Syllabus: GS2- Issues related to Poverty
Source: The Hindu article – 16/8/2023
Context:
- NITI Aayog recently released a report on multidimensional poverty and it shows that the percentage of the poor has gone down and around 135 million people were lifted out of poverty during this period. Also The Global Multidimensional Poverty Index report of 2023 which was released recently, also shows that the incidence of the multidimensional poverty index declined.
Content:
Multidimensional poverty estimates
- Historically, poverty estimation has predominantly relied on income as the sole indicator. The Global Multidimensional Poverty Index (MPI), based on the Alkire-Foster (AF) methodology, captures overlapping deprivations in health, education, and living standards.
The Alkire-Foster (AF) method is a way of measuring multidimensional poverty developed by Oxford Poverty and Human Development Initiative (OPHI’s) Sabina Alkire and James Foster. It involves counting the different types of deprivation that individuals experience at the same time, such as a lack of education or employment, or poor health or living standards. These deprivation profiles are analysed to identify who is poor, and then used to construct a multidimensional index of poverty (MPI).
- The global MPI Report is jointly published by the Oxford Poverty and Human Development Initiative (OPHI) and the United Nations Development Programme (UNDP).
- India’s National MPI also has three equally weighted dimensions – Health, Education, and Standard of living – which are represented by 12 indicators.
- National MPI is published by NITI Aayog
- The MPI data for 2023 shows a significant reduction in the incidence of multidimensional poverty in India over a specific period.
Consumption-based poverty estimates
- Consumption-based poverty estimates are a way to measure the proportion of the population living below a defined poverty line based on their consumption patterns.
- These estimates are derived from surveys like the National Sample Survey (NSS) and are based on income and expenditure data.
- One example of this methodology is the Tendulkar committee methodology, which uses consumer expenditure to estimate poverty.
Why Consumption-based poverty estimates have relevance ?
- Same conclusion derived from both MPI and Consumption-based poverty estimates:
- The report of the Global Multidimensional Poverty Index (MPI) 2018 says that India has made momentous progress in reducing multidimensional poverty.
- The estimates of poverty based on consumer expenditure using the Tendulkar committee methodology and the Rangarajan Committee methodology also show that the number of poor came down in India despite an increase in population.
- The multidimensional indicators/measures raise several issues regarding their measurability and databases . For example, there is a problem with the child mortality indicator as it is for population groups and not for households.
- Aggregation is another problem. In principle, indicators should be independent. Eg: Access to safe drinking water cannot be aggregated with indicators such as child mortality.
- Converting all the non income indicators such as education, health, sanitation, drinking water, and child mortality into a single index makes little sense.
- Defining poverty in terms of income or in the absence of such data in terms of expenditure seems most appropriate, and this method is followed in most countries.
Way forward
- While multidimensional poverty indices offer insights into various dimensions of deprivation, consumption-based poverty estimates remain relevant. Multidimensional poverty estimates are not substitutes for National Sample Survey (NSS) consumption-based poverty ratios
- There is also a need to correct concerns about consumption expenditure surveys that aid in informed policy formulation.
Reference
- https://www.thehindu.com/opinion/lead/consumption-based-poverty-estimates-have-relevance/article67198333.ece
- https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://niti.gov.in/sites/default/files/2023-07/National-Multidimentional-Poverty-Index-2023-Final-17th-July.pdf&ved=2ahUKEwjBosDam-OAAxWX1jgGHal6CbgQFnoECCMQAQ&usg=AOvVaw13g0jPPWadQe4sIrHg0RDu