A Behavior‐Based Approach to the Estimation of Poverty in India

Published date01 January 2019
Date01 January 2019
DOIhttp://doi.org/10.1111/sjoe.12282
Scand. J. of Economics 121(1), 182–224, 2019
DOI: 10.1111/sjoe.12282
A Behavior-Based Approach to the
Estimation of Poverty in India*
Ingvild Alm˚as
Stockholm University,SE-106 91 Stockholm, Sweden
ingvild.almas@iies.su.se
Anders Kjelsrud
University of Oslo, NO-0851 Oslo, Norway
anders.gron.kjelsrud@econ.uio.no
Rohini Somanathan
Delhi School of Economics, 110007 Delhi, India
rohini@econdse.org
Abstract
Estimates of poverty in India are crucial inputs for the understanding of world poverty, yet there
is much disagreement about the numbers and the legitimacy of methods used to derive them. In
this paper, we propose and justify an alternative approach to identify the poor, which uses the
proportion of income spent on food. Our estimates have weaker data requirements than official
methods, and they compare favorably with several validation tests. Most notably, households
around our state poverty lines obtain their calories from similar sources, whereas this is not true
of official poverty lines. We also find that rates of self-reported hunger are higher in states that
we classify as poor.
Keywords: Cost of living;Engel cur ves;measurement
JEL classification:D1; E31; F01; I32
I. Introduction
Almost a sixth of the world’s population and a large fraction of its poor live
in India. Therefore, estimates of poverty in India are crucial inputs for the
understanding of world poverty trends.Yet there is much disagreement about
the numbers and the legitimacy of methods used to derive these estimates.
Since the 1990s, separate official poverty lines have been published for
urban and rural regions of each Indian state to reflect the spatial variation
*We thank OrazioAttanasio, Ragnhild Balsvik, Arne Bigsten, Richard Blundell, Ian Crawford,
Gernot Doppelhofer, Abhiroop Mukhopadhyay, Ragnar Nymoen, and Fabien Postel-Vinay for
useful comments.The paper is part of the research activities at the Centre for the Study of Equality,
Social Organization, and Performance (ESOP) at the Department of Economics, University of
Oslo.
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The editors of The Scandinavian Journal of Economics 2017.
I. Alm˚as, A. Kjelsrud, and R. Somanathan 183
in the cost of living. These estimates have been highly controversial and,
over the past decade, two independent commissions have suggested new
methods for estimating regional prices based on micro price data from
consumption surveys. The first revision, proposed in 2009, resulted in a 50
percent increase in rural head counts for 2004–2005. The second revision,
published in 2014, has resulted in overall poverty rates for 2011–2012 that
are 35 percent higher than estimates based on the 2009 methodology. The
debate on poverty measurement in India is especially charged with the
political rhetoric of poverty eradication accompanying the expanding and
fluctuating numbers of poor families. With many government programs now
targeted only at families that are officially classified as poor, the correct
identification of these families has assumed new importance.
Price series to adjust for cost-of-living differences are at the core of
any comparisons of real income or welfare across individuals and over
time, and so also for poverty calculations. Several studies have suggested
alternative methods of arriving at reasonable price series that could be
used to generate consistent poverty estimates (Diewert,1978;Hill,2004;
Neary,2004;Deaton,2010;Deaton and Dupriez,2011). In this paper,
we propose and justify an alternative approach to estimating poverty that
circumvents direct micro price measurement and aggregation. Since Ernst
Engel’s work (Engel,1857,1895), the empirical regularity of a negative
relationship between the budget share for food and real income has been
well established. We identify regional differences in the cost of living in
India by estimating Engel curves for food. We assume that households
with the same demographic and occupational characteristics spend the same
proportion of their income on food. We then use data from the National
Sample Surveys (NSS) and attribute systematic differences in nominal
expenditures of households with the same food share to different relative
price levels across states. We do this separately for the rural and urban
samples of the NSS, and then we use our price estimates to derive rural
and urban poverty lines and head counts for each Indian state in 2004–2005
and 2009–2010.
Our paper has two main objectives: first, to obtain a set of price and
poverty estimates using the Engel approach; second, to examine whether
the Engel method we use does a better job of identifying the poor than
the current official methodology. If it does, then a comparison of official
estimates with our estimates is meaningful and can reveal biases in official
accounts of poverty patterns and trends in India. Our main strategy for
checking the validity of our estimates is to compare the consumption
behavior of households within a narrow band of our poverty lines with those
in a similar band around official lines. There is evidence that the poor often
obtain their calories from relatively cheap sources, while those who are less
poor substitute towards more expensive calories with favorable attributes
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The editors of The Scandinavian Journal of Economics 2017.
184 Estimation of poverty in India
such as taste or status (Jensen and Miller,2010). We find that households
clustered around our estimated lines obtain large and similar shares of their
total calories from cheap sources, such as cereals, and small shares from
expensive sources, such as fats and oils. In contrast, spending continues
to be related to the nominal expenditures for households around official
poverty lines, suggesting that these lines do not properly account for cost-
of-living differences. We also find higher correlations between rural and
urban prices than official price estimates and higher rates of self-reported
hunger in the states that we classify as the poorest.
The Engel approach estimates only relative price levels. To generate
poverty lines that can be compared with the official lines, we normalize
our estimates so as to generate the official aggregate poverty line for
2004–2005. Therefore, differences between our results and official estimates
appear in patterns of spatial poverty in the two years, 2004–2005 and 2009–
2010, and in changes in poverty over the five-year period. We present three
main findings. First, there is a higher dispersion in poverty across Indian
states in both years. Secondly, the rural poverty rates in the eastern states of
Assam, Bihar, Odisha, and West Bengal are consistently higher than those
implied by official figures. Finally, the decrease in overall poverty over our
five-year period is more modest than suggested by official statistics.
Our paper is related both to the literature on poverty measurement in
India and to studies that use estimated Engel curves to correct for biases
in the measurement of prices over time. Hamilton (2001) pioneered this
strand of research through his study on consumer price indices in the
United States, and it has since been applied to several countries and time
periods.1More recently, it has also been used to estimate biases in spatial
price variations (Alm˚as,2012).2Studies of growth require the identification
of prices over time while studies of inequality are based on spatial price
variation. In order to study poverty, however, the identification of both
spatial and temporal indices are necessary, and we provide a framework to
do so.
The deficiencies in official approaches to price and poverty measurement
in India have been extensively discussed in a series of papers by Angus
Deaton and co-authors, who have provided alternative poverty estimates
based on unit values from the NSS (Deaton and Tarozzi,2005;Deaton,
2008,2010). These studies were influential in bringing about changes in the
1For example, Alm˚as and Johnsen (2013), Bar rett and Brzozowski (2010), Beatty and Larsen
(2005), Carvalho Filho and Chamon (2006), Chung et al.(2010), Costa (2001), Gibson et al.
(2008), Larsen (2007), Nakamura et al.(2016), and Olivia and Gibson (2012) have all applied
this method in different contexts.
2The Engel methodology has been discussed in several papers (e.g., Deaton and Dupriez 2011;
Beatty and Crossley 2012), and validations have been called for (e.g., Ravallion2015).
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The editors of The Scandinavian Journal of Economics 2017.

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