OFFICIAL STATISTICS SPECIALLY DESIGNED FOR INDIAN STATISTICAL SERVICE (ISS) AND OTHER GOVERNMENT & PRIVATE SECTOR STATISTICAL EXAMINATIONS
Get the complete book, written for ISS aspirants and every statistical exam that tests Indian official statistics.
Buy the book on Amazon.inMultidimensional Poverty Index in India: Dimensions, Indicators and ISS Exam Guide
Poverty is not always adequately described by a single monetary threshold. Multidimensional poverty statistics combine several indicators of deprivation so that analysts can study overlapping disadvantages in health, education and living standards.
1. What is multidimensional poverty?
Multidimensional poverty refers to simultaneous deprivation in more than one dimension of well-being. A multidimensional index specifies dimensions, indicators, deprivation cut-offs and weights, and then combines them using a transparent identification and aggregation rule.
Multidimensional Poverty Index in India connects definitions, indicators, denominators and official-data interpretation. ISS questions often test whether a candidate can distinguish a statistical concept from the administrative or policy process behind it.
2. Dimensions and indicators
Typical dimensions include health, education and living standards, with indicators such as nutrition, schooling, sanitation, drinking water, housing, electricity or assets depending on the framework. The exact indicators and weights must be stated because different indices are not automatically comparable.
Broad domains of well-being such as health or education.
Specific measurable deprivation within a dimension.
Rule used to identify a household as deprived.
Average share of weighted deprivations among the identified poor.
3. Identification of a multidimensionally poor household
A household is identified using its weighted deprivation score and a chosen poverty cut-off. If the weighted sum of deprivations reaches the specified threshold, the household is classified as multidimensionally poor under that methodology.
In numerical questions, write the formula first, identify the numerator and denominator, and state the unit. In descriptive answers, define the indicator before discussing its uses or limitations.
4. Aggregation and MPI
A multidimensional poverty index can combine the incidence of poverty with the intensity of deprivation among those identified as poor. A useful exam answer should separate the identification rule from the aggregation rule and explain what each component contributes.
5. Interpretation and data quality
MPI values should be interpreted with their survey source, reference period, indicator definitions and weights. Missing observations, household-level versus individual-level indicators and changes in questionnaire design can influence the resulting estimate.
7. Three important questions
Try each one on your own first, then tap the answer.
Why is a multidimensional approach useful?
Show answer
It captures simultaneous deprivations that may not be visible from a single income or consumption measure.
A household has weighted deprivation scores of 0.10, 0.20, 0.15 and 0.10. If the identification cut-off is 0.50, is it classified as poor?
Show answer
The total is 0.55. Since 0.55 is at least 0.50, it is identified as multidimensionally poor under the stated rule.
Explain the main statistical steps in constructing a multidimensional poverty index.
Show answer
Define dimensions and indicators, establish deprivation cut-offs, assign weights, calculate household deprivation scores, apply the poverty cut-off, and aggregate incidence and intensity into the final index.
Key takeaways
- MPI depends on dimensions, indicators, cut-offs and weights.
- Identification and aggregation are separate statistical steps.
- A multidimensional index complements rather than automatically replaces monetary poverty measures.
- Source, survey design and reference period must accompany published estimates.
Everything on Official Statistics, in one book
CPI is just one chapter of the syllabus. If you want the complete picture, this book is built for you.
- Indian official statistical system: MoSPI, NSO, NSC and the data they produce
- National accounts, GDP, IIP, price indices and labour statistics
- NSS surveys, sampling designs and data quality concepts
- Written specially for the ISS exam, and useful for other government and private sector statistical exams
- Concept-focused notes to revise quickly before the exam
Official Statistics for ISS and other Government & Private Sector Statistical Examinations