Abstract
This study compares and analyzes four leading well-being measures: standard monetary measures (income and expenditure), subjective well-being (life satisfaction), equivalent income, and the Alkire-Foster multidimensional poverty index (MPI). To consistently compare these measures, we conducted a field survey of Delhiās low- and middle-income respondents in India. Using our dataset, we find that the equivalent income approach tends to ignore the economic value of education because of an adaptive preference problem caused by standard measures of life satisfaction. The MPI approach shows that educational deprivation is prominent in our survey. The results suggest that the MPI may complement traditional income comparisons by appropriately setting components, weights, and deprivation cut-offs. Additionally, we find many violations of the dominance principle in these measures, except for MPI.
The authors would like to appreciate kind encouragements and helpful comments from Professors Kotaro Suzumura, Reiko Gotoh (the editor of this book), and John Weymark in developing practical methods of social welfare function approach. We thank the participants of the 15th Annual Meeting International Society for Quality-of-Life Studies, the JADE Conference, and the ANPOSS/ENPOSS/POSS-RT 2021 Joint Conference who heard earlier versions of this paper. Especially, we are grateful to Takeshi Aida for thoughtful comments and suggestions. We also offer our special thanks for the grants from Grant-in-Aid for Scientific Research (C) (grant number: 19K01683) and the Joint Research Center for this research. This study is based on the results of our original survey conducted in India under the approve of ethical review (ethical review number: 2017C006, Hitotsubashi University).
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1 Introduction
How can human dignity be defined? The history of human dignity reveals that the term has been used in more ambiguous and polysemic ways than expected. For example, in the traditions of ancient monarchies and Catholics, dignity simply meant respect for those of higher social positions and their privileges. According to Kant (1785), dignity is inalienable, invaluable, and non-transferable worth granted to autonomous and rational humans, who should be treated as an end in themselves, not a means to some other end. Under the Kantian view, human dignity should have intrinsic value that cannot be compromised by any quantity of resources and money. In bioethics, human dignity is often interpreted as a simple aspect of self-determination (Macklin, 2003). Recently, Nussbaum (2006, 2011) stated that a life with human dignity requires a list of basic capabilities to be higher than some threshold levels. However, a major problem with these concepts of dignity is that persons with severe intellectual or mental disabilities or dementia who need daily care cannot be the object of dignity because they seem to lack autonomy, reason, or some basic capabilities. Trying to reconstruct the idea of dignity in terms of care ethics, Kittay (2005) made some reasoning in a justification for the dignity of persons with disabilities.
Even in the context of evaluating individual well-being, the implications of respect for human dignity are ambiguous. Consider the indexing dilemma shown in the context of Rawlsian primary goods (Gibbard, 1979). This indexing dilemma shows that interpersonal comparisons of individual well-being that satisfy two plausible requirements must lead to a cycle. Formally, it states that no acyclic well-being ranking exists that satisfies the principles of individual preference and dominance. The individual preference principle requires that each intrapersonal comparison of oneās well-being reflects only oneās preference. The dominance principle requires that for all interpersonal comparisons of well-being, individual iās well-being is at least as good as jās well-being whenever iās consumption bundle (or functioning vector) is weakly greater than that of j. Acyclicity requires that no cycle exists for all interpersonal comparison rankings.Footnote 1 It is well known that these principles are incompatible in various contexts of social choice theory (Brun & Tungodden, 2004; Fleurbaey, 2007; Pattanaik & Xu, 2007; Weymark, 2017). This means that any well-being measure faces difficulties in comparing individual well-being whenever there are differences among individual preferences.Footnote 2
It seems plausible that respecting each individualās preference in assessing their living standards should be part of dignity regarding self-determination. Suppose an individualās evaluation of their living standard is not respected, and their situation is simply evaluated by social value judgment. In that case, it may be too paternalistic and perfectionistic to take dignity as self-determination seriously. On the other hand, it also seems plausible that respecting a dominance relation in assessing living standards among different individuals is consistent with the view that a life with dignity should be full of sufficient basic capabilities. As Adam Smith (1776) pointed out, life with human dignity requires a certain material basis. It is valid to claim that the living standard of a wealthier person is strictly better than that of a poorer person. An individual who appears in public feeling shameful (i.e., having inadequate food, clothing, shelter, etc.) can hardly be said to be in full respect for human dignity. This study does not offer a definitive argument as to which principles should be respected in the debate over the indexing dilemma and human dignity. Instead, we review the key measures that have received attention as important indicators of individual well-being and investigate their application issues by focusing on the indexing dilemma.
Let us provide a short history of the measurement of well-being. Traditional methods based on GDP per capita have failed to adequately reflect distributional considerations (especially the lives of the low and middle classes). Moreover, GDP per capita could not consider any deterioration of important components of social welfare (e.g., declining social bonds, worsening inter-group conflicts, degradation of the natural environment, and rising inequality of opportunity). Since the well-known report by Stiglitz et al. (2009), many governments and international organizations have started to consider and use various policy evaluation methods, such as the Alkire-Foster multidimensional poverty index (Alkire-Foster MPI), equivalent income, OECD better life index, and some subjective well-being indices, instead of the traditional GDP per capita. These novel methods are expected to shed light on various individual and social well-being aspects. Specifically, the Alkire-Foster MPI has become a common method for policy evaluation by capturing the deprivations in health, education, and living standards that a person faces simultaneously. (Alkire & Foster, 2011; Alkire et al., 2015). On the other hand, the equivalent income approach, which is a revival version of classical money-metric utility (Samuelson, 1974; 1977) and Pazner-Schmeidlerās egalitarian equivalent approach (Pazner & Schmeidler, 1978), has been now on some trials by applying standard econometric methods to measuring individual well-being (Decancq et al., 2015a). Subjective well-being is a very popular method in many happiness studies and has been considered a surrogate for well-being, reflecting non-monetary life dimensions, such as social relationships (OECD, 2013, 2019). These approaches have several reasonable properties and are useful for measuring various aspects of well-being.
However, each measure used in this study is known to have some defects. For example, the standard money-based approach, which focuses on income or consumption levels, cannot reflect the various disadvantages of discriminated or vulnerable groups that suffer from education and employment opportunities. It also fails to consider the unequal treatment of women in their ownership of household assets, which is often observed in developing countries where gender discrimination is so strong. Furthermore, even if individuals are given the same goods and purchasing power, there are non-negligible individual differences in what they can do due to disabilities and other factors.Footnote 3 The subjective well-being approach has the problem of adaptive preferences, where people often adapt to their circumstances and are likely to report that their subjective well-being is not so bad, regardless of whether they are in a favorable or unfavorable circumstance.Footnote 4 Generally, neither the income nor life satisfaction approach satisfies the dominance and individual preference principles.Footnote 5 On the other hand, the MPI approach satisfies the dominance principle among the poor identified by some MPI criteria, but it violates the individual preference principle.Footnote 6 In contrast, the equivalent income approach satisfies the individual preference principle but violates the dominance principle.
While each well-being measure has some limitations, there are surprisingly few comparative and comprehensive analyses of these indices.Footnote 7 This is mainly because no database allows simultaneous comparison of these indices. Hence, this study compares and analyzes four leading well-being measures (simple money-based indices, happiness as life satisfaction, the Alkire-Foster MPI, and equivalent income) using original survey data from Delhi, India. As a result, we show how often theoretical flaws occur and the extent to which informational loss can be supplemented by using another measure.
This study contributes to the literature on well-being measurements. First, we show that in our dataset from India, the problem of adaptive preference is so serious that the subjective well-being approach cannot work well for measuring individual well-being.Footnote 8 This result further strengthens the conventional perception that we should not only evaluate social welfare in terms of subjective well-being. Second, the problem of adaptive preferences implies that one method of the equivalent income approach, that is based on subjective well-being estimation, fails to reflect some important aspects of well-being, such as education, which does not matter regarding subjective well-being. The fact that subjective well-being among the poor and the low caste is often high also implies an endogeneity problem in previous empirical estimation methods of equivalent income. Third, by comparing leading measures with income and consumption per capita, we show that the Alkire-Foster MPI has a low correlation coefficient with the money-based measurements. This fact suggests that MPI is more sensitive to several aspects of well-being than other measures because it can consider material and social information such as housing, health, job status, and education. Finally, our sample suggests that violations of the dominance principle are the rule rather than the exception in all measures except the Alkire-Foster MPI.
The remainder of this study is organized as follows. SectionĀ 2 explains the survey design and methods of calculating the measures. Additionally, the basic statistics and properties of the well-being measures are reported and discussed. SectionĀ 3 compares the results of four measures by using pairwise rank correlation between them and observing some characteristics of the bottom 10%. Finally, Sect.Ā 4 summarizes the results and discusses the remaining issues.
2 Data and Methodology
2.1 Survey
We surveyed low- and middle-income individuals in Delhi, India, in November 2017 to consistently compare various well-being measures.Footnote 9 To generate our sample, we first randomly select five assembly constituencies. The results of this selection included Jahangirpuri, Lajpat Nagar, Hauz Khas/Malviya Nagar, Raghubir Nagar, and Okhla. Next, to obtain a sample representing low-income individuals, we randomly selected jhuggi-jhopdi (JJ) colonies, which are slum resettlement locations, using the list of JJ colonies available on the Delhi Municipality website. For the middle-income group, areas adjacent to the JJ colonies were selected for the survey. After selecting low- and middle-income localities, we randomly selected households for the survey.Footnote 10 Ultimately, 510 respondents completed the survey. The average age of respondents was 35.36, and men were 46.08%. The number of respondents who belong to households holding the ration card (below the poverty line type, BPL)Footnote 11 is 226, which is almost half the sample. Additionally, 28.63% of respondents receive no official education.
Our survey covered the following information: (1) demographics (gender, age, family configuration, caste, religion); (2) happiness as life satisfaction; (3) education; (4) occupation and employment status; (5) income and consumption level; (6) health status; (7) housing facilities and household assets; (8) social capital; and (9) security and environment. The following subsections explain the four well-being measures used in this study.
2.2 Income and Expenditure
The first well-being measure is based on the income/expenditure approach, which has traditionally been mainstream in poverty and inequality analysis. Income level has often been interpreted as a surrogate for individual well-being and plays an important role in policy goals and evaluation. However, income is just one of the various tools through which an individual achieves well-being. Moreover, there are difficult problems with the income approach, such as life cycle (e.g., a tendency to have low income in oneās youth and old age and high income in middle age) and diversity among human abilities to transform goods into functionings (i.e., differences among individual living levels due to diversity of abilities and social discrimination in gender, class, occupation, and race). Hence, using income level as a surrogate for human well-being requires significant research. Note that, as is widely recognized in recent years, consumption expenditure is a more suitable surrogate for human well-being than income.Footnote 12
In the survey, annual household income and expenditure brackets included 50 or less, 50ā100, 100ā200, 200ā300, 300ā400, 400ā500, 500ā600, and 600 or above, denominated in thousands of rupees. Therefore, to calculate individual income, household income was divided by the square root of the number of persons in the household.Footnote 13 FigureĀ 1 depicts the histograms of individual income and expenditure. Using a poverty line (Rs. 13,608) for urban areas in Delhi, only 2.6% of respondents were below the poverty line in the case of individual incomes, while 16.3% were below the poverty line in the case of individual expenditures. Generally, individual expenditures seem to be a good surrogate for oneās living standard. They are suited to identify the poor since consumption directly generates utility and is smoothed over oneās life cycle. However, previous studies have used individual income because of its availability and simplicity, and we use both income and expenditure variables in the following sections.
2.3 Alkire-Foster MPI
The second well-being measure is the Alkire-Foster MPI, a multidimensional poverty index proposed by Alkire and Foster (2011).Footnote 14 The Alkire-Foster MPI satisfies some desirable properties, such as monotonicity, poverty focus, and decomposability. MPI is defined as a generalization of the weighted average deprivation rate,Footnote 15 which is a natural extension of the well-known single-dimensional income inequality measure (Foster et al., 1984). Therefore, it is expected to be more adequate in analyzing the details of poverty at individual or national levels compared to traditional money-based methods, such as average income and consumption expenditure. The global MPI evaluates national poverty in three dimensions: health (infant mortality and malnutrition), education (school attendance and years of schooling), and living standards (housing facilities and assets), and it succeeds in showing multifaceted deprivations in developing countries.Footnote 16 However, there are certain problems with the MPI approach. For example, any MPIs fail to respect unanimous judgments of interpersonal well-being comparisons.Footnote 17 Additionally, since empirical results strongly depend on a specification of the MPI, such as choosing dimensions, setting each poverty line in each dimension, selecting the poor, and deciding on weights, the rationale of the MPI is still unclear and has to remain arbitrary.Footnote 18
The aggregation method proposed by the Alkire-Foster MPI considers both the incidence and intensity of poverty. Generally, an individual or group iās Alkire-Foster MPI is calculated as follows:
where d is a component of the MPI, \({y}_{id}\) is iās value of component d, \({y}_{d}^{*}\) is the poverty line of d, and wd is the weight of d with \(\sum_{d\in D}{w}_{d}=1\). Let \(max\{{y}_{d}^{*}-{y}_{id}, 0\}/{y}_{d}^{*}\) be the deprivation rate of d. If Ī±ā=ā0, MPI measures the percentage of components that are deprived within the poor. If Ī±ā=ā1, it is equivalent to the weighted average of the deviation rates. We calculate the Alkier-Foster MPI for both cases in which Ī±ā=ā0 and 1, but the case where Ī± is zero shall not be listed in our results to save space.
The dimensions, weights, and components are presented in Table 1. We calculated three types of AlkireāFoster MPI by considering three (AF-MPI-3D), four (AF-MPI-4D), or five dimensions (AF-MPI-5D). For these three dimensions, we consider health, education, and living standards, which have the same dimensions as the global MPI. Additionally, we add the employment dimension for the AF-MPI-4D, and the environmental dimension for the AF-MPI-5D since the dimensions of employment and environment are also considered for estimating the equivalent income based on life satisfaction.
Table 2 summarizes the questions in our questionnaire for measuring each component and the cut-off points for deprivation. Note that our survey is not based on the household level but at the individual level. Since we asked one respondent within each household in our survey due to budget constraints, we have only information about the respondentās health, education, and employment status.
Health is measured by the so-called subjective health index. Previous studies show that the subjective health index can represent the respondentās actual health conditions (See van Doorslaer & Jones, 2003).Footnote 19 We asked respondents to rate and evaluate their health on a 5-point scale: very good, good, fair, bad, and very bad. We assume that a person is deprived if their answer is ābadā or āvery bad.ā Education is measured by years of schooling. A person is considered deprived if the number of years of schooling is less than ten, which is the lower secondary education level. Living standards are measured in almost the same way as the global MPI: cooking fuel, sanitation, drinking water, electricity, roofs and floors, and assets. Drinking water was divided into two categories: quality of drinking water and location of the water source. Employment was measured by two categories: unemployment and working hours. In urban areas, employment matters for well-being. Therefore, a person is considered deprived when they are unemployed. Additionally, a person is considered deprived when they work over 60Ā h per week since such long working hours seem to affect physical and mental conditions. The environment was measured in two categories: safety and air quality. Respondents were asked to rate and evaluate their areaās safety and air quality on a 5-point scale. A person is considered deprived when the answer is ābadā or āvery bad.ā
Table 3 shows the proportion of respondents considered deprived for each dimension. The share of deprivation of education is close to 40%, which is the highest among all dimensions. FigureĀ 2 presents the distribution of the weighted averages of the deprivation rates related to the five dimensions. Although about 50% of respondents in our survey were from BPL households, the most frequent deprivation rate was 0 among all dimensions. Regarding living standards, no individual is simultaneously deprived of all categories, while about 30% of individuals are deprived of at least one category. Note that the deprivation rates for education and employment can range from zero to one.
Finally, we calculated the weighted average deprivation rates of all respondents using three types of Alkire-Foster MPI with Ī±ā=ā1Footnote 20: AF-MPI-3D, AF-MPI-4D, and AF-MPI-5D. FigureĀ 3 shows the distribution of deprivation rates for all the respondents. The global MPI identifies the poor when at least 33% of the weighted components are zero. The solid line in Fig.Ā 3 is the global MPI threshold of the poor (0.33). The poverty ratios identified by AF-MPI-3D, AF-MPI-4D, and AF-MPI-5D were 36%, 18%, and 16%, respectively. While education is a significant dimension that increases deprivation scores, the weights for education decrease as the dimension increases. As a result, the poverty ratio decreases as the number of dimensions considered in the AF-MPI increases.
2.4 Subjective Well-Being as Life Satisfaction
The third well-being measure is happiness as life satisfaction, a key measure of subjective well-being. Generally, subjective well-being, such as life satisfaction, measures the cognitive aspect of subjective well-being by focusing on reflective and objective evaluations of well-being rather than the emotional factors that lead to a feeling of happiness. The biggest problem within the subjective well-being approach is an adaptive preference problem due to both the hedonic and aspiration treadmill. This leads to many paradoxical situations. For example, because individuals with disabilities or low incomes also change their preferences and aspirations to suit their circumstances, they often evaluate their lives at normal levels. As a result, their subjective well-being levels are the same as (or higher than) those of a healthier or wealthier person (Loewenstein & Ubel, 2008; Oswald & Powdthavee, 2008). Our analysis also suggests that an adaptive preference problem exists in our survey.
Following Diener et al. (1985) and OECD (2013), our survey asked five questions on subjective well-being. Respondents were asked about their degree of agreement with the five statements on a scale of 1ā7, where 1 meant āstrongly disagree,ā and 7 meant āstrongly agree.ā The five-question statements were as follows:
-
1.
āIn most ways, my life is close to my ideal.ā
-
2.
āThe conditions of my life are excellent.ā
-
3.
āI am satisfied with my life.ā
-
4.
āSo far, I have gotten the important things I want in life.ā
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5.
āIf I could live my life over, I would change almost nothing.ā
Using the above standard measure for life satisfaction, we summed up the five scores (Fig.Ā 4). While the distribution of life satisfaction was skewed to the right, the scores showed sufficient variation. For example, the highest score for life satisfaction was 30 on the histogram. This suggests that most people are satisfied with their lives, regardless of their actual living standards (i.e., the adaptive preference problem seems to occur).
To investigate the factors affecting life satisfaction, we used an ordered logit model to estimate the correlation between life satisfaction and various factors.Footnote 21 The dependent variable is life satisfaction, and the explanatory variables are a log of income; components of MPI such as subjective health index, years of schooling, living standards, employment, and environmentFootnote 22; other health-related variables such as nutrition, disability score, fatigue, pain, and mental conditions; social capital such as the number of friends, relationship with family, and relationship with neighborsFootnote 23; demographic variables such as gender, age, and square of age; and dummy variables for caste groups, religious affiliation,Footnote 24 and assembly constituencies. The nutrition and disability scores were used as alternative measures for the health dimension of the Alkire-Foster MPI, and the detailed definitions are explained in footnote 18. Additionally, we used fatigue, pain, and mental conditions to control health conditions. For caste groups, scheduled castes (SCs), scheduled tribes (STs), and other backward classes (OBCs) were used.Footnote 25 Others represented higher-caste individuals. To explore the different functional forms of life satisfaction between social categories such as castes and religions, we investigated the specifications between SCs and non-SC individuals separately.
Table 4 presents the results. As shown in Column 1, the coefficients of individual income and living standards representing material wealth are statistically insignificant. Additionally, the coefficient of years of schooling was statistically insignificant. On the other hand, the coefficients of subjective health index, environment, and relationship with family are larger than other coefficients and are statistically significant.
Columns 2 and 3 display the results for SCs and non-SCs, respectively. For example, while the coefficient of individual income is positive and statistically significant among SCs, it is small and statistically insignificant among non-SCs. Additionally, while the coefficient of the relationship with family is insignificant among SCs, it is significant among non-SCs. There are other different results, such as environment, age, and disability. These different coefficients between SCs and non-SCs suggest that functional forms of life satisfaction differ among social groups.
As shown in previous studies, our regression results are consistent with the claim that non-monetary factors are important for subjective well-being. Additionally, because many individuals are materially poor but sufficiently satisfied, there seems to be an adaptive preference problem.
2.5 Equivalent Income
The fourth well-being measure is based on the equivalent income approach proposed by Fleurbaey (2005) and many studies (Fleurbaey, 2007; Fleurbaey et al., 2013; Decancq and Neumann, 2016; Decancq & Schokkaert, 2016; Decancq et al., 2015a, 2015b, 2017). A personās equivalent income is defined as an income level that would make the person indifferent between her actual situation and the hypothetical reference situation, where she would be at the reference values for all non-income dimensions. This approach satisfies the individual preference principle but violates the dominance principle. Although this approach has many problems, it is considered acceptable for measuring individual well-being.Footnote 26
Two practical methods for estimating equivalent income have been proposed in the literature. One method is to estimate the WTP for a hypothetical reference situation by using the contingent valuation method to calculate the equivalent income. For example, Fleurbaey et al. (2013) asked respondents the highest amount they would pay to be in perfect health for one year and estimated their equivalent incomes. Another method is to estimate equivalent income using a regression of life satisfaction. For example, Decancq et al. (2015a) estimated equivalent income under the highest reference values for health, education, and housing. This study follows the latter strategy for estimating equivalent incomes since the contingent valuation method has many disadvantages in evaluating hypothetical reference situations of several non-income dimensions.Footnote 27
Next, we explain our estimation strategy. Following Decancq et al. (2015a, 2016), we calculate the equivalent income using life satisfaction regression. Note that their methodology requires the strong assumption that all individuals in the same group classified by some demographic properties have the same preference relationship. Therefore, we first select the life dimensions to be analyzed to estimate the preference structure using life satisfaction regression. Considering the standard variables of life satisfaction regression and the availability of our data, we selected the following six life dimensions: income, health, education, living standards, employment, and environment. All life dimensions are measured using the same components to calculate the Alkire-Foster MPI in Sect. 2.4. The definition of each variable was the same as that used in the regression analysis of happiness.
We consider the following regression specification:
where \({s}_{i}\) is life satisfaction described in Sect. 2.4, \({y}_{i}\) is an individual annual income, \({z}_{i}\) is a vector of variables reflecting individual characteristics, \({x}_{i}\) is a vector of non-income life dimensions, \({d}_{i}\) is a vector of dummy variables reflecting membership in socio-demographic groups, and \({u}_{i}\) is an error term. To consider differences in preference structures among socio-demographic groups, the coefficients on income and other functions are allowed to differ for the robustness check. We consider SCs and Muslims for socio-demographic groups and then separately run the regression using either dummy variable. Regarding \({z}_{i}\), we included dummies for male, age, the square of age, assembly constituencies, castes, and religious groups. This specification was estimated using an ordered logit model.
An equivalent income \(({y}_{i}^{*})\) is defined as an income level that would make individual i indifferent between iās actual situation and the hypothetical reference situation where i would be at maximum levels (\(\tilde{x }\)) for all non-income life dimensions. As shown in Decancq et al. (2016), we can measure the equivalent income as follows:
where \({y}_{i}^{*}\) is the equivalent income and \(\tilde{x }\) are the maximum levels of \({x}_{i}\).
Table 5 presents the regression results.Footnote 28 The results were almost the same as those of the life satisfaction regression, except for individual income. While the coefficient of individual income in Table 4 is statistically insignificant, that in Column 1 of Table 5 is positive and significant. This may be because the relationship with family matters in subjective well-being estimations. If a person with a high income tends to be highly satisfied with better social relations, then adding social relations as a variable will underestimate the effect of income. On the contrary, if better social relations tend to yield high incomes that directly contribute to improving life satisfaction, ignoring social relations will overestimate the effect of income due to omitted variable bias. Our results suggest that it is difficult to select control variables to estimate the effects of individual income on life satisfaction. Additionally, the coefficient of education is quite small and statistically insignificant since education has little impact on life satisfaction. Thus, education is rarely considered in the equivalent income, which causes quite different results in identifying the poor between the equivalent income and the Alkire-Foster MPI approaches.
3 Comparison of Well-Being Measures
3.1 Comparisons to Income/Expenditure
Income/expenditure is the most popular measure to identify poor individuals. Here, we compare income/expenditure with other measures, such as the Alkire-Foster MPI, life satisfaction, and equivalent income. FigureĀ 5 shows the relationship between the AF-MPI-5D and individual income/expenditure. As the figure shows, some people with low incomes or expenditures have low deprivation scores and are not poor in the AF-MPI-5D. On the contrary, people with an MPI score of 0.4 have various incomes widely distributed from 0 to 300. This implies that income is only one aspect of poverty and that we should identify the poor by considering other non-income dimensions.
FigureĀ 6 shows the relationship between life satisfaction and individual income/expenditure. While people with high incomes are not necessarily highly satisfied with their lives, people with low incomes are often highly satisfied, which suggests an adaptive preference problem in our survey.
Finally, Fig.Ā 7 shows the relationship between equivalent income and monetary measures (income and expenditure). Owing to the adaptive preference problem, some equivalent income is almost the same as the corresponding actual income over various income levels. Moreover, some individuals with high incomes have very low equivalent incomes, which suggests that there are violations of the dominance principle in many cases. FigureĀ 8 displays scatter plots of income and equivalent income measured by different functional forms of life satisfaction among social groups. Panel A is the same as that on the left-hand side of Fig.Ā 7. In Panel B, we control for the interaction term between SCs and each function, while the interaction term between Muslims and each function is controlled in Panel C. Due to the small estimates of the interaction terms, the three figures are almost similar.
Since the coefficients of life dimensions are used as weights for calculating the willingness to achieve the reference vector of life dimensions, the relationships between life satisfaction and life dimensions play a major role in estimating oneās equivalent income. However, life satisfaction is not necessarily correlated with meaningful life dimensions that significantly impact human flourishing. As a result, the equivalent income approach fails to satisfy the dominance principle among different groups.Footnote 29
3.2 Ranking, the Worst-Off, and the Dominance Principle
To compare the interpersonal well-being rankings generated by these measures, we calculated Spearman rank correlation coefficients among seven measures: individual income, individual expenditure, AF-MPI-3D, AF-MPI-4D, AF-MPI-5D, life satisfaction, and equivalent income.Footnote 30 Table 6 presents the results. The correlations between MPI and individual income and expenditure ranged from 0.10 to 0.22. On the other hand, the correlation between MPI and equivalent income is approximately 0.6. This is partly because equivalent income is estimated using the same MPI dimensions. Life satisfaction was also used to calculate the equivalent income. However, the correlation between life satisfaction and equivalent income is lower than that between the MPI and equivalent income. Additionally, life satisfaction is weakly correlated with other measures because it is influenced by various non-income factors other than material assets and education.
Next, let us consider and compare the bottom 10% of respondents identified by different measures. Table 7 summarizes the means of the basic variables. The average income and years of schooling differed among the measures. The bottom 10% identified by MPI or life satisfaction included individuals with high income. In the AF-MPI-3D and AF-MPI-4D results, the average number of years of schooling was less than one year. This is because both the AF-MPI-3D and AF-MPI-4D have a heavier weight of education compared to the other measures. As shown in Table 5, subjective health had a larger impact on life satisfaction than the other variables. Consequently, a person with low subjective health tends to be identified as poor using the equivalent income approach. Additionally, because education is less correlated with life satisfaction, the equivalent income approach implies that individuals with lower education levels are not included in the worst-off group. As the effects of individual characteristics strongly depend on the specifications and definitions of well-being measures, the bottom 10% groups are quite different among the measures. Our results showed that the bottom 10% group identified by AF-MPI-3D had the largest share of women.
Finally, we consider the violations of the dominance principle. The dominance principle requires interpersonal comparisons of well-being to be consistent with resource dominance relationships. That is, individual iās well-being is better than that of individual j whenever iās relevant live dimensions are greater than those of j. Generally, MPI satisfies the dominance principle on the set of poor individuals,Footnote 31 but the other measures violate it. Panel A of Table 8 shows how often these measures cannot satisfy the dominance principle. We calculate the violation ratios of the dominance principle according to several life-dimension scenarios. Generally, there are many violations of the dominance principle for each measure. Violations of the dominance principle tend to increase as the number of life dimensions is reduced, or personal attributes are increased in the equivalent income approach. Additionally, when education, which has a low correlation with life satisfaction, is included in the life dimensions, violations of the dominance principle increase. As shown in Panel A of Table 8, all measures substantially violate the dominance principle, meaning that disadvantaged individuals are often judged to be better than wealthier individuals in some life dimensions. Moreover, Panel B of Table 8 shows that the situation is the same with the bottom 10%. This invokes serious problems in policy intervention since it sometimes recommends antiegalitarian transfers from a disadvantaged to an advantaged group.
Generally, the equivalent income approach is strongly affected by life dimensions that greatly impact life satisfaction.Footnote 32 Moreover, individuals deprived of these dimensions are judged to be disadvantaged in interpersonal comparisons of equivalent incomes. The Alkire-Foster MPI has a fixed weight; therefore, deprivation in one life dimension disadvantages interpersonal comparisons of the MPI by that weight. If the MPI weights were determined using statistical methods such as life satisfaction regression and principal component analysis, a large part of the differences between the MPI and equivalent income approaches might disappear.Footnote 33
Although many life dimensions seem important for well-being, they do not necessarily significantly affect life satisfaction. Moreover, the relationship between life dimensions and life satisfaction could be very weak because of the aspiration/hedonic treadmill problem. In this study, we have been able to illustrate that education is such a factor and that there are still major challenges in using well-being measures to estimate equivalent incomes and determine endogenous weights.Footnote 34 We must develop a measure of life satisfaction that can adequately reflect a cognitive evaluation of a good life based on appropriate life dimensions. We also need to consider developing a methodology for better preference-based interpersonal comparisons.
4 Conclusions
As the indexing dilemma shows in the context of individual well-being measurement, there is a sharp conflict between the individual preference principle (requirement of dignity as a self-determinant) and the dominance principle (requirement consistent with the dignity view as sufficient basic capabilities). While philosophical considerations are important in determining which requirement of human dignity should be respected, we empirically analyze the extent to which key well-being measures conflict with the two principles. This study conducts a field survey in and around the slums of Delhi to consistently compare four leading well-being measures: individual income/expenditure, the Alkire-Foster multidimensional poverty index, happiness as life satisfaction, and equivalent income. As Sen (1985) suggests, regarding the problem of adaptive preference in India, we find that lower caste or Muslim respondents tend to have high life satisfaction. Therefore, following the subjective well-being approach based on life satisfaction, even lower-caste respondents with health problems or lower living standards are evaluated as enjoying good lives. On the other hand, due to this adaptive preference problem, the equivalent income approach based on life satisfaction regression fails to grasp various important aspects of well-being, such as education and health. Additionally, this study confirms that the MPI approach could reflect various deprivation levels of the poor while strongly depending on the functional form of MPIs. From our survey data, the correlation between MPI and individual expenditure is relatively low among our measures, suggesting that the MPI approach can be a complementary and useful tool for measuring individual well-being. Moreover, our results show that violations of the dominance principle are not rare, following both subjective well-being and equivalent income approaches. These empirical results suggest that a more sophisticated strategy is needed in the estimation methods and theoretical analysis of well-being measures to take human dignity and decent lives seriously.
The remaining issues of this study are as follows. First, our sample is small and limited because we focused on slums in India. Additionally, our survey data did not include details of household information, especially for children. More comprehensive data are required to further investigate the properties of various well-being measures. In particular, we should reexamine how often violations of the dominance principle occur in equivalent income and subjective well-being approaches by testing them with a large dataset.
Second, we cannot sufficiently reflect the differences among individual preferences to calculate equivalent income in the previous estimation method. Although the equivalent income approach emphasizes the importance of reflecting individual preferences for interpersonal comparisons of well-being, the previous method strongly assumes that individual preference structures are the same for similar demographic groups. Thus, an improvement in measuring equivalent incomes is needed, and we must consider the heterogeneity of preferences within similar demographic groups.
Third, among the individual well-being measures for evaluating human dignity and decent lives, a consensus-based method proposed by Sakamoto (2018) is consistent with a unanimous judgment and satisfies the dominance principle. Similar to the estimation strategies of the equivalent income approach, we can also use three methods: life-satisfaction-based estimation, inferring willing-to-pay to obtain oneās reference bundle, and revealed-preference-based estimation. If the dominance principle plays a dominant role in measuring individual well-being, these methodologies should be developed.
Finally, a theoretical framework for social evaluation should be developed for aggregating individual well-being. Practical exercises of the social welfare ordering approach require us to develop a class of acceptable individual well-being measures and construct a class of desirable aggregation rules for various contexts of social choice theory. It is especially important to characterize a class of ethically appealing social welfare orderings in uncertainty and risky situations.
Notes
- 1.
Formally, acyclicity is defined as follows: for all alternatives x1, ā¦, xn, if x1 is strictly better than x2, x2 is strictly better than x3, ā¦, and xn-1 is strictly better than xn, then xn is not strictly better than x1.
- 2.
See Weymark (2017) for elegant survey on the indexing dilemma and the related impossibility results.
- 3.
Note that the income approach also has some problems from the well-known life-cycle perspective. Sen (1985) proposed the capability approach as an appropriate method for measuring well-being that can solve these problems. However, its empirical methodology to embody an idea of the capability approach and formal models to clarify its theoretical performance are still in development, and further examination is needed.
- 4.
See Decancq et al. (2015a) for the issues of adaptive preferences, hedonic treadmill, and aspiration treadmill. They point out that there are problems with the equivalent income approach using subjective well-being when adaption is severe, as shown in this study.
- 5.
The income approach violates the dominance principle depending on the dimensions of well-being. Now, consider the set of functionings according to the capability approach. Let f(y1, i) and f(y2, j) be functioning vectors of a healthy person i with income y1 and a disabled person j with income y2, where f(y1, i)ā>āf(y2, j) and y2ā>āy1. This violates the dominance principle. Similarly, given external and internal resources, it is easy to consider a case where the income approach violates the individual preference principle. Also, it is easy to consider the case in which the subjective well-being approach does not satisfy both the principles of dominance and individual preference. In the context of intrapersonal comparisons of well-being, there can be a lot of paradoxical situations whenever subjective well-being easily adapts to the status quo. Consider, for example, a miserable man who ignores his family and friends in pursuit of career promotion and monetary rewards and continues to work excessively, resulting in economic success. Suppose he is unhappy because he has lost close connections with his family and the meaning of life and is on the verge of a psychological crisis. How can we make an intrapersonal comparison of his well-being? In the ex-ante evaluation, he prefers to live a materially rich but spiritually poor life, while in the ex-post evaluation, he prefers to live a spiritually rich but not materially rich life. See Decancq et al. (2015a) for the argument of experienced and decision utility.
- 6.
Another major problem is that the MPI approach may violate unanimous judgments on interpersonal comparisons of well-being; see Sakamoto (2018) for the possibility theorem of the principles of consensus and dominance.
- 7.
Decancq and Neumann (2016) recently compared five well-being indices (income, subjective well-being, composite well-being index, equivalent income, and von Neumann and Morgenstern utility) using the German SOEP dataset. Decancq et al. (2016) compare four well-being indices (income, subjective well-being, composite well-being index, and equivalent income) by using the official Colombian dataset. However, these findings are limited because they do not consider the adaptive preference problem and violations of the dominance principle.
- 8.
The problem of adaptive preference was originally pointed out by the classical study of Sen (1985). This problem may be caused by the history of caste discrimination and less social mobility in India.
- 9.
We conducted the survey in collaboration with Outline India, a research agency in Haryana. Prior to the main survey, the pilot survey was conducted, and the agency trained all surveyors before conducting in-person interviews.
- 10.
Specifically, we followed systematic random sampling. We selected every fourth household for both income groups. If households refused to participate or were vacant, we went to the next household and followed our every fourth-household rule.
- 11.
BPL is determined by various factors such as expenditure and occupation. In our survey data, even among low-income respondents, there is a mix of BPL and non-BPL households.
- 12.
Stiglitz et al. (2009) clearly state that consumption expenditure is more appropriate than income for measuring individual well-being. But, of course, income is relatively easy to observe, while it is difficult to observe and collect consumption expenditure objectively.
- 13.
For this calculation, we use each median for each bracket. For the 600,000 and above bracket, we use Rs. 600,000. This estimation is very standard in practical applications but has many theoretical issues to consider. See Decancq et al. (2015a, Sect.Ā 5.1) for the problem of estimating household equivalence scales.
- 14.
- 15.
In special cases (in which the parameter of deprivation aversion equals zero), the Alkire-Foster MPI is just a head count ratio. But, generally, it takes the form of a weighted average of the powers of the deviation rate from the poverty line.
- 16.
See the latest global MPI report in OPHI.
- 17.
- 18.
One way to treat MPIās arbitrariness is to focus on robust results with the addition of various factors (Foster et al., 2013). Alternatively, a structural estimation method may be useful, in which factors related to poverty are summarized and analyzed as a lament variable. Another option is to focus on robust results even if the weights change within a certain rational range. See Seth and McGillivray (2018) for details.
- 19.
We confirm the correlation between the subjective health index and other variables related to health conditions such as disabilities, fatigue, and pain. For robustness check, we will use variables of nutrition and disabilities as alternative measures for health. Nutrition is measured by the availability of food. A person is considered deprived if their household members skip or cut the size of meals because of being poor. Disabilities are measured by the method developed by the Washington Group on Disability Statistics. We use variables related to seeing, hearing, walking, cognition, self-care, and communication to calculate the disability index.
- 20.
If a person is deprived of a third or more of ten (weighted) components, the global MPI identifies them as poor. However, since there is no specific reason and rationale for this identification strategy, we calculate the sum of deprivation rates.
- 21.
The number of observations is reduced to 432 because some variables are missing due to respondentsā refusal to answer.
- 22.
Education is measured by educational years from 0 to 16. Living standards are measured by the presence or absence of six components and take values from 0 to 6. For employment, we create two dummy variables for unemployment and hours worked deprivation respectively, and add them together. Similarly, for the environment, we create two dummy variables for quality air and safety deprivation respectively, and add them together.
- 23.
We asked respondents about their relationship with family and neighborhoods on a 1 to 5 scale, where 1 means āvery badā and 5 means āvery good.ā.
- 24.
Religions are divided into three categories: Hindu, Muslim, and others.
- 25.
In the definition of ethnicity in India, SCs are former untouchable castes, STs are socially and economically marginalized ethnic groups, and OBCs are other socially and educationally backward classes. Although affirmative actions have been taken since the independence, the disparity in the living standards between SC/ST households and other households remains stark. Castes still play an important role in India. Munshi (2019) reviews the consequences of caste involvement in the Indian economy.
- 26.
Generally, this approach has two big problems. First, the approach depends on the choice of reference points. Second, a regressive income transfer (an income transfer from someone with a low equivalent income to someone with a high equivalent income) may be seen as a welfare improvement. See Sects.Ā 3 and 4 in Decancq et al. (2015a) for defenses of these issues.
- 27.
The other method is to calculate equivalent incomes by using the estimated revealed preferences. Indeed, Akay et al. (2020) infer the revealed preferences on the income-leisure domain using the UK dataset and calculate equivalent incomes. However, this method focuses on only two life dimensions (income and leisure). Since we aim to compare well-being measures regarding various life dimensions, we opt for the simple strategy based on the subjective well-being equation to calculate equivalent incomes.
- 28.
The number of observations is reduced to 432 because some variables are missing due to respondentsā refusal to answer. However, for the estimation of equivalent income, the number of observations is 494 because we included respondents with missing variables such as caste categories.
- 29.
- 30.
In this section, we exclude respondents with at least one missing variable among seven indices from the analysis.
- 31.
Since the Alkire-Foster MPI is a measure focusing on the poor only, it takes all zero values for non-poor individuals. As a result, there are violations of the weak dominance principle for non-poor individuals. In the case of generalized composite indices, violations of the dominance principle never occur, even in a weak sense.
- 32.
In addition to the equivalent income estimation based on life satisfaction, we have also estimated equivalent incomes based on the so-called satisfaction of the life-of-ladder method. Then, violations of the dominance principle among the bottom 10% are drastically decreasing compared to the life satisfaction estimation method. However, since there seem to be no significant differences in the other results worth noting, we skip them.
- 33.
Decancq and Neumann (2016) estimate equivalent incomes using income, health, and unemployment as life dimensions and education as individual characteristics. Their results show that there is little difference between ranking by the equivalent income and that of the composite measure (only the bottom 10 percent differs between the two approaches), but this paper finds the opposite result. Many studies have shown that weights and living dimensions matter in these well-being measures.
- 34.
This result suggests that the revealed preference method, another estimation method for the equivalent income approach, may have the opposite result against the life satisfaction method. While education is not correlated with life satisfaction, individuals spend a lot of time and money on their education. This fact would lead to the exact opposite estimation results between the two methods, where the impact of education on well-being is strong in the revealed-preference method but very weak in the life-satisfaction method.
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Mori, Y., Sakamoto, N. (2024). Does the Choice of Well-Being Measures Matter in Respecting Human Dignity? Empirical Results from an Indian Slum Survey. In: Gotoh, R. (eds) Dignity, Freedom and Justice. Springer, Singapore. https://doi.org/10.1007/978-981-97-0519-1_12
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