|Year : 2018 | Volume
| Issue : 2 | Page : 74-93
A statistical model for quantification of Panchakośas of large collective entities
Bhalachadra Laxmanrao Tembe1, Promila Choudhary2, HR Nagendra2
1 Department of Chemistry, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India
2 Directorate of Distance Education, SVYASA University, Bengaluru, Karnataka, India
|Date of Web Publication||28-Nov-2018|
Dr. Bhalachadra Laxmanrao Tembe
Indian Institute of Technology Bombay, Mumbai - 400 076, Maharashtra
Source of Support: None, Conflict of Interest: None
There are several ways of assessing the well-being of individuals as well as a collection of individuals. The panchakośa model is an evolved model for analyzing the well-being of individuals. For large collections of individuals such as nations, several ways are available for estimating the gross national happiness indices. In the present article, quantification of the five sheaths or the panchakośa of large collections of individuals is outlined. Methodology: The methodology uses large sets of data available from reliable sources such as World Development Indicators reports as well as the United Nations Organization data. Different characteristics of nations and its people are used as parameters for quantifying the five kośas of collective entities and these are rescaled so that a numerical estimate is made on a scale of 0–100 for each kośa. Results: The data for the five kośas can be combined to get an effective quantitative measure of happiness or well-being of a nation. The happiness levels in different kośas for 24 countries from different continents are estimated by a simple weighted average or a statistical method using 41 parameters. The results show a fair amount of ruggedness after the number of parameters increases beyond about 5 or 6 for each kośa. Conclusions: This Panchakośa Model of Happiness-I appears to be a fairly systematic way of analyzing the happiness levels in different kośas and can be used as a basis for a healthy model of development and interactions of large collective entities such as nations.
Keywords: Collective panchakośas, happiness levels, normalized parameters, quantification
|How to cite this article:|
Tembe BL, Choudhary P, Nagendra H R. A statistical model for quantification of Panchakośas of large collective entities. Int J Yoga - Philosop Psychol Parapsychol 2018;6:74-93
|How to cite this URL:|
Tembe BL, Choudhary P, Nagendra H R. A statistical model for quantification of Panchakośas of large collective entities. Int J Yoga - Philosop Psychol Parapsychol [serial online] 2018 [cited 2021 Mar 8];6:74-93. Available from: https://www.ijoyppp.org/text.asp?2018/6/2/74/246332
| Introduction|| |
The panchakośa viveka that has been formalized in the Taittiriya Upaniśad provides a way to classify a human being into five interrelated sheaths. Such a classification helps in studying these sheaths individually as well as jointly and has also provided a basis for therapy,,, for curing individuals, in whom these sheaths are not functioning in an optimal manner. These five sheaths are developed differently in different individuals. It is natural to expect that an analogous classification will be useful to study different units in societies, such as a family and communities in villages and cities, and this could be extended to countries as well as the whole world.
Such an extension of the concept of kośa (sheaths) to different units will require reasonable to good definitions and meanings for the kośas in these different entities. Although there could be multiple sets of definitions of these kośas, the effort would be all the same worthwhile, particularly if such a definition could provide a means for healing these sheaths in these units.
The first step would be to define the five kośas for families. Since human beings are strongly interacting systems, the manomaya kośa of a family is unlikely to be a linear combination of the manomaya kośas for the individual members of the family. In addition, in children aged 0–15 years, these kośas are generally not fully developed. To develop and characterize the kośas of the families, one needs to collect the data of several family members and this is an arduous task. Similar argument will apply to a cooperative society or a village or a city. While modern family counseling services contribute toward solving problems in families, the elders in joint families in the past and the village elders in ancient and even recent times continue to provide valuable suggestions to maintain healthy manomaya kośas of families and villages.
If we turn our attention to a group of persons in very large numbers such as the states of a country or countries themselves, we can use the methods of statistics to come up with a suitable definition of the five kośas of countries. A recent mathematical definition of happiness, and the metric developed for gross national happiness (GNH), can provide suitable guidelines to provide definitions for different kośas of collective entities such as nations. Possible steps toward this approach are outlined below. Such a definition for families too will certainly be useful.
- Annamaya kośa: An estimate for this kośa may be derived using the following data: Available land and water resources, agricultural area, gross domestic product (GDP), gross national product (GNP), road, rail, water, and air connectivity.,,,,,,,,, The proposed method will be normalized to the population.
- Prānamaya kośa: Life expectancy, employment levels, deaths caused by cancer and AIDS, the number of doctors available, internet and mobile connectivity, etc.,,,,,,,,,,,,,,,,
- Manomaya kośa: Mental health status of the country, crime and insurgency levels, corruption levels, strikes and agitations, suicide levels, divorce levels, smoking and drug related problems, number of professional counseling centers, psychiatric centers, number of jailed persons.,,,,,,
- Vijyānamaya kośa: Literacy, educational institutions at various levels, index of entrepreneurship, effectiveness of legal systems, research institutions, research publications, conferences and workshops, effectiveness in legislations.,,,,,
- Ānandamaya kośa: GNH, levels of charity, and social work.,, This is a difficult kośa to measure as Bhrigu relates this kośa to a state of bliss. The closest measures are taken from different approaches of happiness in societies including the social measures and the Cantril ladder.,,,,,,,,,,,,, These include the ideas of happiness in education, the Sach's happiness report, quality-of-life research, quality-of-life scale reliability, and sensitivity of subjective well-being measures.
After developing an index system, it will be applied to the following nations: India, Pakistan, China, Japan, Bhutan, Singapore (Asia), United Kingdom (UK), Sweden, the Netherlands, Romania, Greece, Russia (Europe), the United States of America (USA), Brazil, Mexico, Chile, Nicaragua (America), Egypt, Nigeria, Ethiopia, Yemen, Niger, Namibia (Africa) and Australia. It would be interesting to compare the countries which have similar economies. It will also be interesting to explore the role if any of the basic differences of religion, spirituality, and the political economy of these countries has an impact on the differences in the happiness parameters of these nations.
Most planning models of growth of nations do not include spiritual levels (levels of happiness) in their conceptualization or implementation. This leads to societies or nations where happiness levels do not increase in spite of exceptional technological levels. A study such as the proposed one could help in a complementary or a supportive manner toward the well-being of a nation in a manner similar to how an Integrated Approach to Yoga Therapy (IAYT),,, is having an impact on the health of individuals. The methodology of the present work is given in the next section. Data and results are given in the results and discussion section, followed by conclusions.
| Methodology|| |
The subject of happiness is subtle, difficult, as well as elusive. The concept of happiness has evolved over time, right from the Vedic period as well as from the time of Aristotle. The notion of happiness as activity, virtue, satisfaction of desire, pleasure (Eudaemonism vs. Hedonism), fortune, stoic nature, duty, transcendence, utilitarianism, self-realization, and supreme good has evolved over time, and a perfect definition has not been arrived at. The conventional economic approach took monetary and physical income as the most important indicator for well-being. This has serious limitations. The capability approach to well-being has been developed by Amartya Sen and Martha Nussbaum, and the happiness approach to well-being has been championed by Richard Easterlin's aim to overcome the conventional economic approaches. Even the methods of education as well as therapy, whose primary aim is to increase the overall happiness in a society, do get questioned from time to time. Even more challenging is the task to define a quantitative scale for happiness. This too has been discussed for a long time in literature. A lot of effort across all the continents has been invested in arriving at a scale. We shall mention only representative efforts in this area. These will also help us in setting up a statistical model.
There are significant differences between the happiness in ānandamaya kośa and the happiness that is understood in common practice. The ānandamaya kośa mentioned in the Taittiriya Upaniśad goes well beyond the manomaya and vijyānamaya kośas; it is thought to be a dominantly subjective experience, approaching bliss, intuitive harmony, and peace and is not easy to measure. However, as a first approximation, we shall adopt a measure obtained from the common measures of happiness and extend it to our statistical model.
Among several models that are available in literature, we choose two statistical models. One is an experimental definition of happiness which has been recently verified by functional nuclear magnetic resonance measurements and which is based on the subjective response to rewards. We refer this model as a Computational Model-I (CM-I). The other is the GNH Index for happiness defined in the studies in Bhutan. We refer this second model as Survey Model-I (SM-I). In the work presented here, we construct a model based on the panchakośa analysis. We refer this model as a panchakośa Model of Happiness-I (PKMH-I).
CM-I analyzes happiness as a subjective response to rewards, such as money that might elicit affective and motivational responses. The behavioral findings were based on two laboratory-based behavioral experiments as well as a large-scale smartphone-based experiment. The relationship between reward-related task events, neural responses to those events, and subjective well-being was also probed by functional magnetic resonance imaging (fMRI). fMRI is used to trace task-dependent neural activity in the ventral striatum of the brain, a major projection site for dopamine neurons, correlated with subsequent reports of subjective well-being.
By repeatedly asking participants to report on their subjective emotional state, their feelings can be related to antecedent life events including rewards. The subjects were asked to perform a probabilistic reward task, in which they are asked to choose between certain and risky monetary options. After every few trials, they were asked the question, “How happy are you right now?” Such an approach is expected to elicit rapid changes in affective state. Similarly, experience sampling adapted to laboratory and fMRI settings was also used for corroboration of data obtained from questionnaires in a survey using mobile response data. The experiential sampling questions make no reference to past events and concern the present overall subjective emotional states.
From brain responses to rewards, it is known that midbrain dopamine neurons represent reward prediction error (RPE) signals in animals and humans. Neuroimaging studies report the correlations of RPEs in the ventral striatum. This is an area of the brain that is a target for dopamine projections, in tasks from reinforcement learning to gambling. Many studies have also related subjective feelings about discrete events to neural activity. The behavioral data on a sample of 21–26 persons were fitted using a CM inspired by models of dopamine function. It was shown that momentary subjective well-being is explained not by task earnings but by the cumulative influence of recent reward expectations and prediction errors, resulting from those expectations. Temporal difference errors that dopamine neurons are thought to represent are closely related to these quantities. In the first case, the happiness at time t is fitted by the following model.
Happiness (t) = w0 + w1Σjγt-j CRj + w2Σjγt-j EVj + w3Σjγt-j RPEj
where CRj refers to certain rewards, EVj refers to expected values or outcomes and RPEj refers to reward prediction error (differences between experienced and predicted rewards). The summation is for j going from 1 to t. All the coefficients w0, w1, w2, and w3 turned out to be positive. All the gammas (γt-j) are forgetting factors which are all positive and these decay exponentially as one goes back further to earlier events. The weights for EVs were smaller than the weights for RPEs. One advantage of CM-I is that it is based on experimentally measurable data and also data based on surveys (a smartphone-based platform: The Great Brain Experiment, www.thegreatbrainexperiment.com; for iOS [Apple] and Android [Google] systems). The sample consisted of 18,420 anonymous unpaid participants who made over 200,000 happiness ratings. However, experiments which require highly sophisticated equipment (such as fMRI) and also huge surveys are prohibitively expensive and cannot be readily extended to other samples.
In the GNH model used in Bhutan which is referred here as SM-I, a comprehensive study was undertaken using 124 variables grouped into nine equally weighted domains to define an index of happiness.
A quantitative GNH value has been proposed to be an index function of the total average per capita of the following nine measures:
- Economic wellness or living standard indicated via direct survey and statistical measurement of economic metrics such as consumer debt, average income to consumer price index ratio, and income distribution
- Environmental wellness or ecological resilience indicated via direct survey and statistical measurement of environmental metrics such as pollution, noise, and traffic
- Physical wellness or health indicated via statistical measurement of physical health metrics such as severe illnesses
- Mental wellness or psychological well-being indicated via direct survey and statistical measurement of mental health metrics such as usage of antidepressants and rise or decline of psychotherapy patients
- Workplace wellness (time use) indicated via direct survey and statistical measurement of labor metrics such as jobless claims, job change, workplace complaints, and lawsuits
- Social wellness or community vitality indicated via direct survey and statistical measurement of social metrics such as discrimination, safety, divorce rates, complaints of domestic conflicts and family lawsuits, public lawsuits, crime rates
- Political wellness or good governance indicated via direct survey and statistical measurement of political metrics such as the quality of local democracy, individual freedom, and foreign conflicts
- Education indicated via literacy, schooling, knowledge
- Cultural diversity indicated via customs in societies, values, sports, drama, and films.
The above nine domains were built from 124 variables which constitute the basic building blocks of GNH. These variables could be packed into 33 clusters, but the important feature is that subjective variables had smaller weights. A threshold or sufficiency level was attached to each variable. The population was finally categorized into deeply happy (77% level sufficiency), extensively happy (66%–76% level sufficiency), narrowly happy (50%–65% level sufficiency), and unhappy (<50% level of sufficiency). Furthermore, it is to be noted that one needs to score equally high points in all the domains to be happy. Using a complementary matrix of sufficiency indices and the normalized weights for each of the factors, a GNH index has been defined. The concept of multidimensional poverty of Alkire and Foster has also been used in defining the GNH.
The weights of 33 variables, i.e. weights of different variables in nine domains in the GNH model of Bhutan, are depicted in [Table 1].
|Table 1: Weights of different variables in nine domains in the gross national happiness model of Bhutan|
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The method of estimating the GNH placed the data collected from people from different districts and age groups into a matrix form. The main data matrix M is an n × d matrix with rows, i ranging from 1 to n. The rows i refer to individuals and columns j ranging from 1 to d refer to different dimensions of achievements. Rows represent individuals and columns represent achievements in dimensions. To obtain a GNH, one needs a set of criteria for the range of sufficiency (adequateness) of the parameter to be placed into different levels of happiness. If the element Mij is less than some critical value Zj for a given column (predefined), then a depravation matrix G is defined, whose element Gij is 1 if Mij < Zj. Nonzero values of depravation matrix indicate depravation. For each of the d dimensions, weighting factors are applied such that the sum of weights Wj = 1. By summing the weighted columns, the depravation for the dimension is obtained. Let us call the depravation row vector as D. If this is subtracted from the unit row vector U, U − D gives the GNH row vector, which can be normalized and summed to get the GNH index. Details of the indices are given in the Bhutan report.
As we mentioned, ours is a modeling study wherein the data are collected from different sources, particularly the sites of the United Nations Organization and the World Development Indices/Indicators of the World Bank. From these data, statistical methods are used for converting them into suitable normalized parameters in the range of 0–100 for each kośa. The subjects used herein include all the members in the country for analysis purposes. A plan of computing the happiness of the PKMH-I is outlined below. Since the collected data are based on statistical reports, the chances of subjectivity are considerably reduced and equal weights may be assigned to each of the parameters of the present study. If we choose to define a scale of 0–100, the PKMH-I may be defined as:
PKMH-I for a kośa = Σ wi yi,
where wi is the weight of the parameter (in fraction or percentage) and yi is the normalized statistical measure of the parameter (in the range of 0–1 or from 0% to 100%). We will compute an overall score, but individual kośa scores will be more informative.
Since our model is a statistical method, the required data are collected from a wide range of sites and from recent reported literature. While there could be some uncertainties and minor variations in the data from different sources, these data will certainly help us to come up with a quantitative model which can be improved by additional checks on the self-consistency of the data. The application of the method across more than one calendar or financial year and extending to other countries can be explored later.
In our proposed model PKMH-I, we are using N (presently 41) variables that are presumed to be independent. Although there are a few residual dependences among these variables, we test for the impact of these by randomly removing, say 10% of the variables and noting their impact on the final results. The robustness of a statistical model is known to increase when the number of variables contributing to the model increases. The N variables are redistributed into different kośas by taking n1 parameters or variables for the annamaya kośa, n2 for the prānamaya kośa, n3 variables for the manomaya kośa, n4 for the vijyānamaya kośa, and n5 variables for the ānandamaya kośa. Of course, N = n1 + n2 + n3 + n4 + n5.
The rationale is based on extending the ideas relevant to the kośa of a given individual to large collections of individuals. Prānamaya kośa for an individual refers to the human body, the intake of food, clothing, and shelter. For a large collection, this kośa will consider the total food available for the nation; the total space, water resources, GDP, etc. are also considered. For the prānamaya kośa of a nation, life expectancy, employment, etc. are considered. There are negative characteristics such as HIV and cancer deaths too. The collective manomaya kośa deals with the mental and emotional health of a nation. Crime and corruption affect mental health negatively. Thus, the least corrupt country will have a better mental health for this particular parameter. One feature of these models is that we cannot easily say that the specification of parameters is complete for any kośa. However, the advantage is that if more parameters are identified, they can be very easily included in the model. Another feature is that all parameters are not completely independent. Large amount of corruption will lead to crime, and thus, these two, namely, corruption and crime, are not independent. However, they are both very good indicators of the mental health of a nation. In fact, larger the set of parameters one uses for specification of a kośa, the effect of interdependencies of the parameters gets diminished. For the vijyānamaya kośa, the intellectual growth of a nation through its academic and research institutions can provide a very good measure.
While most indices do serve the purpose of quantifying the happiness levels of populations, there are several ambiguities if the domains are not made sufficiently distinct. For example, in [Table 1] (GNH model), mental health is not included in psychological well-being. This would correspond to the manomaya kośa. Similarly, harmony and spirituality are counted in distinct domains, while they should be classified under ānandamaya kośa. The state of bliss cannot be obtained unless there are peace, harmony, and contentment. The panchakośa model (PKMH-I) provides a less ambiguous and a more unique way of classifying the parameters of the above domains and this model is quantified in the present work.
As we mentioned, this is a modeling study wherein the data are collected from different sources, particularly the sites of the United Nations Organization and the World Development Indices/Indicators of the World Bank. From the data, statistical methods are used for converting the data into suitable parameters in the range of 0–100 for each kośa. The subjects used herein include all the members in the country for analysis purposes. Since the collected data are based on statistical reports, the chances of subjectivity are considerably reduced and equal weights may be assigned to each of the parameter of the present study. Other models for unequal weights will also be alluded to. The next section describes the quantitative characterization of the kośas followed by conclusions and perspectives.
| Results|| |
We present the results for each kośa first and then combine them for a total score. The data extraction has been primarily done using the internet and published articles. The major sites used are the WHO sites and the sites that use published literature from reputed journals. Later, a comparison with mainly published literature data could be made.
We have collected the data for 24 countries from across the continents. These countries are China, India, Pakistan, Bhutan, Singapore, and Japan (from the Asian region); UK, Sweden, the Netherlands, Romania, Greece, and Russia (from the European region); USA, Brazil, Mexico, Chile, Nicaragua, and Australia (from the American and Australian continents); and Egypt, Nigeria, Ethiopia, Yemen, Niger, and Namibia (from the African continent). This will enable us to compare countries across the continents. We begin with the annamaya kośa parameters.
Annamaya kośa parameters
Annamaya kośa has to deal with all the physical resources available to the nation and how well they get distributed in the population. Land and water resources have to be scaled to the population. As outlined in the Methods section, the total score for each kośa has to be scaled or normalized between 0 and 100. There are 11 parameters chosen for the annamaya kośa and each of these parameters has been given 9.09 weightage for estimating the total annamaya kosha happiness parameter. To calculate the relative values of each parameter, the parameter is individually scaled between 0 and 100, and then, the values for all parameters are averaged. The actual values of these parameters are given in [Table 2]. The first parameter is the land and water area available for each country (A1). The parameters are labeled from A1 to A11 for annamaya kosha, B1 to B9 for prānamaya kosha, C1 to C9 for manomaya kosha, and so on. The areas in km2 per person in Australia, Russia, Bhutan, Brazil, and USA are 0.366, 0.122, 0.054, 0.043, and 0.03 km2, respectively, and for all other countries, the values are much smaller. We assign all values >0.03 km2 per person as 100% and scale the remaining areas by the ratios of actual area per person divided by 0.03. In this way, countries such as India and Japan get at least 7% relative value. Dividing all areas by the highest value of 0.366 give a value of <10% to the USA and hardly any value to countries such as India and Japan. This discussion illustrates that there is some degree of arbitrariness in these computations. However, if the number of parameters is increased, the impact of this arbitrariness is significantly minimized. The next parameter is the agricultural area in each country (A2). Nigeria has the highest value of 79%. For this parameter, we simply use the percentage of agricultural area. Thus, although India and Nigeria have very low scores for the land area available per person, the large agricultural area in these countries helps these countries to gain quite a bit in their scores through the agricultural area percentages. The percentage of water in the countries ranges from 0.1 to 18.4 (A3). This is multiplied by 10 to convert it into a percentage. For all countries where the percentage exceeds 100, a value of 100 is assigned. The purpose for rescaling the larger percentages (over 100) to 100 is to get a good spread in the normalized values. The distributions at the higher ends are often very far from a normal distribution, and this rescaling helps in keeping the overall parameters in a reasonable range between 0 and 100 across all countries. Poverty lines and malnutrition are an indication of severe deficiency in the annamaya kośa (A4). The indexed measure for poverty line is 100 minus the percentage of persons living at an income of < 4 US Dollars a day. Countries such as Australia, UK, Japan, and Russia get high scores here. However, India, Nigeria, Pakistan, and Bhutan all get small scores. For malnutrition (A5), the scaling used is 100 minus ten times the percentage of malnourished. Countries such as USA, Russia, and China get high scores, while India, Bhutan, Pakistan, and African countries get small scores. The next parameter is GNP measured in million US Dollars (2005 value). For this parameter, the value of 25,000 million US $ and above is taken as 100 and other GNPs are divided by 25,000 million US $ and this fraction is multiplied by 100 to get a percentage. For the malnutrition parameter (A6), we take 100 minus the percentage of malnourished population. Road lengths (A7) and rail lengths (A8) are considered next. These are first divided by the area of the country. To get the normalized values between 0 and 100, the ratio is multiplied by 100,000 for road length ratio and 1,000,000 for the rail length ratio. For road lengths, India, Singapore, Japan, Sweden, UK, and the Netherlands score a 100, while for rail lengths, only Singapore and the Netherlands score high. Precipitation rate (A9) is scored as follows. Countries with >1000 mm rain per year get a score of 100, while countries with <1000 mm rain get a score of 0.1 times the rain in mm. The populations of homeless people (A10) are scored by subtracting the percentage of homeless people from 100. The number of hospital beds per 1000 of population (A11) is the last parameter for the annamaya kośa. This number is multiplied by 10 to get a normalized score. The total populations of these individual countries are given in the last column (A12).
[Table 3] presents the normalized data for all these parameters on a scale of 0–100. The columns are labeled as A1N to A11N and they correspond to columns A1 to A11 which contain the actual/unscaled/nonnormalized data given in [Table 2]. The scaling procedure has been described above.
Prānamaya kośa parameters
Prāna is energy and the flow of prāna is the flow of energy. For a population, Prana represents its vitality and vibrancy. This is by and large determined by the mobility of the population and how the population spends its energy and thus gainfully employed. For computing the index for this kośa, we have identified the following parameters. They are employment rates (B1), unemployment rates (B2), life expectancy (B3), the number of cancer deaths per lakh of population (B4), number of HIV deaths per lakh of population (B5), the number of doctors per 1000 members of population (B6), the number of airports (B7), the percentage of internet users (B8), and the percentage of mobile phone users (B9). These parameters are depicted in [Table 4]. Normalizing these is a bit easier than normalizing the annamaya kośa parameters. Employment rate is counted as it is since it is a percentage. The unemployment rate is counted as 100 minus the unemployment percentage rate. Life expectancy is the next parameter. Japan with a life expectancy value of 84 gets 84%, while Nigeria with a 52.6-year life expectancy gets a score of 53. Cancer death data are age normalized per 100,000 of population. Maldives has the highest value of cancer deaths per 100,000 population. The data for all the countries are normalized with 360 as the highest value. Countries with values close to 360 get 0%, while countries with smaller cancer deaths get a larger score. HIV deaths are in the range of 1/100,000–6/100,000 of population. The normalized score for this parameter is 100 times (1 − number of HIV deaths/10). The number of airports is divided by the area of the country and multiplied by 106/15. With this scaling, UK, USA, and Singapore get a normalized score of 100. Since the number of internet users and the number of mobile users are given in percentages, there is no need to rescale them. Only when the values are >100%, the value of 100 is assigned to the normalized parameter. [Table 5] gives the normalized parameters (relative scale factors) for prānamaya kośa. The normalized scores are given in columns B1N to B9N of [Table 5] corresponding to the columns B1 to B9 of [Table 4].
Manomaya kośa parameters
Manomaya kośa of large collection of people deals with the mental satisfaction of the countries or societies. Mobs that are rioting have an extremely ill-developed manomaya kośa. They may do anything in frenzy and we witness these phenomena on several occasions. A war is the “best example” of a disturbed and highly tense population. The after-effects of the World Wars are still being felt and so are the effects of riots. The indices for the manomaya kośa comprise the following factors. They are suicide rate per year for 100,000 population (C1), corruption index on a scale wherein 0 is the most corrupt and 100 is the least corrupt (C2), prison population rates (C3), percentage of divorces to marriages (C4), net migration rate (C5), rule of law index (C6), smoking- and alcohol-related deaths (C7), drug-related deaths (C8), and violence-related deaths (C9). These indices are not strongly correlated with the GNP of a nation. Rich countries have suicide rates comparable to the poor countries and they have higher divorce rates. While the causes need to be analyzed carefully, these data indicate that even countries with a very large GNP or GDP need to improve their manomaya kosha. The scale for suicide rates is computed as 100 minus 100 multiplied by suicide rate/25. The last denominator is chosen to be a slightly larger value than the largest suicide rate. Least suicide rates get high scores in the happiness indices. In the corruption index, 0 corresponds to the most corrupt. As the values range between 0 and 100, the actual value may be taken either as a percentage of corruption-less-ness or as a percentage of being uncorrupted. For prison population rates, we take the value of 0.1 times the rate. Large prison populations or conviction rates are both good and bad. Here, we take it to be good as it may increase order in society at least due to the fear of being punished. The negative or bad part is that so many crimes are committed in the first place. The divorce rates are given in column C4. Higher rates of divorce indicate smaller capacities to accommodate alternative points of view. The index is calculated as 100 minus the percentage of divorce rate. Low divorce rates indicate greater stability, although a flip side of this is that if there is greater inequality between men and women, women may be far more accommodative so as to avoid divorce even at a great personal cost. Net migration percentages are given in column C5. An influx into a country indicates that there is a considerable well-being. People migrate to better environments. This is a major reason for the overcrowding of cities all over the world. The normalized index for this parameter is taken as 50 + 5 times the migration rate. The migration rates are in the range of 5–6 per 1000 of population. A country with a high rate of migration will have a large value of this parameter. The rule of law index given in column C6 is given as a percentage. Higher values of this index indicate conformity of the population to the prevailing laws. This is taken as a percentage. Alcohol-related and smoking-related deaths are in the range of 0–50 per lakh of population. This parameter is normalized as 100 minus the parameter value, C7. Drug-related deaths are in the range of 0–5 (C8). For this, normalization is done as 100 minus five times the value of the parameter. Violence-related deaths are in the range of 0–25 (C9). This is normalized as 100 minus four times the parameter value (C9N). The manomaya kośa parameters are given in [Table 6].
The normalized values of the manomaya kośa parameters are summarized in [Table 7].
|Table 7: Normalized manomaya kośa parameters (relative scale factors) corresponding to the columns C1 to C9 of Table 6|
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Vijyānamaya kośa parameters
Vijyānamaya kośa parameters include the literacy rate percentage (D1), percentage of graduates (D2), number of research papers (D3), the number of researchers per million of population (D4), number of colleges and universities (D5), the ratio of male literacy to female literacy (D6), and the role of voice of people and the accountability of the governance (D7). For an individual human being, this kośa corresponds to “viveka” or the ability for discrimination. For a society, these parameters should reflect its ability to increase intellectual and social awareness. There has been a remarkable increase in these factors along with economic development. Literacy, arts, culture, science, and education contribute to this kośa. For normalizing, for the first two columns (D1 and D2), which represent percentages of literacy and graduates, the values are taken as such. The next three parameters are normalized as follows. The number of research papers in million is multiplied by 250 to get the rescaled values in the range of 0–100. Many countries such as China, India, UK, and USA score high on this scale, while the African countries score low values. The number of researchers per million of population is multiplied by 0.02 to get normalized scores in the range of 0–100. Singapore and Sweden score 100 in the normalization. The number of universities and colleges is divided by the population of the country and multiplied by 6 × 106 to get normalized values in the range of 0–100. The next column (D6) is the ratio of female literacy to male literacy. This value is converted to a percentage by multiplying by 100. Treating all human beings (as well as other creatures as well) as equal is a great sign of viveka and it is reassuring to note that this aspect of development is far more encouraging in the present century than what it used to be, a 100 years or even 50 years ago. Having a good representation of female members in panchayats or local bodies of governance and legislative assemblies and reserving seats for them in these bodies is very encouraging for the social and global Vijyānamaya kośa. The last column is the role of the voice of the people and the accountability of the government (D7). This is higher for democratic countries where the people have a greater say in the mode and functioning of the government. Since this is given as a percentage, the value is already normalized. The Vijyānamaya kośa parameter values are given in [Table 8].
The normalized scores/indices for the Vijyānamaya kośa parameters are given in [Table 9].
|Table 9: Normalized 1 Vijyānamaya kśa parameters (relative scale factors)|
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Anandamaya kośa parameters
The parameters for ānandamaya kośa include the values for the human development index (E1), charity work in terms of money (E2) and time (E3) given, world giving rank index (E4), and the Cantril ladder of life scale gallup (E5). Among all the kośas, it is hardest to compute the values for this kośa as ānanda or the state of bliss is indescribable. When Bhrigu attains this state, he does not return to his father Varuna for confirmation since he is convinced that he is in the state of Brahman. For a nation, instead of estimating the state of bliss, it is easier to estimate the extent of spirituality through the acts of giving or the extent of karma yoga in their citizens. To add a bit of corresponding materialistic content as well as to consider the opinions of populations (happiness has a strong subjective component too), we have considered the human development index and the Cantril ladder. The Cantril ladder is one of the scales to measure global life satisfaction.,,,,,,, It may be considered as a satisfaction with life scale (SWLS). Among various components of subjective well-being, the SWLS assesses global life satisfaction. Many of these scales do not consider features such as loneliness that are responsible for dissatisfaction. The SWLS is shown to have favorable psychometric properties, including high internal consistency and high temporal reliability. Scores on the SWLS correlate well with other measures of subjective well-being and also correlate predictably with specific personality characteristics. SWLS is suited for use with different age groups. Thus, we thought that this ladder can be added as one of the parameters for the anandamaya kośa. Cantril's ladder elicits respondents to rate their current life satisfaction on a ladder that ranges from 0 to 10, where 0 reflects worst imaginable life satisfaction and 10 reflects best imaginable life satisfaction. Respondents are first asked to describe these two anchors and then requested to rate their current life satisfaction on this “ideographically anchored” continuum. These parameters are given in [Table 10].
For normalization, the human development index (E1) and the Cantril ladder (E5) are already in the 0–100 scale. Charity work in terms of money and time is also in a percentage. The world giving indices are ranked from 1 to 222. Since all these countries chosen here have ranks between 0 and 100, the percentage is calculated as 100 minus the rank of the country. If all the countries in the world are included, then a formula such as (222 − rank) ×100/221 is more appropriate for normalization. The normalized ānandamaya kośa parameters are given in [Table 11].
|Table 11: Normalized ānandamaya kośa parameters (relative scale factors)|
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Combined happiness indices and graphical representations
The data obtained in the last five sections of the previous section are summarized in [Table 12]. Each column gives the total happiness index for a given kosha, which is averaged over all the parameters for that kosha with equal weightage. The last column gives an overall happiness index, the statistical index that was sought in the present work. The next few figures present these data in a pictorial way.
The happiness indices for the five kośas and the total happiness index (averaged over the five kośas) for the 24 countries are shown in [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6].
|Figure 1: Total happiness indices in the annamaya kośas for 24 countries|
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|Figure 2: Total happiness indices in the pranamaya kośas for 24 countries|
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|Figure 3: Total happiness indices in the manomaya kośas for 24 countries|
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|Figure 4: Total happiness indices in the vijnyanamaya kośas for 24 countries|
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|Figure 5: Total happiness indices in the anandamaya kośas for 24 countries|
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|Figure 6: Total happiness indices (averaged over all the kośas) for 24 countries|
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To see how sensitive the normalized parameters are to the choice of the parameters, we recalculate the total happiness indices by choosing (n1, n2, n3, n4, n5) to be (10, 8, 8, 6, 4). We have done this by removing the last parameter for each one of the kośas. This altered set of total happiness indices is given in [Figure 7]. We see that none of the happiness indices for the same countries between the two figures [Figure 6] and [Figure 7] differ by more than 5%–6%. However, the average values of the individual koshas change by about 10%.
|Figure 7: Total happiness indices (averaged over all the kośas) for twenty four countries with different parameterization (the last parameter for each kośa removed) than the one used in Figure 6|
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This confirms our stand that as the number of parameters increases beyond 7 or 8, there is a great degree if invariance between the predictions from different parameterizations. This supports one of the goals of the model to capture the essence of the kośas.
Another model to consider is to look at various linear combinations of different kośas to see if this has a major impact on the happiness indices. In principle, all the kośas have a great degree of independence; otherwise, a person such as Shri Ramakrishna who paid so little attention to his annamaya kośa could have hardly attained the highest states of Samadhis, so characteristic of the ānandamaya kośa. The result of a recalculation with the weights of 1, 1.1, 1.3, 1.5, and 1.7 for the annamaya, prānamaya, manomaya, vijyānamaya, and ānandamaya kośas, respectively, for the 24 countries is shown in [Figure 8]. The new results do not differ from the old ones by more than 2%–4%. The deviations are both positive and negative. An explanation could be that the values of happiness parameters for different kośas of different countries have very weak correlations between themselves.
|Figure 8: A total happiness model with weights of 1, 1.1, 1.3, 1.5, and 1.7 for the annamaya, Pranamaya, manomaya, Vijyanamana, and anandamaya kośas, respectively|
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| Discussion|| |
We thus have a quantitative model for happiness indices of different nations based on the panchakośas (PKMH-I) that are familiar to the individuals as outlined in the Taittiriya Upaniśad. The available data could be classified into the parameters for different kośas and simple normalization procedures could be adopted to give a spread of each of the parameters between 0 and 100. As the weights for each of the parameter chosen for a given kośa were the same, the final score for a kośa could be simply computed as an equally weighted average. The scores for different kośas for each country are quite different, and thus, these can be used as good indicators for a holistic planning for a nation, just as IAYT has been used for improving the health of individual patients. A remarkable observation is that the countries with very high level of satisfaction or happiness (many affluent countries) are not having equally high values of the manomaya kośa parameters (except for Australia and Singapore which are rather small populations), while a small country such as Bhutan with a difficult terrain and a low value of annamaya kośa parameter has a happiness level at the manomaya kośa in the same range as for countries such as UK, Japan, and USA. It is thus not surprising that the GNH Index study of Bhutan has been praised so highly. There is so much to learn even from such a small country.
We note that some of the results are on the expected lines. Countries with high levels of annamaya kośa tend to do quite well on the vijyānamaya kośa. While our model can certainly be improved, let us assess how this can be used by these nations. The two dominant messages are that even for the countries with large values of happiness indices, improvements are certainly possible and those areas can be identified by looking at individual kośas. In countries with large natural resources, a lot of room exists for improvements in manomaya and ānandamaya kośas. The second message is that for countries with low scores, all is not lost as there are areas in which they are doing well. These countries just have to plan better and adopt a more holistic model of development. This also brings out the main feature that only economic development is not a complete development and the countries may now choose to interact so that they can increase mutual happiness indices rather than try to dominate one another through military or economic wars. The interaction models between countries that led to tragedies such as the Bhopal Gas Tragedy or even the models where powerful countries simply go and occupy smaller and weaker countries are so harmful to both the interacting countries. Had the British or the North Americans considered to interact favorably with the manomaya kośa of all its occupied territories, they would have been a much happier nation and society today and would have increased goodwill toward themselves from a large part of the world. Their nations would not have faced such intense security threats so often. Thus, the interaction model that uses the Upaniśadic kośa concepts has a lot to offer for the models of interaction between the countries. This is where quantification of the kośas is likely to be of good use.
An interesting feature in the normalized kośas is that the so-called developed countries do very well in all the other four kośas relative to the manomaya kosha. The opposite is true for Asian and African countries (except Russia) which do much better in the manomaya kośa relative to the other four kośas. A possible explanation is that in these developing countries, the population is aware of the deficiencies in theirs annamaya, prānamaya, and vijyānamaya koshas and adopt themselves better to the limited resources. The opposite seems to be true in developed countries, wherein there is a lot of material prosperity and comfort. In their quest for material happiness, their populations have lost quite a bit in emotional tolerance as witnessed by larger divorce rates and problems associated with drugs, smoking, and alcohol. We thus note that our model provides an alternative to the present available models of happiness.
| Conclusions and Perspectives|| |
Improvements in our model are certainly possible as there are many factors such as the environment that need to be considered in greater detail. The factors such as freedom for individual pursuit and the aggressive policies of nations in interfering with the affairs of remote countries to increase their individual domination need to be taken into account in a more elaborate manner. These data will also help economically developed countries to inspect their policies with other nations, by asking the question: Do our policies with other nations help us to increase the happiness levels of both countries? These will clearly bring out the answer that either wars of sanctions or vetos do not add to the happiness indices in any of the kośas. Thus, there is a need for greater harmony and peace rather than aggression. Just as the purpose of yoga is to harmonize and elevate different kośas of the individual bodies, these indices can be used to plan the activities of nations to improve harmony and peace.
Another feature of this study is that we did not get data for all the parameters that we initially planned to get and some new parameters were found along the way. Some parameters had to be inferred from other available data. A considerable portion of the data is from fairly reliable web-sites. However, these need to be cross checked with published literature from the journals of the social sciences. Some of the data need to be checked for internal consistency as well. Another interesting observation is that the aggregate happiness index computed for Bhutan in its national study was well over 60 and the percentage of very happy people was 43. The value that we compute is near 46. A conclusion from this observation is that when we develop a comparative and nonsurvey-based scale, there is a greater objectivity. At the same time, there is some satisfaction that the numbers represented here can be classified into different kośas and that our value and the national value for aggregate GNH for Bhutan have a strikingly close similarity.
The greatest strength of this study, like all statistical models, is the opportunity it provides for quantitative classification of the kośas of populations based on the model proposed in the Taittiriya Upaniśad. At the time of Bhrigu and Varuna, there were hardly any hospitals or even machines to measure weights or blood pressures. While Bhrigu's analysis was entirely spiritual and theoretical, it is remarkable that this model provides a basis for an alternative therapy to improve the physical and mental health of people. It would be certainly tempting to speculate that a study such as this or a similar one which analyzes the overall state of a nation into well-defined and distinct segments could be used to improve the development models that nations use in their planning. Another strength of this study is that the number of parameters used for each kośa can be easily increased systematically so that all the koshas can be comprehensively defined. We may then get good limiting values for the well-being of nations in their different kośas.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8], [Table 9], [Table 10], [Table 11], [Table 12]