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Understanding needs and AscribedQuality of life through maternalfactors infant mortality dialecticSwasti Vardhan Mishraaa Department of Geography, Visva-Bharati, Santiniketan, WestBengal, IndiaPublished online: 24 Sep 2014.

To cite this article: Swasti Vardhan Mishra (2015) Understanding needs and Ascribed Quality oflife through maternal factors infant mortality dialectic, Asian Geographer, 32:1, 19-36, DOI:10.1080/10225706.2014.962551

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Understanding needs and Ascribed Quality of life through maternalfactors infant mortality dialectic

Swasti Vardhan Mishra*

Department of Geography, Visva-Bharati, Santiniketan, West Bengal, India

(Received 7 June 2013; accepted 2 September 2014)

In this paper Needs theory is being redefined as the foundation block of the Quality of life (Qol)structure and as a starting point in the lexicon of Qol philosophies. It is argued that theelementary and the most important characteristic to define Qol is always the needs-basedapproach through its merging with the means-end dialectic. Keeping this epistemologyintact, Ascribed Qol (aQol) is defined as that Qol which transfuses from mother to her childby meeting the needs of a mother and through that meeting the needs of her child. Also, thedisjuncture between global and local estimations is highlighted, reflecting on its implicationsfor policy prescriptions. Referring to MosleyChens framework for child survival,empirical study has been made for eight socially and demographically backward states(EAG (Empowered Action Group) states) of India to justify the idea of aQol. The OLStechnique and geographically weighted regression, using the data from the Annual HealthSurvey, 20102011, were used for conforming to the tenets of the framework. It isempirically argued that within the aQol frame mothers education has the most influencingrole in securing survival of her infant vis-a-vis institutional delivery and full antenatal checkup.

Keywords: quality of life; need; EAG states; geographically weighted regression; infantsurvival

1. Introduction

The idea of and concern for human well-being is riveting echoing since the times of Aristotle(Diener and Suh 1997; Forward 2003) or perhaps much before than the known history. The ideaof Aristotelian good life and living well (Forward 2003; Hagerty et al. 2001; Phillips 2006)percolated into the political arena of the 1930s and mirrored the material part of the Quality of life(Qol). However, it was during the second half of the twentieth century that the realization dawnedon the intellectuals and policy framers that apart from wealth, non-pecuniary dimensions of lifeform an intrinsic part of lifes quality or state of human well-being. This state is materialized withthe meeting of needs and wants (Smith 1977). However, to need and to want is different thedifference is that of urgency and intensity. A particular material possession (or in another instancea definite cognitive experience) can be testified both as a need and as a want by different crosssections of a society in different times. A need for one can be a want for the other. Needs areminimalist in nature; they exemplify a level beyond which an individual can compete anddelve into wants and have sophisticated Qol. Thus, meeting needs and achieving the resultant sat-isfaction (term used in the crudest of form rather than in utilitarian parlance) is the basic Qol.

2014 Hong Kong Geographical Association

*Email: swastivardhanmishra@gmail.com

Asian Geographer, 2015Vol. 32, No. 1, 1936, http://dx.doi.org/10.1080/10225706.2014.962551

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Lifes quality is ascertained, essentially at the basic level, from the feasibility with which thebasic needs are met and the amount of effort one has to put to procure those needs. In this regard,the individual who wants to attain maximum Qol tries to achieve the same with more feasibilityand less effort. For basic Qol, the quality is equated with feasibility and effort, and for sophisti-cated Qol (i.e. beyond the basic needs threshold) it is equated with life satisfaction and quantity ofsatisfaction or utility derived. Basic Qol is not empowered to differentiate between individualsbased on the satisfaction achieved due to its inescapable character. On the contrary, sophisticatedQol is non-mandatory and optional and so facilitates easy differentiation.

Taking the pre-eminence of needs theory in the Qol domain, the paper queries about thelinkage that exists in the form of Ascribed Qol (aQol) between a mother and her child. Thereexists a paramount correlation between a mothers attributes and her childs health (Caldwell1979; Mosley and Chen 1984), and this correlation is explicit in the flow of lifes qualitythrough two proximate determinants maternal factors and personal illness control actedupon by a socioeconomic variable individual-level productivity of the Mosley and Chens(1984) framework of child survival. aQol is, at the most elementary level, the Qol meted to aninfant by her mother through meeting the infants basic need of survival, as a corollary tomeeting the mothers own basic need of health and literacy.

The main objective of the study is to look for any relationship between female literacy, fullantenatal checkup, institutional delivery and infant survival in eight Empowered Action Group(EAG) states of India, as an exercise to test any existence of aQol. The objective of the studyis to test the intensity of relationship between the explanatory variables and explained variablethrough classical method of OLS at the state level and contemporary technique of geographicallyweighted regression (GWR) at the district level. Since, it is not possible for a global measure(OLS) to account for the spatial non-stationarity and local variance, a local technique (GWR)is deemed fit for the purpose. Also the significance of the relationship is tested, for both themodels, along with the significance of the model itself in case of the global measure.

The motive behind the study is to show how the disjuncture between a global and a localmeasure of correlation could induce widely separated policy prescriptions. Holding a classicalmodel as a background, it is argued that it holds true even if the estimations are changed fromglobal to local, but the sub-form of the model shifts accordingly. Quite tuning to the tenets of Eco-logical Fallacy, the study adorns a descriptive form, describing the interactions. And paves wayfor the policy framers to think at a local scale and meet the local needs.

The following section paraphrases the idea of Needs as documented by several ways ofknowing it, and its eminent role in Qol studies. Besides, the idea of aQol is explained in avery subtle yet simple way for easy understanding. The section that follows focuses on thecase under study, the type and source of data and the methodology used for understandingaQol. Research findings are reflected in the next section, which make way for the adoption ofaQol understanding, paving the way for the mothers education as the paramount force tocurtail infant mortality. Also the disjuncture between the global and local measures of ascertainingthe relationship is highlighted effectively. The paper concludes with the contribution this studymakes in understanding the classical model yet with a contemporary technique, which wasdeemed fit for the purpose. And, it is also reflected how using a local measure keeps up thespirit of local governance, if such is the endeavor of the state.

2. Needs, Qol and aQol: concepts and theoretical considerations

Defining Qol, despite being pervasively done, is elusive. Even though the number and scope ofQol studies has increased over time (Wish 1986 in Trksever and Atalik 2001, 164), the defi-nitions differ to such an extent that it is impossible to search for their conformance. Qol concepts

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adorn such characteristics as a consequence of its eclectic interpretation (Trksever and Atalik2001) and multidimensional construct (Cummins 1999; Snoek 2000).

Based on the categories of satisfaction a person wants to attain for enhancing his or her lifesquality Qol is divided into two broad categories Objective Well-being (OWB) and SubjectiveWell-being (SWB). OWB, also called material well-being (DAcci 2011), which concerns itselfwith tangible and material fulfillment of life. OWB is conspicuous and is equated with meetingneeds (Phillips 2006). On the other hand, SWB is concerned with subjective interpretation ofones life happiness and pleasure one gets. It depends upon the cognitive and psychologicalelements of an individual. Therefore, SWB besides being externally determined also gets influ-enced by the dispositional characteristics of an individual (Diener and Suh 1997, 202). SWBmirrors the hedonism (DAcci 2011; Phillips 2006) and gratification of an individual i.e. thesatisfaction of an individual with his life, life domains and community conditions and services(Sirgy 2011). Thus, it is quite explicit that without reaching the threshold of OWB, enjoyingSWB is not possible. Meeting needs is that threshold.

Needs approach is the most practical and argues for a minimum standard for attaining lifesquality. This approach precisely endeavors to secure three elementary needs literacy, healthand nutrition. The most important theory to reflect on this approach is Doyal and GoughsTheory of Human Need (Phillips 2006). Taking a cue from the basic needs approach and enlar-ging its view, Doyal and Gough posited that there are two types of needs basic or primary needsand intermediate needs. In the basic needs category they included: Avoiding serious harm andminimally disabled social participation (Phillips 2006, 87). And to meet these two primaryneeds, at the initial level, eleven intermediate needs have to be secured, which include, interalia, appropriate health care, secure childhood and appropriate education. They believe thatthese two goals or two sets of needs are essentially to be achieved for human emancipation(Phillips 2006, 86).

Acting both as a means and an end in itself, need-based Qol thought is a much delved intoperspective of human well-being. In this regard, Drewnowskis (Smith 1977) and Headeys(Sirgy 2011) ideas and their analogy to stock and flow concept looms large. Drewnowskisschema talks of two indices state of well-being index and level of living index. State ofwell-being is a condition of an individual at a certain point of time and depends upon the levelof living he is practicing. And to reflect on the paramountcy of needs, health and education areincluded in both of his indices. That means to have a healthy state of existence an individualmust be practicing life in a healthy manner. Again, following Headey implicitly, Level ofliving of an individual equates as a stock concept which determines the flow concept/state ofwell-being of the individual. However, when Headey talks clearly of stocks and flows, he doesnot reflect on the stock-flow dialectic as underlined above. Instead what seems explicit is the dis-crete categorization of Qol into two categories. However, the satisfaction or dissatisfaction fromeveryday life (a flow concept) tangentially depends upon the characteristics of an individual(stocks health, education, social networks, leisure skills, work skills, etc.).

Health and literacy, essentially, are the basic needs as exemplified by the measures of humandevelopment index, physical quality of life index (Roy 1985) and several others (for some inter-esting projects see Hagerty et al. 2001). For considering health in the study I have taken the dataon full antenatal checkup, institutional delivery and infant mortality rate (IMR). Here IMR servestwo purposes: first, it is a single largest category of mortality (Singh 2007), and, second, it servesthe idea of aQol. To know how all these indicators interact, it is essential to revert back to one ofthe oft-cited theories on child survival Mosley and Chens (1984) framework. The frameworkstates that child survival depends upon five proximate determinants maternal factors, environ-mental contamination, nutrient deficiency, injury and personal illness control. But these determi-nants do not operate independently; rather they depend on the independent socioeconomic

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variables. These variables act through the proximate determinants to influence child survival.Independent variables are three in number individual-level productivity, household-level pro-ductivity and community-level productivity. Out of these three, individual-level productivity(IP) entails maximum command over the proximate determinants and eventually over theIMR. As it constitutes different facets of parents including the education, IP is considered thestrongest of the independent variables to reflect on the health status of a child. Out of IP,mothers education solely exerts such an overwhelming influence that Mosley (1983) has ident-ified the process as Social Synergy (in Mosley and Chen 1984, 35).

The present study focuses on how a socioeconomic determinant of Mosley and Chens (1984)framework individual-level variable (that of a mother) works through the proximate determi-nants of maternal factors and personal illness control to secure the life of an infant. Personalillness control is an instrument of resilience, which ascertains that the mother is kept healthythrough preventive measures during gestation and during parturition, or otherwise cured tohealthy being through curative means. And as mentioned earlier maternal factors are the para-mount force to secure infant survival through multiple means education, health and age at mar-riage of the mother among others. Full antenatal care (FANC) and institutional delivery (IN.DEL)fits under personal illness control determinant, whereas female literacy (working as a proxy forfemale education) is confined as a maternal factor (LIT), and in fact the most important.However, all the three explanatory variables are solely mother centric and limit or enhance thepossibility of infant survival in what can be termed as one-to-one strategy of survival.

Female literacy (education) is the most important variable to influence infant survival (Cald-well 1979) having number of ways of influencing it. Female literacy affects the other two vari-ables in question institutional delivery and full antenatal care as education endows amother with discretionary power to choose the best among the alternatives, be it modern medi-cine, right dosages of iron and folic acid, going for institutional delivery or postpartum care(Gunasekaran 2008; Mosley and Chen 1984). Also, quite often the mothers education bringsadditional resources to the family through her engagement in economic opportunities (Gunase-karan 2008).

Education also provides the power of rationale and economic thinking and helps in settingpreferences for a child, gauging the associated cost and contemplating possible trade-offs.Besides, as Caldwell (1979) puts it female education helps in favorable shift of intra-householdpower relations, enabling her to take conducive decisions for her childs survival against the per-vasive stereotypical norms and traditions. Jejeebhoy has called it womens decision makingautonomy (Kravdal 2004).

A plethora of research has been conceived, drawing on the literacyIMR linkage; thoughregional in nature they tend to conform to the hypothesis of strong negative correlationbetween mothers literacy and childs survival in the first year of birth (Anand et al. 2000;Franz and FitzRoy 2006; Kateja 2007). However, there are few other studies which have not con-sidered the explanatory role of literacy in determining the level of IMR (Nubler 1995 in Hagertyet al. 2001; Shimouchi, Ozasa, and Hayashi 1994). While considering education as the paramountSocioeconomic status (SES) of mothers that influences child survival, Fuentes-Afflick and Hessol(1997) posited that low SES of mothers increases the IMR (in Frisbie 2005). On the contrary, afew authors (Hummer, Eberstein, and Nam 1992 in Frisbie 2005) think that the absence of familyincome data in the vital statistic records induces substituting the absence with data on motherseducation.

To contradict the belief that family income is relatively more influencing on IMR than themothers education, Tresserras et al. (1992) concluded that income cannot be accepted as theexplanatory variable of child survival, as the unequal distribution of income produces a falseimpression (cited in Watters 2003). Thence, Tresserras et al. (1992) and Bolam et al. (1998)

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proposed to study of IMR through the status of female education as the most reliable (as cited inWatters 2003).

Institutional delivery (IN.DEL) is another considerable factor to determine infant survival. Itbrings into question the hygienic and appropriate setting of parturition, and tries to control theexogenous factors negatively affecting the infants life. Exogenous factors are the environmentalsettings and processes which affect infant mortality, as against the endogenous factors which actwithin the body of a mother. Antenatal care influences endogenous factors. Antenatal care pro-tects a mother from injury, infection and infirmities and at the same time supplements her withrequired nutrients, so as to keep both the persons healthy during gestation and at the time of par-turition. Postpartum care is as crucial as antenatal care but since the data on institutional deliveryincludes the postnatal care information (Annual Health Survey 2012), it is excluded for avoidingredundancy.

There exists a biological link between a mother and her child (Mosley and Chen 1984). Thusthe characteristics of a child are dependent upon that of the mother. Taking a cue from the afore-said paragraphs it is explicit that mothers Qol (literacy and health) dictates the Qol of her child(health and survival). And, in none of the other human relations is such a direct influence of apersons Qol on another beings Qol evident which is biologically, socially, economicallyand literally administered. This Qol of a child being dictated by the mothers Qol is what Ipropose to call as aQol. Since none of the Qol in any of the senses can be so explicit and axiomaticlike that of a motherchild transfusion, aQol is only endemic to such a relationship. aQol is thequality of life meted to an infant by her mother through biological, economical, social and literalmeans by meeting the infants basic need of survival, as a corollary to meeting her own basic needof health and literacy (Figure 1).

Figure 1. The aQol idea.Source: Author.

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Biological mechanism of aQol forms the all-inclusive mechanism. It axiomatically includesthe social, economical and literal mechanisms of Qol transfusion. A fetus is attached to themother through the placenta a tube made up of cells. This conduit feeds and sustains thefetus by getting the required nutrients from the mother. Though it is also established thatthe exchange is bi-directional and sustainability is mutual, the transfusion from a fetus to themother is supplementary while that from mother to fetus is exclusive and hence more crucialfor life. Therefore, this channel is the only source of securing a healthy infant during birth.Furthermore, a favorable power relation in a society and economic liberty reinforces the biologi-cal mechanism of infant birth and growth. How such conflation works are elementary and para-phrased in the next paragraphs.

Economical mechanism is about taking independent economic decisions by mothers for thesurvival of the infant. An educated female can only help in securing her infants survival if shehas access to sufficient resources and have the liberty to utilize them in the best possible alterna-tive. Having quality food, sufficient health supplements, opting for the best healthcare facility andpostpartum care including infant and child immunization are the best available alternatives havingintense economic connotations.

Societies, especially in the South Asian realm, have unscientific traditions and ways of lifewhich turn out to be fatal for both the infant and the mother. In this context the role and practicesassigned to females (especially those who are lower in the family hierarchy, for example, daugh-ter-in-law) are at times abusive for the survival of the fetus. As mentioned earlier only educationcan shift the intra-household power relations in favor of a mother and in that process in favorof the infant. Whether it is to feed an infant with the colostrums in the first hour of birth or tonegate the custom of being secluded after birth, an educated mother is expected to voice allthese, against the social oddities.

While the former mechanisms implicitly enhance the probability of infant survival, literalmechanism is more ocular. When a mothers action, which is to a large extent a cumulative ofthe last three mechanisms, is visible through implementation it can be called as the literal mech-anism. For instance, a mother watching her steps so as not to hurt the fetus, keeping tabs on hermedicinal schedules, sleeping and relaxing in a correct posture and maintaining hygienic sur-roundings can be called the literal mechanism of Qol transfusion.

3. Case, data and methodology

EAG states are the eight states of India showing heightened socio-demographic backwardness.Constituted in 2001, the states of Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Rajasthan,Odisha, Uttar Pradesh and Uttarakhand (Figure 2) have formed EAG in the Ministry of Healthand Family Welfare, Government of India.

The concern behind is to facilitate area-specific programs, and alter the effects of ill-govern-ance and bad monitoring system through effective community involvement.

EAG states have high records in some crucial variables of Biodemography, for instance,Infant Mortality Rate, Maternal Mortality Rate, Population growth, Total Fertility Rate and Ante-natal care. However, the negativities do not only mirror nonexistence of prompt health care facili-ties or fund unavailability; but are also fallout of ill-governance and non-conducible monitoring ofthe implemented programs. To be precise, the lowest marks are dependent upon both the demandside and the supply side of the services, which had been mutually sustaining each other at thelowest levels. Pluralistically burdened from many facets of society, environment and traditionthe general conception of the target group in these states has always had a cynical angle tomodern medication and health services. To compliment, the lethargy and unaccountability ofthe service providers have also fueled the situation to a certain extent.

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The core tenet of the EAG states policy is to stabilise population, where provision of qualityhealth infrastructure forms the means to attain the said end. As a result, of late, the states seem tobe moving in the same direction as evident from latest census data (Census 2011) populationgrowth rate in the EAG states has decreased by 4% (average) points for the very first time, theexception being Chhattisgarh where the population growth rate has rather increased from thelast census.

The EAG states contribute to around 46% of Indias total population, and the rest by 20 statesand 7 union territories accommodate the remaining (Table 1).

Infant mortality rate (IMR) of the EAG states fluctuates between 36 and 59 where all the statesexcept two have rates at par and above the national level by a good margin. The same situationsurfaces with regard to total fertility rate (TFR) where only Odisha rate is below the nationalaverage. In fact Odisha has reached replacement-level fertility. Regarding the percentage of

Figure 2. The EAG states, India.Source: Author.

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effectively protected couple (Effective couple protection rate (CPR)), the situation in Bihar is theworst 16.5% juxtaposed to 40.4% of the national average. Only two states Rajasthan andMadhya Pradesh are above the national mean. In the case of maternal mortality ratio (MMR)all the states are above the national average (212). Nevertheless, the decline in MMR is remark-able in these states; dropping from 375 in 20042006 to 308 in 20072009. The decline in thenational average during the same time span is from 254 to 212.

For the present study data were procured from the first round of the Annual Health Survey(AHS) (20102011), conducted in eight EAG states (Bihar, Chattisgarh, Jharkhand, MadhyaPradesh, Odisha, Rajasthan, Uttar Pradesh and Uttarakhand) and Assam. AHS is the first of itskind in India which reflects on the district-wise core indicators of fertility and mortality. At theinitial stage it was planned for the aforementioned nine high-focus states, which account for48% of the countrys population (Census 2011). AHS being a panel survey facilitates easy tem-poral comparison of the inter-district health scenario. However, in this study only 8 EAG stateswere selected, which comprises 261 districts. Exclusion of Assam is justified on account of itsnon-proximity to other states and therefore its inability to participate and go through spatial stat-istics treatment.

Approximately 3.5 million ever married women (1549 years) were surveyed in these eightstates and the criteria for sample size at the district were ascertained by looking at the IMR valuesand considering other practical issues related to the execution of the survey. AHS classifies thedata based on residence (rural or urban) but the data used for the study are non-categorized (total).

Female literacy rate data are taken from Census 2001, which is the percentage of females whocan read and write with understanding in any language. Infant mortality rate is the number ofdeaths of infants under one-year-old in a given year per 1000 live births in the same year. Inall the institutional delivery data, postnatal care is not registered; in the cases where thewomen stayed for 48 hours or more it is presumed that postnatal care was given. Full antenatalcheckup consists of three visits of antenatal checkup, at least one tetanus toxoid injection, andIron and Folic Acid tablet consumption for 100 days or more.

The classical method of OLS is run for getting the correlation coefficient value. And t-testvalue is estimated to obtain the significance of the relationships. Besides, f-test is run forjudging the significance of the model. Confidence level for both the tests is kept at 95% or more.

The value of r as generated using OLS identifies a single value to represent a relationship uni-formly all over the space (including the subunits). However, social processes are non-stationary(Fotheringham, Charlton, and Brunsdon 1998), i.e. they are not constant over space like physical

Table 1. Selected estimates of EAG states compared to the national average.

States Population % of total IMR MMR TFR CPR

Bihar 104,099,452 8.60% 44 261 3.5 16.5Uttar Pradesh 199,812,341 16.51% 57 359 3.3 27.7Rajasthan 68,548,437 5.66% 52 318 2.9 45.7Madhya Pradesh 72,626,809 6.00% 59 269 2.9 46.4Odisha 41,974,218 3.47% 57 258 2.1 25.9Chhattisgarh 25,545,198 2.11% 48 2.7 Jharkhand 32,988,134 2.73% 39 2.8 Uttarakhand 10,086,292 0.83% 36 India 1,210,569,573 44 178 2.4 40.4

Sources: Population figures Census of India, 2011; MMR Special Bulletin on Maternal Mortality in India 20072009Sample Registration System, June 2011; CPR Family Welfare Statistics in India 2011; IMR Sample RegistrationSystem Bulletin, October 2012; TFR Sample Registration System Statistical Report 2012.

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processes. Hence, it is a fallacy to represent them through a generalized single value of the stat-istic. Second, different social processes play differently in spatial subunits; thus the relationshipsare intrinsically different across space (Fotheringham, Brunsdon, and Charlton 2002, 9). Third,the generalized model, which dictates a relationship, may omit the crucial variable (s) or representthe relationship in an incorrect functional form (Fotheringham, Brunsdon, and Charlton 2002,10). Keeping the above contentions in sight, it seems that the single value of the coefficientsmoves toward an ecological fallacy. To curtail this fallacy, Fotheringham, Brunsdon, and Charlton(2002) have introduced a new local measure of Regression GWR. The difference between thelocal and the global is simple a global measure tends to generalize, in this case the relationshipbetween the variables, but a local measure disaggregates this relationship in accordance with thespatial subunits under study. A local measure also highlights the anomaly of a single units valuewith respect to that of the global measure. Clearly, a local measure, taking into consideration theidea of spatial non-stationarity of a process, generates local values of a parameter. For instance,where OLS generates a single value of r, GWR generates the same for every spatial unit underconsideration. In this regard, GWR conducts Regression equation on every single spatial unitand generates estimates for each of them.

The GWR model is written as (Fotheringham, Brunsdon, and Charlton 2002)

yi = b0(ui, vi) + Skbk(ui, vi)xik + 1i,

where (ui,vi) denotes the coordinates of the ith point in space and k (ui,vi) is a realization of thecontinuous function k (u,v) at point i. Keeping in view the unequal size of the districts the kernelused is adaptive in nature.

Since GWR does not provide for the f-test values for single subunits the significance of themodel is assumed to exist for the study.

4. Research findings

4.1. Ordinary least square

Ordinary least square (OLS) regression for all the EAG states combined (Table 2) shows full ante-natal checkup (r =0.426) as more important an explanatory factor for lowering infant mortalityrate than female literacy (r =0.349).

However, institutional delivery turns out to be insignificant for the relationship (p = .458).Also, both the explanatory terms account for more than 22% variance in IMR. Overall, themodel aQol fits well in case of EAG states combined together. Fragmenting the states for individ-ual state-wise calculation of the statistic brings into light the prominence and intensity of themodel under consideration. What we have come across are the problems of Modifiable ArealUnit Problem (MAUP) (Openshaw 1983) and ecological fallacy surfacing explicitly. Since wehave changed our unit of estimation on the same sets of data from a cluster of states to individualstates our estimates have changed a lot. Besides, the statistic which was dictating the nature of themodel for the cluster does not fit well for the individual states.

The model is not significant for the two states of Chhattisgarh and Odisha. The significancelevel of both the intensity of relationships (r) and the model is clearly absent in the two states.Odishas demographic paradox (Pradhan and Arokiasamy 2006) is mystifying. It has got thesecond-best records among the EAG states in the dimensions of full antenatal checkup(18.5%), institutional delivery (71.3%) and female literacy (68%), yet is tabled at the thirdhighest position in IMR estimates (62). The paradox is tangentially related to the motherchildinteraction, for Odisha has got very high rates of underweight married women (39.5%, where

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all India average is 32.2%) in the age bracket of 1549 years (Sengupta and Syamala 2012).Though malnourishment is attested as one of the prominent reasons of infant mortality, it isstill less influential than female literacy and antenatal checkup. Pradhan and Arokiasamy(2006) subscribe to the fact that such a paradox is fallout of lowered investment in health-careinfrastructure and low social sector development, like poverty, nutrition and rural development.

As for Chhattisgarh, a factor other than those included in the model, malnutrition, seeminglyplays a role in influencing IMR (39.7% of married women in the age bracket 1549) (Senguptaand Syamala 2012). Apart from that health infrastructural gap (Palmer and Kollannur 2010), largetribal population, insufficient public transport cumulatively influences IMR more than a mothersown Qol in Chhattisgarh. Nevertheless, Chhattisgarh has the third lowest IMR value (53) in theEAG cluster and has shown immense improvement from 76 in 2001 (Registrar General 2002) to48 in 2011 (Registrar General 2012). Credit for such a remarkable feat is the state governmentsown intervention Nava JatanYojana and Mitanin program.

Regarding other states, IMR in Jharkhand is most influenced by literacy (r =0.812) fol-lowed by Rajasthan (0.685), Uttarakhand (0.672) and Uttar Pradesh (0.636). The least influ-enced states are Bihar (0.540) and Madhya Pradesh (0.427). And when related to the other two

Table 2. Selected outcomes of the OLS model.

States VariablesRelationship strength with

IMR (R)Relationshipsignificance R2

Modelsignificance

EAG states IN.DEL 0.006 Insignificant 0.227 SignificantLIT 0.349 SignificantFANC 0.426 Significant

Bihar IN.DEL 0.479 Significant 0.282 SignificantLIT 0.540 SignificantFANC 0.384 Significant

Chhattisgarh IN.DEL 0.217 Insignificant InsignificantLIT 0.280 InsignificantFANC 0.288 Insignificant

Jharkhand IN.DEL 0.773 Significant 0.621 SignificantLIT 0.812 SignificantFANC 0.614 Significant

MadhyaPradesh

IN.DEL 0.478 Significant 0.268 SignificantLIT 0.427 SignificantFANC 0.285 Significant

Odisha IN.DEL 0.180 Insignificant InsignificantLIT 0.053 InsignificantFANC 0.055 Insignificant

Rajasthan IN.DEL 0.243 Insignificant 0.435 SignificantLIT 0.685 SignificantFANC 0.393 Significant

UttarPradesh

IN.DEL 0.532 Significant 0.425 SignificantLIT 0.636 SignificantFANC 0.443 Significant

Uttarakhand IN.DEL 0.163 Insignificant 0.651 SignificantLIT 0.672 SignificantFANC 0.040 Insignificant

Notes: Relationship significance is ascertained where p .05. Model significance is ascertained where p .05 for the f-testconducted. The value of coefficient of determination (R2) is adjusted after taking into consideration the number ofexplanatory terms, and in most of the cases it is smaller than the R2.IN.DEL, institutional delivery; LIT, female literacy rate; FANC, full antenatal care.Source: Author.

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variables, literacy is the most influencing in all the states except in Madhya Pradesh. A consider-able performance in this regard is shown by Uttarakhand where two explanatory variables insti-tutional delivery and full antenatal checkup have insignificant influence on the IMR scenario.Literacy exclusively dictates around 65% of the variance in IMR in this state.

Barring two indifferent states, Odisha and Chhattisgarh, in none of the states is literacy insig-nificant for the relationship. Institutional delivery shows good influence over IMR in the states ofJharkhand (r =0.773) and Uttar Pradesh (0.532), while not performing well in Rajasthan(0.243) and Chhattisgarh (0.217). Inconsistent with the EAG cluster statistic, full antenatalcare shows least influence on IMR in all the states but Rajasthan.

The coefficient of determination (R2) value ranges between 0.282 in Bihar and 0.651 in Uttar-akhand. In all the states except two (Bihar and Madhya Pradesh), the explanatory variablesaccount for more than 40% of the variance in IMR. Thus, employing a classical method of esti-mating a relationship it is fruitfully argued that maternal factors have substantial influence oninfant survival, which helps in explaining the transfusion of Qol from mother to her child.

4.2. Geographically weighted regression

As a continuation of MAUP and ecological fallacy, a local regression model (GWR) is employedto understand the statistic nuances in subunits (districts), and how the relationship fits or unfits inthe local units of study. Model significance in the local measure is not possible, so it is assumedthat the model fits well as it fitted in case of OLS estimates for the EAG cluster. Also, R2 is notestimated for the districts separately, as correlation coefficient mirrors the same.

The intensity of the relationship between IMR and FANC is insignificant in 151 of 261 dis-tricts (Figure 3). And out of the significant districts, four districts three in Chhattisgarh and onein Madhya Pradesh have shown a positive relationship. In these four districts maternal factorsplay a less significant role, whose effect is masked by other variables infrastructural gaps, tra-ditional prejudices detrimental to safe practices and early marriage. In all the three districts ofChhattisgarh the percentage of early marriage (below the legal age of 18 years) among thefemales is above the state average of 6%. However, in the district of Madhya Pradesh early mar-riage among females is more than double (28.6%) the state average of 12.5%. The most intenseIMRFANC relationship is estimated for Pilibhit district in Uttar Pradesh (0.929), while theleast performing district is Madhepura in Bihar (0.426). To argue for female education as themost important way of securing infant survival, 79 districts out of 151 insignificant ones (inthe IMRFANC interaction) reflect a strong relationship between IMR and female literacy.

The linkage shows strong clustering, with most of the values above 0.7, in the states of UttarPradesh, Uttarakhand, part of Bihar and Jharkhand and western Madhya Pradesh. Differing fromthe OLS estimates the linkage is insignificant in Rajasthan; only four districts have a significantrelationship.

IMR-IN.DEL linkage estimates showmore dismal figures only 90 out of 261 districts have asignificant relationship (Figure 4). Out of which 15 have a positive relationship most of them(10 districts) are located in Odisha and Chhattisgarh and the remaining scattered in the three statesof Jharkhand, Madhya Pradesh and Rajasthan. The relationship is mostly clustered in westernUttar Pradesh and central and south-western Madhya Pradesh. The most intense relationship isestimated for Bareilly in Uttar Pradesh (0.882) and the least intense estimate is for Bhagalpurin Bihar (0.428). In 171 insignificant districts (in the IMRINT interaction), 92 districtsshow an intense relationship between IMR and female literacy.

IMRLIT estimates are the strongest to reflect on the aQol idea (Figure 5). 161 districts out of261 have a negative relationship between both the variables. And none of the significant districtshave any positive value. Thus, female literacy has a universal influence in enhancing the chances

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of infant survival. Odisha and Chhattisgarh have only a few districts to reflect on the relationship 7 out of 46 districts. The clustering is observed in western Uttar Pradesh, Uttarakhand, MadhyaPradesh, eastern Jharkhand and Bihar and Rajasthan. In some districts of western Uttar Pradeshand nearly the whole state of Uttarakhand the value is above 0.800. In 25 districts out of 100insignificant ones full antenatal checkup helps in explaining the aQol, and in only 2 districtsdoes institutional delivery do the job.

Individual states have a lot to mirror about their interaction scenario per se (Table 3). Forinstance in Bihar the highest number of negative relationship is in the IMRFANC interaction 19 districts out of total 37! Also negative relationship is observed for 18 districts in case ofthe IMRLIT interaction and for the IMRIN.DEL interaction it is only 7 districts. When

Figure 3. Infant mortality rate and full antenatal checkup interaction.Source: Author.

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juxtaposed to the OLS values the picture is opposite; there INT-FANC has the least influence.Only five districts Bhojpur, Buxar, Patna, Saran and Vaishali have shown a significantrelationship in all the three interactions, and all the relationships are negative.

In Chattisgarh only three districts show significant status in the IMRFANC interaction butwith a positive relationship. Five districts that are significant in the IMRIN.DEL interactionare all positive, as well. But in the IMRLIT interaction all the three significant relationshipsare negative. Only the district of Korba shows a significant relationship in all the three inter-actions, but only negative in the IMRLIT interaction, while the other two are positive. In Jhark-hand 11 significant negative relationships are observed in the IMRFANC relationship, while the

Figure 4. Infant mortality rate and institutional delivery interaction.Source: Author.

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number is 13 in case of the IMRLIT interaction. Out of three significant IMRIN.DEL relation-ships, two are negative. Only 2 districts (Kodarma and Ranchi) out of 18 show significance in allthe three interactions. However, Kodarmas significance in IMRIN.DEL is positive.

Out of 45 districts only 13 in Madhya Pradesh show a significant relationship in all the threeinteractions, and these are negative. The districts are Barwani, Bhopal, Dewas, Dhar, East Nimar,Harda, Hoshangabad, Indore, Jhabua, Narsimhapur, Raisen, Sehore and West Nimar. The IMRLIT interaction is more intense in this state with 38 of the districts having a negative significantrelationship, while it is 23 in case of the IMRFANC interaction. And only 15 have shown this

Figure 5. Infant mortality rate and female literacy rate interaction.Source: Author.

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Table 3. Selected outcomes of the GWR model.

StatesNo. ofdistricts

IMRFANC IMRIN.DEL IMRLIT No. ofdistrictshaving

significantvalue in allthe threevariables

No. ofdistrictshaving

significantrelationship

No. ofdistrictshavingnegative

relationship

No. ofdistrictshavingpositive

relationship

No. ofdistrictshaving

significantrelationship

No. ofdistrictshavingnegative

relationship

No. ofdistrictshavingpositive

relationship

No ofdistrictshaving

significantrelationship

No. ofdistrictshavingnegative

relationship

No. ofdistrictshavingpositive

relationship

Bihar 37 19 19 7 7 18 18 5Chattisgarh 16 3 3 5 5 3 3 1Jharkhand 18 11 11 3 2 1 13 13 2MadhyaPradesh

45 24 23 1 18 15 3 38 38 13

Odisha 30 1 1 4 4 4 4 Rajasthan 32 4 4 4 3 1 24 24 UttarPradesh

70 38 38 44 44 47 47 25

Uttarakhand 13 8 8 2 2 13 13 Total 261 108 104 4 87 73 14 160 147 46

Note: Relationship significance is ascertained where p .05.Source: Author.

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status in case of the IMRIN.DEL interaction. Three districts in the IMRIN.DEL interaction andone in the IMRFANC interaction have a positive relationship.

None of the districts in Odisha and Rajasthan have significant status in all the three inter-actions. However, in both the states female literacy has got a significant influence over IMR.And, in both the states the IMRIN.DEL interaction has one or more districts showing positiverelationship. In Uttar Pradesh, the aQol seems to follow a complete circle. With nearly thesame number of districts in all the three interactions having a significant negative relationship,it can be assumed that all the explanatory variables seem to be working at definite spaces tobring down the IMR. Owing to its largeness, different districts of Uttar Pradesh seem to be inter-acting with different spatial forces influencing from different directions (following Toblers(1970) logic), and thus no predominant explanatory variable can be held accountable. In Uttarak-hand all the districts (13) show a significant negative relationship in the IMRLIT interaction, and8 districts and 2 districts in the IMRFANC and IMRIN.DEL interactions, respectively.

OLS has reflected on the relationships but when GWR was run the prior estimations wereproved wrong for some cases, and hold correct for some others. But when GWR estimationswere segregated state-wise, more interesting conclusions have surfaced vis-a vis OLS estimations.The unit-level estimations and one variables eminence over the other two point to differentialneeds of the districts that could guide policy recommendations in some fruitful direction. It isobserved that the significance in all the three variables interactions is found in only few of thedistricts, while majority of the districts have one or two significant causal factors. And thesenuances at the local level are what policy framers can capitalize on for framing parochial policiesor service provision. Or in another case they can endeavor toward making the insignificant vari-able significant through effective service delivery mechanisms.

The mechanism of Qol transfusion is estimated to be working well for nearly all the socio-demographic backward districts in the EAG cluster in one way or the other. Only 73 districtshave not reflected on the aQol idea most of which are in the states of Chhattisgarh andOdisha (totalling 38 districts). None of the districts in Uttarakhand have strayed from the ideaof aQol. Besides, the relationships are spatially visible in clusters, bringing afresh Geographysown first law that everything is related to everything else, but near things are more relatedthan distant things (Tobler 1970).To be precise, any practice (beneficiary oriented) or any inter-vention (policy oriented) in any specific district or group of districts has influenced the relatedscenario of the proximate districts and, following the Gravity model, decreases its influencewith distance.

5. Concluding remarks

Meeting need is essential it is essential for having a flourishing life and for enjoying the heavy-weight Qol. The idea of aQol is about meeting the needs of a mother and in the process meetingthose of her child. Though it has now become a clich to speak of motherchild interactions inpopulation sciences, it has not yet been defined from the humanistic perspective of Qol. Apartfrom that, any estimation that had been given has got a generalized picture to present and thesubunit nuances of estimation have been seldom tried. It has serious policy implications andworks as a road map in ensuring development from below. Since its 73th and 74th constitutionalamendments India is keeping herself tied to the principle of local governance; and a generalizedpicture is sure to bring failure to her attempts. It is in this regard that the study is paving the wayfor addressing local hitches in achieving development, including the most coveted millenniumdevelopment goals.

In the study, drawing from MosleyChens framework, the survival of infants is found to beinfluenced by maternal factors, among which full antenatal checkup is the most important in the

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EAG cluster estimate. Moving along the MAUP, when the unit of analysis is changed and esti-mates done on state-wise segregated data, literacy turns out to be the strongest explanatory vari-able. And the same estimates were obtained for the local regression, thus conforming to the ideaof Social Synergy. States of Chhattisgarh and Odisha are indifferent to aQol, showing insignif-icant relationship with regard to all the three explanatory variables. Main impediments in boththese states revolve around health infrastructure, low social sector development and large ruraland tribal population. These hurdles are too strong to be masked by the aQol idea. In Rajasthanand Uttarakhand none of the variables except female literacy is as strong as to determine infantsurvival. Institutional delivery has the least influence in all the states. Overall, only 73 districts(including districts showing positive relation) out of 261 have not shown any syndrome of aQol.

The insignificant districts do have some other explanatory variables to account for the changein IMR. However, their inclusion into the model would have made it unmanageable and alsobecause other left out maternal factors like age, parity and income have a direct bearing oneither or all of the three variables under consideration. And, inclusion of any other variable notconcerning a mother into the model would have questioned the whole idea of aQol.

Revisiting the framework (Mosley and Chen 1984) is fruitful in the sense that it still holds itsground in ground realities and helped to unfurl the whole idea of aQol. The future direction insuch studies could be to include the left-out variables or try to strengthen the aQol through rig-orous field-based studies, including the cultural multiplicities into the model. And, also it isquite imperative for the future researches to make estimates at the local level, which will havemultiple implications for issues ranging from conceptualizing problems, through policyframing, to service delivery mechanisms.

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Abstract1. Introduction2. Needs, Qol and aQol: concepts and theoretical considerations3. Case, data and methodology4. Research findings4.1. Ordinary least square4.2. Geographically weighted regression

5. Concluding remarksReferences