Wellbeing inequality in a developing country: from theory to practice

38 | ICUEH2017 Wellbeing inequality in a developing country: From theory to practice PHAN VAN PHUC University of Wollongong, Australia Can Tho University – pvphuc@ctu.edu.vn MARTIN O’BRIEN University of Wollongong, Australia – martinob@uow.edu.au SILVIA MENDOLIA University of Wollongong, Australia – smendoli@uow.edu.au Abstract The emergence of multiple concept of wellbeing that can be quantified has allowed researchers to move beyond a narrow focus on income and consumption as a primary measure of inequality and poverty Although analyses of multidimensional wellbeing are increasingly feasible due to the availability of data, the consumption or income is still applied in a number of studies As a result, the literature on wellbeing remains deficient in two main ways: (1) the use of inappropriate proxies for wellbeing, and (2) ignorance of the interdependency between dimensions of wellbeing This paper develops a fundamental framework and applies a principal component analysis method for a calculation of the wellbeing level and wellbeing inequality in Vietnam Our results show that not only the level, but also inequality, of wellbeing increased in the period 1993–1998 and 2002–2008 This challenges the consensus of a moderate level, and stability in, wellbeing inequality using income proxied measures We argue that empirical studies of wellbeing need to incorporate multiple dimensions in addition to dimensional interdependency characteristic and thus, implementation in the wellbeing analyses of wellbeing using principal component analysis can obtain the unique results of the level and inequality of wellbeing Keywords: inequality; principal component analysis; Vietnam; household wellbeing Phan Van Phuc et al | 39 Introduction The concept of inequality has been extended in relation to multiple dimensions of wellbeing The rhetoric question ‘equality of what?’, raised by Sen (1980), requests a comprehensive examination of the facets of, and proxies used for measures of, inequality The income-proxied approach derived from the utility comparison becomes unbearable in terms of drawing a complete picture of interpersonal wellbeing differences (Sen 1997) Although perception of wellbeing varies among different contexts as it depends on social norms and values, the domains of wellbeing go beyond income dimension This idea is traced back to Sen (1985a)’s capability approach that emphasizes not only what people have, but also the extent to which they are free to and to be A general perception of wellbeing is anything making a good life (Deaton 2013, p.24) despite no consensus on the concept of wellbeing has been reached; differences in wellbeing achievements needs an assessment of ‘wellness’ of people’s state of being (Sen 2003b, p.36) In developed countries, a plethora of studies focus on wellbeing achievements or outcomes with a range of indicators including, but not limited to, income and wealth, health and feeling, education and social engagement (Deaton 2013, p.24) For instance, OECD (2013) chooses eleven indicators which represents plausible dimensions of wellbeing which specifies the eight components suggested in Stiglitz et al (2009) In developing countries, however, it is insufficiently concerned with research in wellbeing and inequality in wellbeing (Cho 2015) Although data availability for assessments of inequality in wellbeing has emerged, serious problems with empirical analyses remains First, one-indicator proxy leads to a distorted picture of wellbeing which comprises a variety of factors For instance, Vietnam shows confusing levels of inequality with income and expenditure indicators which are equally accepted as proxies for wellbeing (Zhuang et al 2014) In 2008, there was a significant gap between the Gini coefficient of income (0.44) and that of expenditure (0.36) Additionally, the development of measures of inequality in multiple dimensions disentangles the ambiguities of single indicator-proxied method Rather, it further provides confusing and inconclusive results corresponding to varying choices of parameters used and the sequence of aggregation across dimensions in the estimations 40 | ICUEH2017 of inequality (e.g inequality aversion), even with the same methods and datasets (e.g Nilsson 2010, Justino 2012) Finally, the assumption of non-interdependencies between variables is also violated as there is plausible evidence of interrelations between economic, education and health indicators Russian data, for example, reveals a complexity of interrelations across dimensions Decancq and Lugo (2012) found that this correlation structure changed remarkably in the examined period (1995–2005) Unfortunately, they could not scrutinise thus far because of the paucity of data which is common in the majority of countries Therefore, the extent to which the indicator weights in a computation of inequality have not been thoroughly examined A postulation of equal variable weight is, however, inconsistent with the fact that variables may have unequal influences on wellbeing, and thus on the level of overall inequality To fill these deficiencies, this paper develops the analytical framework based on Sen’s capability approach and applies the polychoric principal component analysis (PCA) to Vietnamese data In doing so, this research contributes to the literature on wellbeing inequality in two main ways First, it extends the analysis of wellbeing by including the various wellbeing components We consider the contribution of non-economic dimensions and the interactions of all indicators to an overall trend in multidimensional inequality Furthermore, we compare wellbeing inequality trends over time and across different geographical areas The remainder of the paper proceeds as follows The next section discusses the capability approach The methodology, data and variables are described in Section Section compares the wellbeing index construction based on the polychoric PCA to the income-proxied wellbeing Section analyses inequality in wellbeing The conclusive part investigates further steps to identify major causes of inequality The capability approach Capability indicates the individual ability to obtain real achievements in relation to external and internal conditions that influence personal transforming from commodity possession to personal wellbeing (Sen 1985b) Despite economic dimension is an important contributor to wellbeing, it could not capture all determinants of quality of life that people are able to or live in their favoured ways Phan Van Phuc et al | 41 Sen (1985b) introduces his own approach to make the individual wellbeing comparable through a relationship between the two core concepts: ‘capability’ and ‘functioning’ This correlation is addressed by a simple equation which is slightly modified by Kuklys and Robeyns (2006) as follows: Qi(Xi) = [bi|bi=fi(c(xi))|T(i,s,e) for several fi ϵ Fi, and several xi ϵ Xi] (1) where: bi denotes individual i’s ‘being’; fi is a functioning and belongs to Fi (vectors of individual’s functionings); c(xi) is a function of conversion from a vector of possession of commodities (xi) to their characteristics, xi ϵ Xi (different sets of commodities); and T is the transformation conditions comprising three components; these are individual circumstances – Ti (e.g sex, physical ability), social factors – Ts (e.g public policies) and environmental conditions – Te (such as environmental pollution, weather) How ‘well’ of personal ‘being’ firstly depends on commodity ownerships and individual functionings Given a bundle of commodities (xi), different choices in Fi lead to varieties in the wellbeing level (bi) This expression is called the personal capabilities – Qi(Xi) Xi refers to all kinds of resources and is subject to the personal budget constraint The capability approach is totally and directly operational with respect to freedom of choices Kuklys and Robeyns (2006) appreciate its extendable characteristic by adding plausible functionings such as ‘being educated’ and/or ‘being employed’ Dang (2014) supports a concentration on the achieved functionings which are more operational than on a set of capabilities in the case of inadequate information on freedom conditions Despite difficulties of data collection relating to achieved capabilities or functionings, in practice, the data and variable suitability and analytical methods associated with such data should be thoroughly examined (Sen 2003a, p.53) Sen however does not provide a specific discussion beyond that point, but he recommends a solution to choosing dimensions, indicator weights, and calculation metric through a democratic public decision Wellbeing evaluations should consider with respect to social and historical contexts (Jackson 2005, Qizilbash 2011) 42 | ICUEH2017 An important characteristic of the capability is its ‘incompleteness’ of a contribution of functionings which lead to the wellbeing level (e.g Sugden 1993) Alkire (2002b) advocates that this incompleteness is not a shortcoming, but it opens for adaptations to the cultural and personal circumstances She also appreciates Sen’s capability that does not dwell on a fixed subset of capabilities, but proxies for capabilities should be adjusted in favour of research perspectives ‘The capability approach can often yield definite answers’ of the levels of individual wellbeing in such a case (Sen 2003a, p.46) Finally, inequality should be assessed as a ‘failure of [and differences in] certain basic capabilities’ (e.g being nourished, being sheltered) respectively as income and preference are not accurate measurements to comparing interpersonal wellbeing (Sen 1985a, 2006) There is nothing mathematically wrong with the measurements of inequality derived from income, ‘but [to] interpret them as utility comparison…would be a complete non sequitur1’(Sen 1997, p.392) Methodology, data and variables 3.1 The polychoric principal component analysis We choose the polychoric PCA developed in Kolenikov and Angeles (2004, 2009) to analyse wellbeing and inequality because this modified PCA is superior to its naïve version The standard PCA is originally constructed to handle non-discrete variables An application of PCA to the non-continuous data may have problems First, if one breaks a categorical variable into more than two dummies, PCA could create numerous spurious correlations Second, a transformation from ordinal variables to dummies cannot retain the ordinal feature of indicators More importantly, if categorical variables are treated as continuous ones, a violation in the assumption of a normally distributed variable in PCA occurs analogously to the case that discrete variables are used as independent variables in OLS since discrete variables not have a density but high skewness and kurtosis (Kolenikov and Angeles 2004, 2009) The polychoric PCA minimises violation of a normal distribution assumption when applied to discrete data The polychoric PCA can also assign various weights for different units and categories of indicators and describe more precisely wellbeing inequality (Ward 2014) non sequitur: ‘a statement that is not connected in a logical or clear way to anything said before it’ (Merriam- Webster dictionary n.d.) Phan Van Phuc et al | 43 Correlation coefficients in the polychoric PCA are described in the following steps First, two ordinal variables xi, xj indicate asset ownership, educational outcomes, or health status They are discretised in dk categories (k = 1…m), and dr categories (r = 1…n) respectively Thus, the thresholds of xi, xj are denoted as τi,τj corresponding to dk, dr These axioms yield the following two equations: xi = k iff dk-1 < τik