Abstract
Purpose
Despite significant contribution by India’s informal sector, tattered conditions have inflated the burden of health shocks in many ways. This study tries to examine the economic burden of health shocks and its associated consequences on households whose members are involved in informal sector. We primarily focus on three objectives for our analysis: (1) compute distribution and magnitude of health shocks and health expenditure between formal and informal workers; (2) evaluate the incidence and intensity of catastrophic health expenditure (CHE), and measure its impoverishment effect; (3) estimate the major determinants of CHE for informal sector households.
Methods
Underlying objectives have been estimated using standard catastrophic and impoverishment measures (poverty headcount and poverty gap) and Poisson, logit and Tobit multivariate regression models. For empirical analysis, data is exploited from the recent round of Indian Human Development Survey (IHDS-II), 2012.
Results
We find that around 27% of households engaged in the informal sector spends more than 5% threshold on their health payment. We also find that OOP health expenditure pushes 7.12% informal sector households in poverty. Moreover, we also find that the impoverishment effect mainly rests on outpatient health expenditure and poverty deepening for informal sector households.
Conclusion
Our findings indicate that informal sector workers are highly vulnerable to health shocks and economic burden in terms of high treatment costs and low insurance coverage. Further, we also show that workers engaged in the informal sector witness greater probability of incurring CHE and impoverishment. Results from the Tobit model suggests that various factors such as insurance coverage, severity of illness and others are crucial predictor of catastrophic spending.
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Notes
Ill-health is a state of inferior physical or mental condition in which some disease or impairment is present where people are unable to function normally and without pain.
It holds to be less than one-tenth per worker of an organised sector.
Burden of diseases are evaluated mainly in terms of prevalence rate, treatment, type of treatment and cost of treatment.
However, In India and in other developing countries of Asia, the “unorganised” segment of the economy closely approximates the ICLS concept of the informal sector (ILO 1993).
Low share of informal sector may also owe due to the nonavailability of adequate infrastructure as well as an appropriate amount of resources unlike that in agricultural activities which can be undertaken without a lot of these prerequisites.
The study mainly focuses on IS workers where the major concentration would be on the nonagricultural sectors in India.
Safety net is in terms of access to better wages, job security, maternal and child health care benefits, retirement benefits and other such benefits.
All types of STM, like fever, cough and diarrhoea imposes the highest burden on IS workers.
Robustness is a check so as to see if the burden of disease differs statistically between the formal and informal sectors.
It was completed during 2011–2012. IHDS-II is a nationally representative, multi-topic survey produced by the National Council of Applied Economic Research (NCAER), New Delhi, and by the University of Maryland. It covers around 42,152 households and 204,568 individuals across 1,503 villages and 971 urban neighbourhoods in India.
The information on total household expenditure is given in the data, while the variable, non-food expenditure of the households are generated as total household expenditure net of foods expenditures. Moreover, foods expenditure variable are generated by summing all expenditures on foods.
Note that because of the rapid inflation during 2012 while the survey was being carried out, IHDS-II adjusted the household poverty line for the month of the interview.
Whether the surveyed household belongs to rural or urban areas
All measures are estimated by considering the number of household members to receive the more accurate results and reduce the household size bias.
Where x is total food consumption of the household, for more detail see the data section.
The value of threshold (z) represents the point at which the absorption of household resources by spending on health care is considered to impose a severe disruption to living standards. The study employs a different level of threshold (z) such as 5, 10, 15, 20, 25 and 30% for both the denominator total household expenditure (x) as well as non-food expenditure (y− x).
Nonfood expenditure is calculated after subtracting food expenditure from total expenditure.
It captures the average amount by which expenditure on healthcare (as a proportion) exceeds the chosen threshold.
H captures the incidence of CHE and O reflects the intensity of that catastrophe occurrence.
The given figure is a variant on Jan Pen’s “parade of dwarfs and a few giants” (Wagstaff and Doorslaer 2003).
For easy understanding, we assume that households keep the same rank in the gross and net of OOP expenditure distribution.
Where X i denotes the household per capita expenditure and PL is poverty line.
A variable is called limited which is continuous over most of its observation but contains the mass of it as one specific value, for instance zero in case of health spending.
Mainly due to several financial and economic constraints.
This is the total health expenditure including doctor fees, medicine, travel and other expenditure related to taking care of a patient (in rupees).
It means that with this sample there are around 25% of households with health care payments as a share of their total expenditure exceeding the chosen (5%) threshold.
Mean gap or measure of overshoot (O) measures the average degree by which health expenditure (as proportion) exceed the selected threshold (z).
Like headcount, overshoot is comparatively lower at higher threshold.
It means that those household spending more than 5% of budget share on health, health expenditure, on an average, is 3% higher.
These measures are evaluated both before and after accounting for healthcare expenditure.
It may holds in case of short-term but frequent morbidity inclusion of other expenditure like travel, foods and patient care.
The Tobit model estimates showing the over all factors of monthly per capita consumption expenditure (MPCE), insurance coverage, assets ownership, severity of illness, age structure of household members (presence of number of child and number of elderly in the household), type of treatment, distance of treatment location, waiting time for treatment and accompanying other family members for treatment are found to be statistically significant predictors of response variables.
However, other control variables such as pension of any member, income from other sources and member of any village group does not have any clear and significant effects on response variables.
Non-communicable diseases like high blood pressure, heart disease, diabetes and cancer are more prevalent among FS workers.
such as monthly per capita consumption, severity of illness, presence of child and elderly members in the households, treatment in private hospital, distance of treatment location, waiting time for the treatment and whether accompanied by other members during treatment
This means that different groups of people get access to health services in equal proportion according to their needs.
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The authors declare that there are no potential competing interests of any nature. It is also stated that Nadeem Ahmad and Khushboo Aggarwal have made substantial contributions to the conception and design of the paper and have been involved in drafting the manuscript. All authors have given final approval of the version to be published. This study was not funded by any institution or person.
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Ahmad, N., Aggarwal, K. Health shock, catastrophic expenditure and its consequences on welfare of the household engaged in informal sector. J Public Health 25, 611–624 (2017). https://doi.org/10.1007/s10389-017-0829-9
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DOI: https://doi.org/10.1007/s10389-017-0829-9