Has India eliminated extreme poverty?

In the second post of a six-part series on estimating poverty in India, Gaurav Datt unveils the claim that India was on the verge of eradicating extreme poverty and challenges two key assumptions on which it is based. Rather, it shows that the survey catch rate has declined, while top income shares have increased over the past decade. Finally, it presents some alternative estimates, which call into question the elimination of extreme poverty.

The World Bank uses the standard of $1.90 per person per day (in US dollars at 2011 purchasing power parity) to monitor levels of extreme poverty around the world. According to this standard, 22% of the Indian population was considered to live in extreme poverty in 2011-2012. Although a decade has passed, more recent estimates of poverty in India remain embroiled in controversy. The reason for this is the lack of comparable data from the National Sample Survey (NSS) Consumer Expenditure Surveys (CES), which have been the mainstay of poverty measurement in India for decades. (Datt et al. 2020).

The last publicly available round of the NSS consumer survey was for the year 2011-12. Another round of the survey was conducted for the year 2017-18, but neither the results nor the data from this survey have been released, with the government citing “data quality issuesas the reason for its deletion. But leaked tabulations of the investigation – which indicated a stagnation of national poverty and an increase in rural poverty since 2011-12 – led many observers to question whether the decision (taken months before the 2019 general election) was politically motivated.

However, the lack of data has led to a number of attempts to estimate poverty levels after 2012 in India using various imputation methods and/or alternative data sources.1 Most of them point to a decline in poverty since 2012, but at a slower pace than observed for the previous decade, which seems consistent with India’s slower growth especially since 2016-17.

An exception to this rule is the IMF’s recent working paper Bhalla, Bhasin and Virmani (hereafter BBV), which declared the virtual elimination of extreme poverty in India. To put it in their own words: “According to this standard [the World Bank’s $1.90 poverty line]India can reasonably say that before the pandemic [sic] India was on the verge of eradicating extreme poverty”.

There are also other claims in the document regarding the decline in inequality and the negligible impact of the pandemic on poverty, but I focus on the claim of the virtual elimination of extreme poverty before the pandemic. Does it stand up to reasonable scrutiny? To answer this question, we need to unpack the claim.

How precise are the poverty estimates?

BBV presents two series of poverty estimates: with or without taking into account food transfers through the Public Distribution System (PDS). Without food transfers, they claim that poverty fell from 21.8% in 2011-12 (not much different from the aforementioned estimate of 22%) to 3.4% in 2019-20; with food transfers, the estimated reduction in poverty is from 19.9% ​​to 1.9%. Thus, it suffices that the claim of virtual elimination of extreme poverty be based on estimated progress even without food transfers; the evaluation of the subsidy component of food transfers is just icing on the cake. So, let’s focus on the predicted drop in poverty from 22% in 2011-12 to 3% in 2019-20.

BBV’s methodology for arriving at these numbers is relatively simple.

  1. Start with the consumption distribution from the latest NSS consumption survey for 2011-12.
  2. Suppose that average consumption per capita in nominal terms has increased at the same rate as private final consumption expenditure (PFCE) per capita in the national accounts (NA).
  3. Suppose that each household’s per capita consumption grew at the same rate as the average per capita consumption, to arrive at the “updated” distribution for each subsequent year after 2011-12 (which, except for the average, is the same as the 2011-12 breakdown).
  4. Estimate the poverty measures for each subsequent year using the “updated” distribution from stage three and the 2011-12 poverty line updated by the Consumer Price Index (CPI).

Thus, BBV effectively assumes distribution-neutral growth since 2011-12 at a rate given by NA growth in private consumption per capita.2 BBV defends its first hypothesis by arguing that “investigative capture [the ratio of survey-to-NA consumption] in India “normalized” to around 50% in 2004-5, 2007-8 (a small sample NSS survey), 2009-10 and 2011-12 according to the URP [Uniform Recall Period] method”. The problem is that there is no evidence for this.

Figure 1 shows the survey catch rate (also called the “transmission” rate) over almost sixty years based on the work of Datt et al. (2020). It has been falling rapidly since the mid-1980s, with no sign of stability either since 2004-05. Even using the new revised national accounts retrospective series, the survey catch rate was 48, 45 and 46% for the years 2004-05, 2009-10 and 2011-12 respectively. This is hardly the evidence on which to hang the ratio’s projected constancy for the next decade, especially in light of its secular decline over the previous four decades.

Figure 1. Decreasing ratio of per capita consumption between survey and NA (nominal)

Source: Based on data from Datt et al. (2020).

Notes: i) The graph shows the ratio of the nominal values ​​of consumption per capita from the NSS surveys to that of the national accounts (NA). ii) To mesh the survey and NA data, the latter (available on an annual basis) are interpolated linearly at the midpoint of the survey period for each round of the NSS. iii) Per capita consumption survey estimates refer to those based on the uniform recall period. iv) New series (NS) refers to the revised retrospective series of NA after the base was shifted to 2011-12.

The decline in the survey catch rate is closely related to the violation of BBV’s second assumption. The capture rate of surveys has declined to a large extent precisely because surveys fail to capture the upper end of the income distribution, while the highest income shares have increased rapidly. Estimates by Chancel and Piketty (2019) indicate that missing top earners account for a large and growing share of the middle earner gap in the NA and surveys, which was around 45% in 2014-15 (Figure 2 ).

Figure 2. Missing top incomes as a proportion of the NA survey income gap (left panel) and increasing income inequality (right panel)

Source: Chancel and Piketty (2019)

The two hypotheses are therefore poorly justified.

Drop those assumptions and the whole house of cards built by BBV comes crashing down. Alternative estimates that relax the first assumption and use a survey capture ratio of 0.67 put the extreme poverty rate for FY 2017-18 at around 10%. Other estimates that use alternative (re-weighted) consumer pyramid household surveys (CPHS) data since 2014, and survey-to-survey imputation methods3 to derive consumption levels comparable to NSS surveys, also arrive at a similar estimate of extreme poverty of around 10% for 2019-20 (Roy and van der Weide 2022). These alternative estimates are not perfect, but they cast significant doubts on the claim of the eradication of extreme poverty in India.


As early as 1988, BS Minhas warned against pro rata adjustment (or proportional) to the distribution of consumption based on the survey in the documents of the seventh five-year plan:

“…it is dangerous to make a pro rata adjustment to the observed size distribution of consumer spending in a particular NSS cycle by multiplying it by a scalar derived from the ratio of the NAS [National Accounts Statistics] estimate of aggregate private consumption (for a given financial year) and total household expenditure available from the NSS cycle. This kind of senseless tinkering with the distribution of the NSS, as practiced by the Planning Commission in the Seventh Plan documents, does not seem permissible either in theory or in the light of known facts.

Minhas was referring to a pro rata adjustment of levels. BBV estimates are a pro rata adjustment using growth rates (i.e. multiplying the base distribution of the NSS by a growth scalar given by the growth in per capita consumption from the NAS) , which is also dangerous when applied over a period of ten years. It is unfortunate that the shortcomings of the Indian statistical system and the lack of comparable data have created an environment in which such tinkering has continued more than three decades later.


  1. See, for example, Newhouse and Vyas (2019), Edochie et al. (2022) and Roy and van der Weide (2022).
  2. BBV also presents a version assuming neutrality of distribution at the state level. They also have another version of their estimates using the NSS’s Modified Mixed Recall Period (MMRP) consumption measures.
  3. These methods involve the imputation of consumption in a target survey without a comparable measurement of consumption in the source survey. This is done either by using an estimable relationship between consumption and a set of common household characteristics in the two surveys, or by estimating a relationship between the measurement of consumption in the two surveys.

Further reading

  • Bhalla S, K Bhasin and A Virmani (2022), ‘Pandemic, poverty and inequality: evidence from India‘, International Monetary Fund, IMF Working Paper No. 2022/069.
  • Chancel, Lucas and Thomas Piketty (2019), “Indian Income Inequality, 1922-2015: From British Raj to Billionaire Raj?”, The income and wealth test65(S1): S33-S62.
  • Datt, Gaurav, Martin Ravallion and Rinku Murgai (2020), “Poverty and Growth in India over Six Decades”, American Journal of Agricultural Economics, 102(1): 4-27. Available here.
  • Edochie, Ifeanyi Nzegwu, Freije-Rodriguez, Samuel, Lakner, Christoph, Herrera, Laura Moreno, Newhouse, David, Roy, Sutirtha Sinha and Yonzan, Nishant (2022). What do we know about poverty in India in 2017/18? World Bank Policy Research Working Paper 9931.
  • Minhas, BS (1988), “Validation of the large-scale sample survey data case of NSS estimates of household consumption expenditure”, Sankhyā: The Indian Journal of Statistics, Series B (1960-2002)50(3): 279-326.
  • Newhouse, David Locke and Vyas, Pallavi (2019). Estimating poverty in India without expenditure data: a survey-to-survey imputation approach. World Bank Policy Research Working Paper 8878.
  • Roy, SS and R van der Weide (2022), ‘Poverty in India has declined over the past decade, but not as much as previously thought‘, World Bank, Policy Research Working Paper 9994.

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