Source: The Hindu
- Prelims: Current events of national importance(Different social service Schemes, MPI)
- Mains GS Paper I & II: Social empowerment, development and management of social sectors/services related to Health, poverty and hunger etc
ARTICLE HIGHLIGHTS
- Based on multidimensional poverty measurement, the Poverty Ratio (Head Count Ratio) in Tamil Nadu declined from 4.89% in 2015-16 to 1.57% in 2020-21, based on the fourth and fifth rounds of the National Family Health Survey (NFHS) data.
- NITI Aayog, armed with a fairly large sample survey data of NFHS 4 (with more than six lakh households in India), estimated the Multidimensional Poverty Index (MPI) and published the baseline report in 2021.
INSIGHTS ON THE ISSUE
Context
National Family Health Survey (NFHS)
- It is a large-scale, multi-round survey conducted in a representative sample of households throughout India.
- It comprises detailed information on key domains:
- Population
- Health
- Family Welfare
- Associated domains:
- Characteristics of the population
- Fertility
- Family planning
- Infant and Child mortality
- Maternal and Child health
- Nutrition and Anemia
- Morbidity and Healthcare
- Women’s Empowerment etc.
- It also provides data by socio-economic and other background characteristics which are useful for policy formulation and effective programme implementation.
- The main objective of successive rounds of the NFHS has been due to its reliable and comparable data relating to health and family welfare and other socio-economic emerging areas in India.
NFHS-5 Report:
- The NFHS-5 National Report lists progress from NFHS-4 (2015-16) to NFHS-5 (2019-21).
- The scope of NFHS-5 was expanded by adding new dimensions in the earlier round of the survey (NFHS-4) such as:
- Death registration
- Pre-school education
- Expanded domains of child immunization
- Components of micro-nutrients to children
- Menstrual hygiene
- Frequency of alcohol and tobacco use
- Additional components of Non-Communicable Diseases (NCDs)
- Expanded age range for measuring hypertension
- Diabetes among all aged 15 years and above.
- It provides information on important indicators which are helpful in tracking the progress of Sustainable Development Goals (SDGs) in the country.
Multidimensional Poverty Index (MPI) by NITI Ayog:
- NITI Aayog, armed with a fairly large sample survey data of NFHS 4 (with more than six lakh households in India), estimated the Multidimensional Poverty Index (MPI) and published the baseline report in 2021.
- The MPI is a product of:
- Head Count Ratio
- Intensity of Poverty.
- Poverty is the outcome of simultaneous deprivations in multiple functions: The rationale for the MPI was derived from the concept that poverty is the outcome of simultaneous deprivations in multiple functions such as:
- attainments in health
- education
- standard of living.
- Weighted average across 12 indicators: The NITI Aayog identified 12 indicators in these three sectors and calculated the weighted average of deprivations in each of these 12 indicators for all men and women surveyed in NFHS 4.
- If an individual’s aggregate weighted deprivation score was more than 0.33, they were considered multidimensionally poor.
- The proportion of the population with a deprivation score greater than 0.33 to the total population is defined as the Poverty Ratio or Head Count Ratio.
- Estimation of the Intensity of Poverty: This is the weighted-average deprivation score of the multidimensionally poor.
- For instance, the Intensity of Poverty in Tamil Nadu declined from 39.97% to 38.78% during this period, indicating that the summary measure of multiple deprivations of the poor has only marginally declined in these five years, and has to be underlined for policy focus.
- Greater decline in Head Count Ratio compared to Intensity of Poverty: The MPI for Tamil Nadu declined from 0.020 to 0.006.
- This sharp decline in MPI is largely due to a greater decline in Head Count Ratio compared to Intensity of Poverty.
- This gives a clue that any further decline in MPI in Tamil Nadu should happen only by addressing all the dimensions of poverty and reducing its intensity substantially across the State.
Direction of intervention:
- Overall population deprived: The deprivation estimation also indicates that the overall population that has been identified as deprived in most of the indicators individually is higher than the population identified as multidimensionally poor.
- Deprived severely in a few functions: This once again reiterates the point that people may be deprived severely in a few functions, but may not be multidimensionally poor.
- This adds another aspect of public policy intervention, i.e., attacking poverty in Tamil Nadu should not only be multidimensional but also universal.
- Only this approach can address deprivations in all the indicators.
- This will also surely and squarely reduce the Intensity of Poverty in Tamil Nadu.
- Usefulness of the MPI: Statistically, the Head Count Ratio and Intensity of Poverty can be calculated for each district and segregated by gender, rural and urban, and other dimensions.
- Therefore, the usefulness of the MPI and its components is enormous in terms of understanding poverty in its totality as well as the granular details that are essential for sectoral and spatial policy and programmatic interventions.
- Quality of survey data: The strength of the MPI as an instrument for data-driven public policy depends on the quality of survey data, namely the NFHS data.
Quality of NFHS data:
- National Sample Survey Organisation’s (NSSO) sample surveys: The National Sample Survey Organisation’s (NSSO) sample surveys have been debated among economists and statisticians, both in terms of sampling and non-sample errors, right from its initial days in the 1950s.
- NSSO’s methodologies: Following several review reports on the NSSO’s methodologies, the NSSO has been attempting to improve sampling design and reduce non-sampling errors, particularly with reference to recall periods for providing consumption expenditure by households.
- Errors in NFHS data: Demographers have written several articles on the non-sampling errors in different rounds of the NFHS data.
- They tested, for instance:
- the arbitrariness in reporting the age of the dead
- differences in data quality between educated and uneducated respondents
- data quality based on differences in time taken to complete a survey of different household types, etc.
- All these have serious implications for health data such as fertility and death rates.
- They tested, for instance:
- Market-based approach: A market-based approach to decide the data collection process is also critiqued by demographers.
- In Tamil Nadu, the NFHS data was collected in two time periods: 8,382 households (30%) in the pre-pandemic period and 19,547 households (70%) in the post-lockdown period, aggregating to 27,929 households for the State.
- The pandemic has resulted in increasing pregnancy among women below the age of 21 years, more so among teenage girls.
- Death per 1,000 households surveyed increased from 118.23 to 135.01
- This is clear evidence of the impact of the pandemic.
- Estimations of the Head Count Ratios for the 12 indicators: It was found that such ratios were lower in the post-lockdown period than in the pre-pandemic period:
- Leading to the inference that post-lockdown
- The deprivation in several functionings was lower
- Implying a lower poverty ratio as well as Intensity of Poverty.
- In particular, the deprivation in terms of nutrition and maternal health declined, and schooling and school attendance increased in the post-lockdown period.
- Increase in deprivation in nutrition and maternal health: Substitution of dry rations for hot meals in the mid-day meal programmes and high pressures in hospitals in handling COVID-19 cases are expected to increase deprivation in nutrition and maternal health in the post-lockdown period, contrary to the decline in deprivation in nutrition and maternal health in the post-pandemic period that we derived from this database.
- Tamil Nadu is known to have increased enrolment and reduced the dropout rate year after year; hence, the increase in deprivation in terms of schooling should raise questions.
Causes of Poverty:
Reasons for prevalent malnutrition in India:
- Monoculture agricultural practices: While foodgrain production has increased over five times since Independence, it has not sufficiently addressed the issue of malnutrition.
- Poverty: Though poverty alone does not lead to malnutrition, it affects the availability of adequate amounts of nutritious food for the most vulnerable populations.
- Lack of sanitation and clean drinking water: Lack of potable water, poor sanitation, and dangerous hygiene practices increase vulnerability to infectious and water-borne diseases, which are direct causes of acute malnutrition.
- Migration: Seasonal migrations have long been a livelihood strategy for the poorest households in India, as a means to access food and money through casual labour.
- However, children and women are the most affected, suffering from deprivation during migrations impacting their health condition.
- Gender injustice: There is a correlation between gender discrimination and poor nutrition.
- Malnourished girls become malnourished adolescents who marry early and have children who become malnourished, and so the cycle continues.
- Lacunae at policy level: There is a lack of real-time data that brings all these factors together to show the extent of India’s malnutrition.
- Lax implementation: Providing nutritious food to the country’s children is more a matter of political will and effective policy implementation at the grassroots level.
- For example, the Acute Encephalitis Syndrome (AES) outbreak in Bihar marked the failure of the Integrated Child Development Scheme (ICDS) in the state.
Government initiatives:
Way Forward
- Combined survey data from two different time periods: Data separated by a major pandemic have to be approached with caution while interpreting the statistics derived from the entire database.
- Assuming that survey data are from a single time period, it is normal to compare the results of survey data on specific indicators, with the programmatic data derived from official records.
- Address deprivations across the entire population: In order to reduce the Intensity of Poverty we need to address deprivations across the entire population, that is there should be a universal approach instead of a targeted approach to addressing it.
- Programmatic interventions should be curated with ground-level realities: The survey data gives us only broad policy pointers whereas programmatic interventions should be curated with ground-level realities.
- At the same time, continuous engagement with survey data in terms of improving the sample design and response quality has to be sustained.
- Data Analysis: Analyzing the data and finding the incongruence of inferences from different databases on an issue would help improve data gathering systems.
- Evaluate programmes: There should be a process to monitor and evaluate programmes and address systemic and on the ground challenges.
- Recommend that a new or existing committee or the relevant standing committees meet and deliberate over effective policy decisions,monitor the implementation of schemes, and review nutritional status across States.
- Planning, programmes, monitoring, training and procurement: There is a need to strengthen the coordination of all its aspects, focusing on planning, programmes, monitoring, training and procurement.
QUESTION FOR PRACTICE
- Can the vicious cycle of gender inequality, poverty and malnutrition be broken through microfinancing of women SHGs? Explain with examples.(UPSC 2021)
(200 WORDS, 10 MARKS)
- There needs to be engagement with survey data, but ground-level realities should shape programmatic interventions. Critically analyze.
(200 WORDS, 10 MARKS)
