend the observed health inequities across these states.Inclusion o income inequality 5 into the analysis would signiy that the health inequality in a state is not only dependent on theoverall level o income but also on its distribution. This too is notsucient to explain the observed pattern o health inequalitiesacross dierent states. For example, consider major states such asKerala and Madhya Pradesh, which are exception to any suchdirect relationship. Both these states have higher levels o incomeinequalities coupled with varying levels o income and healthinequality (lower in Kerala and higher in Madhya Pradesh).Thereore, without asserting association any urther, we presenta simple model to better elucidate the expected relationshipbetween the two. 4 inom inqualty and Halth inqualty To understand health inequality through the income domain, weadopt a simple model discussed in Wagsta (2002, 1986). In thismodel, the relationship between health and medical care isassumed to be concave, meaning that medical care is subject todiminishing returns in the production o health. It suggests thatricher individuals are likely to end up with higher levels o healthand that increases in income inequality result in higher levels o health inequality. Further, it is inerred that i medical care issubsidised through public spending, it helps to lower the levels o health inequality. It also suggests increases in health inequality i rising incomes are accompanied by technological improvementsin healthcare. In order to veriy these predictions rom the model,an empirical analysis has also been attempted. This exerciseshows that neither income inequality nor the public share o health spending proves to be a signicant determinant o healthinequities but that average income o the population is signicantin determining the same. However, such ndings raise thequestion as to why these empirical ndings dier rom thetheoretical insights oered by the model. For instance, why arerising levels o income inequalities not accompanied with higherlevels o health inequality? In act, the interstate analysis to bediscussed later also provides us with similar results (Table 4, p 45).Does it imply that the supposedly strict concave relationshipbetween health and medical care is weak?To explore urther, we modiy theassumption o a strictly concave relation-ship between income and health andinstead work with a convex-concaverelationship (as shown in Figure 2)between income and health. This modiedassumption helps to elucidate relationshipbetween health and medical care expend-iture – particularly in a developingcountry – by capturing the indivisiblenature o health expenditure and tocomprehend health inequality in a strik-ing manner. What motivated us to engage with such a relationship are the acts thatin a developing country whenever an individual decides to seek medical care, his rst task would be to arrange or an array o healthcare-related expenses beginning rom travelling cost andmedical ees. Also, during the initial phases o the treatment process,oten, the individual is advised to undergo a ew diagnostic tests, tabe 3: cassfaon aodng o inome leves and Heah inequay Lower Health Inequality Higher Health Inequality Lower Arunachal Pradesh, Jammu and Kashmir, Madhya Pradeshincome Andhra Pradesh, Assam, Bihar, Manipur, Mizoram, Nagaland,Chhattisgarh, Jharkhand, Tripura, UttaranchalMeghalaya, Orissa, Rajasthan,Uttar Pradesh, West BengalHigher Haryana, Goa, Karnataka, Kerala, Delhi, Gujarat, Himachal Pradesh,income Sikkim Maharashtra, Punjab, Tamil Nadu For income, per capita NSDP (2004-05, at factor cost) and for health inequality, CI of under-fivemortalities are used with their all-India levels employed as a cut off mark to classify the statesinto low and high categories. Fgue 2: inome-Heah reaonshp H 2 HealthIncomeY 1 Y 2 Y 3 Y 4 H 1 H 3 H 4 Special article Economic & Political Weekly EPW august 2, 2008 45 which undoubtedly help detect the ailment accurately but impor-tantly require additional expenditure. It must be noticed that many such expenses are indivisible and unavoidable under conditionso eeble health systems as is the case in many developingcountries. Such specic diculties in accessing quality medicalcare provide inadequate (or lower) returns to health at low levelso income. This line o reasoning is conceived in terms o the initialconvex region o the income-health unction depicted in Figure 2.Under such a ramework, richer individuals are likely to endup with higher levels o health but it also suggests that individualincomes have to exceed a certain threshold (somewhere close to Y 3 ) to able to meet the initial expenditure requirements ormedical care in order to reap greater health benets. For instance,consider two individuals with incomes Y 1 and Y 2 respectively asshown in Figure 2. In the absolute sense, both these incomes arelow and thus, lead to low levels o health. But still, there exists acertain degree o income inequality between these individuals(absolute and relative income inequality, given by Y 2 – Y 1 , Y 2 / Y 1 )that leads to health inequalities (given by H 2 – H 1 , H 2 / H 1 ). It isimportant to note that the inequalities in health, both in absoluteand relative senses, are smaller than the inequalities observed inthe income distribution and suggest that at lower levels o income,health inequalities are also low. But i individuals are around thethreshold income level beyond which they would be able to aordbetter healthcare, then the relationship between income andhealth inequalities worsens. To demonstrate this act, considertwo individuals with incomes Y 3 and Y 4 respectively and allowor a considerable degree o income inequality between them(i e, Y 4 – Y 3 , Y 4 / Y 3 ). Here, unlike in the earlier case, we observethat despite similar degrees o income inequalities, the level o health inequality ( H 4 – H 3 , H 4 / H 3 ) has increased with increasein incomes.In a nutshell, the modication o the income-health unctionallows one to iner that or a given level o income inequality, i overall income levels are lower (higher) then health inequalitiesare also lower (higher). It also suggests that the levels o income inequality also have signicant bearing upon the extento health inequality but that the impact becomes more observablei the income inequalities are associated with higher levels o income. More importantly, under conditions o lower incomesand high-income inequality, the health inequality levels wouldget enhanced whereas i income levels are higher and incomeinequality levels are low, they would have a moderating impacton health inequality levels. Another related discussion that isrelevant here is the impact o public health spending uponhealth inequality. Although it is desirable that such acilitiesshould be distributed more evenly across the population, theactual result may be undesirable as health acilities providedthrough public health spending oten tend to be concentratedin particular regions such as urban areas or certain other target-locations thereby, oten ailing in guaranteeing universal access andopportunity. Any such bias in the provisioning o public healthcould thus worsen the distribution o health across individuals.In order to quickly veriy the predictions o these two dierentrameworks in the Indian context, a simple regression exercise isundertaken here. This analysis could also be viewed as apreliminary attempt to comprehend the dierences in healthinequality across the dierent states o India in terms o incomeinequality, per capita income and share o public health spending.We have selected the negative o the under-ve mortality CI as anindicator o child health inequality. As explanatory variables, theGini measure o inequality in per capita monthly consumptionexpenditure is taken as a proxy or income inequality, per capita NSDP at actor cost is utilised to represent the state per capitaincome and public spending on health as a share o total healthspending is taken to represent the role o subsidies in healthcare.The results rom the regression analysis are presented in Table 4.Model 1 shows that in the Indian context, income inequality ispositively but insignicantly related with levels o healthinequality. The R-squared value suggests that hardly 2 per cent o the variations in health inequality are actually explained by thedierences in income inequalities. This nding is similar to whatWagsta (2002) nds while comprehending the dierences inhealth inequality across developing countries. Given the inability o income inequality alone to capture the variations in healthinequality, we add other important variables to comprehend thecausation. Specically, in model 2 we control or income inequality and public health spending levels and thereby attempt to elicitthe role o per capita income in determining health inequalities.The results endorse the view that increases in average incomealso increase the levels o health inequality as indicated by thepositive and signicant coecient o NSDP per capita. Thetheoretical ramework discussed earlier has predicted thisrelationship. However, it is also observed that the coecientobtained or the variable o public health spending as a proportiono total health spending possesses a negative sign, suggesting itsavourable eect or reducing health inequalities. However, theeect turns out to be statistically insignicant.The overall results obtained here (in models 1 and 2), especially in relation to income inequality and average income, are partly inagreement with the ramework but do not lend any concretesupport to the relative income hypothesis. In other words, it may also be opined that the concave relationship between income andhealth is somewhat unable to capture the conditions prevalent indeveloping countries. Hence, now we go on to test the alternateramework namely o the convex-concave relationship between tabe 4: regesson resus fo cis of Unde-Fve Moay Variable Model 1 Model 2 Model 3Parameter Parameter Parameter(t-statistics) (t-statistics) (t-statistics) Constant 0.076 0.080 0.048(0.992) (0.920) (1.482)Gini coefficient 0.183 0.160(0.767) (-.0546)NSDP per capita 9.49E-06**(2.429)Public spending on health as % total -0.00012(-0.124)Avg of Gini and normalised NSDP per capita 0.211***(2.894)F statistic for model 0.588 2.438* 8.378***R-squared 0.024 0.249 0.267Adjusted R-squared -0.017 0.147 0.235N 26 26 25 *** Significant at the 1% level, ** significant at the 5% level.
