Send message to Authors

Paper short abstract

Sen's theory of human developmen has influenced the UN's approach and led to the establishment of organizations like the HDCA. Sustainable development, particularly in response to climate change, requires ecological tax reforms to transition to a green economy. The Capabilities Approach could inform the restructuring of tax systems to address environmental and social harms more effectively.

Paper long abstract

Amartya Sen's theory has significantly underpinned the United Nations' approach to human development. This is evident through the examination of the United Nations Human Development Reports, which conceptualize development as the process of broadening human choices. The significance of Amartya Sen's contributions to these multidimensional approaches is unmistakable as his theory serves as a foundational framework supporting various others and has been instrumental in the establishment of an organization dedicated to the study of human development - the Human Development and Capability Association, of which Amartya Sen is one of the founding members (DIB, 2001, p. 97). The concept that development aims to enhance human lives by broadening their capabilities was deeply fostered by Amartya Sen. He stated that "development is about removing the obstacles to what a person can do in life, obstacles such as illiteracy, ill health, lack of access to resources, or lack of civil and political freedoms" (FUKUDA-PARR, 2003, p. 303). Sen argues that the proposed theory advocates for development as a process of expanding substantial freedoms, contrasting with theories that limit the scope of development by focusing solely on income growth, maximizing well-being, or industrial and technological progress (SEN, 1999). The impacts of climate change are becoming more tangible and are impacting every corner of the globe, pushing it towards a threshold where the harm to humanity's existence could become extremely serious and irreversible. In this context, the aim is to explore sustainable development from the taxation activity from a hypothetical-deductive method, adopted in a theoretical research based on literature review. We observe that the pursuit of sustainable economic development demands the implementation of ecological tax reforms aimed at fostering the transition to a green economy - if adopted correctly, steering taxation is a powerful public policy that may direct the taxpayers’ choices towards greener consumption and attitudes and restrain polluting actions. However, efforts to mitigate the effects of climate change on well-being may clash with many contemporary approaches to achieving well-being, which still heavily rely on fossil fuel-derived energy (VILLANI; VISCOLO, 2020). Yet, these changes call for alterations in production processes, consumption patterns, and lifestyles – as well as intense public debate among lawmakers and, most importantly, involving citizens, institutions, and economic groups, so as to conscientize the most relevant players in domestic and global scale. Ultimately, these variations could impact certain economic sectors and socially vulnerable groups heavily reliant on energy, such as households facing financial constraints or sudden economic, social, or health challenges, hindering their ability to afford necessities, including energy utilities. On the other hand, human well-being relies on the mitigation of climate changes, more urgently and decisively and ever. We conclude that the Capabilities Approach could serve as foundation for rethinking national tax systems with the aim of eradicating or diminishing greenhouse gas emissions and its externalities stemming from the rise in greenhouse gas concentration in the atmosphere, as the existing tax rates on polluting goods are remarkably low and fail to adequately mirror the environmental and social harm caused by these products.

Send message to Author

Paper short abstract

Cost of dying prompts an imperative understanding of health shock. Nationally representative panel data from the Consumer Pyramid Household Survey spanning 2014–2023, we examined how the death of a household member affects household expenditure and socio-economic status. Data from 0.6 million individuals, highlights the spending behavior, supported by the hypothesis of Grossman Model (1972).

Paper long abstract

High cost of dying in India: Understanding causal relationship using panel data of 0.6 million individuals

Background

The high cost of death underscores the impact of health shocks on household poverty in developing countries like India. Allocation of household funds to healthcare is a key factor in household impoverishment, reflecting India's healthcare sector characterized by low government spending, high out-of-pocket expenses, and limited financial protection. India's health spending, at 1.5% of GDP, is one of the lowest worldwide (RBI 2020-21).

Methodology

This paper aims to analyse the relationship between individual deaths and household expenditure using a sample of 0.6 million individuals. Pooled OLS regression, also known as a population-averaged model, was employed. This approach averages out individual effects and provides consistent and asymptotically normal estimates. The basic model equation is as follows:

HE_"it" =β_"0" +〖"β*" 〗_(1 ) Death_it+ β_"n " 〖SES_Cov〗_"it " +ε_(it )

Where, 〖HE〗_it captures the household expenditure at household level, where 〖Death〗_it is the main predictor variable, representing the death of an individual in the household. β_1 is the slope coefficient associated with the Death_it, which quantifies the expected change in the household health expenditure (〖HE〗_it) associated with the variable Death_it in the household. β_n represents a matrix of coefficients for ‘n’ socio-economic covariates 〖(SES_Cov〗_it) used in the regression model as control variables.

We utilized a combined approach of pooled ordinary least squares (OLS) regression and fixed effects (FE) panel models to focus on within-household variations over time. This method helps address potential biases arising from unobserved household-specific factors, particularly relevant when analysing health expenditure in households with higher death rates. These models are particularly suitable for the dataset used in this study, as suggested by Brüderl and colleagues (2015), because they eliminate between-household variation and focus solely on within-household variation. The basic model is outlined as follows:

HE_"it" =β_"i" +〖"β*" 〗_(1 ) Death_it+ β_"n " 〖SES_Cov〗_"it " +δ_t+ u_it+ε_(it )

where β captures how the outcome, in our case the health expenditure (〖HE〗_it), changes after a household change in the main independent variable, in our case death status (Death_it). The restrictive assumption of this model is that the unobserved covariates are time-constant (δ_t). In the model, 〖HE〗_it is the health expenditure at the household level and β_i represents individual fixed effects, capturing unobservable characteristics specific to each household that do not change over time. Therefore, this model accounts for individual-specific characteristics β_i and time-varying, but household-invariant factors u_it, providing a more nuanced understanding of the relationship.

The basic equation of the probit model used above is;

P (HE_"it" =1)=φ (β_"0" +〖"β*" 〗_(1 ) Death_it+ β_"n " 〖SES_Cov〗_"it " +ε_(it )

Where, P (HE_"it" =1) is the probability that health expenditure HE_"it" at household (i) and time (t) is equal to 1. In the Probit model, we are modeling the probability of a binary outcome (in this case, health spending being 1 means “spending” on health care while the other case would be “no spending”). Additionally, {φ (.)} is the cumulative distribution function of the standard normal distribution evaluated at the linear combination of the model's parameters and the error term. Thus, the above Probit Model provides insights into the relationship between the death of an individual within a household and the probability of incurring health expenditure, using the cumulative distribution function of the standard normal distribution to model this relationship.

The basic equation of the Tobit Model used above is;

〖HE〗_it*=β_"i" +〖"β*" 〗_(1 ) Death_it+ β_"n " 〖SES_Cov〗_"it " +u_(it )+ε_(it )

Where the condition,

〖HE〗_it= 〖HE〗_it* if 〖HE〗_it*>0

and,

〖HE〗_it=0 if 〖HE〗_it*<0

〖HE〗_it* is the latent variable representing the household expenditure before censoring. It's a linear combination of the model parameters, including individual fixed effects β_(i ), the effect of death status 〖"β*" 〗_(1 ) Death_it, and other factors u_(it ). The model is conditioned by 〖HE〗_it, which is the observed household expenditure, which is subject to censoring if 〖HE〗_it*>0 then the observed expenditure is the same as the latent variable 〖(HE〗_it=〖HE〗_it*). However, if 〖HE〗_it*<0, then 〖HE〗_it=0, implying that the household did not incur any health expenditure.

Therefore, the Tobit model is valuable when dealing with data that includes a substantial number of observations with zero values (censoring). By estimating the latent variable 〖HE〗_it*, the model accounts for both the observed expenditure and the censored cases where expenditure is zero. Also, β_(1 )indicates the change in the latent variable associated with a unit change in the death status variable, providing insights into how death status influences household expenditure in situations where censoring may occur.

Descriptive Statistics

Table 1 presents descriptive statistics for a sample of 600,000 individuals across representative households from the 2018–2023 panel. It provides insights into member status (alive or deceased), health expenditure, and various household demographic and socioeconomic characteristics. The mean health expenditure amounts to Rs. 534.67 ($6.44), with the highest recorded expenditure reaching Rs. 572,050 ($6,874.04). The average age of the study subjects is 34 years, and the death rate among panel individuals stands at 1.0% of the sample (n=3,338,623). It is noteworthy that 97% of individuals self-identify as 'healthy,' while only 36% possess health insurance coverage. Hospitalization records indicate that 0.1% (n=2,268,434) of individuals have reported admissions. In terms of gender distribution among study participants, 46% are female and 54% are male. On average, approximately 43% of individuals have achieved a secondary level of education. Marital status reveals that approximately 59% of individuals are 'ever-married,' surpassing the proportion of singles at around 33%.

The descriptive analysis highlights a non-linear distribution in household sizes, with health expenditure proportions varying across different household compositions. For instance, single-member households allocate 29.5% of expenditure to health, while households with 2 members allocate only 0.05%. Additionally, the introduction of the Marital Status variable after 2018 affects the sample size for this variable.

Implications of the study

Studying household expenditure in India, considering rational and emotional factors, is vital due to extended family structures and high emotional engagement. Large-scale panel studies are uncommon in developing countries, making our research involving 6.5 million individuals significant. Our goal is to identify causation on a broad scale, aiding policymakers in lowering out-of-pocket healthcare costs to achieve the Sustainable Development Goals by 2030.