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Social Indicators Research
Published in: Social Indicators Research 1/2021

16-02-2021 | Original Research

Vulnerability to Food Insecurity: A Decomposition Exercise for Rural India using the Expected Utility Approach

Author: Mousumi Das

Published in: Social Indicators Research | Issue 1/2021

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Abstract

Devising multi-pronged strategies for those highly prone to food insecurity in the future remains a challenge for food policymakers. This set of food-insecure households faces different kinds of shocks (economic, political, environmental, pandemic, personal, etc.), which render them vulnerable to food insecurity. This study identifies those who are vulnerable to food insecurity, and will be useful for policymakers to resolve food insecurity related challenges in crisis or no crisis periods on an ex-ante basis. This paper decomposes the total welfare loss resulting from vulnerability to food insecurity into components due to ‘food poverty’ (expected consumption of the current food secure falling below the food poverty line), and risk (variability of food consumption over time). The last term can be further decomposed into the aggregate (region or community-specific), and idiosyncratic (household or individual-specific) risks. The paper identifies the key determinants of the different components of vulnerability to food insecurity based on household consumption expenditure surveys conducted in rural India in 2004–05, 2009–10, and 2011–12. Expected utility-based measures, and pseudo-panel regression techniques were used to identify risk-prone households. The key findings are: idiosyncratic risk is the largest driver (a loss in the utility of almost 51%) as compared to the poverty and covariate shocks; states with poor Public Distribution System (PDS) performance are more susceptible, a rise in the price of staple food items increases the chances of food insecurity, gendered disparity persists over time, forward social and religious groups are more vulnerable, and finally a higher level of urbanization increases the exposure and sensitivity to shocks (like the spread of contagious diseases), disrupts the supply of food commodities from rural markets, loss in income, and increase in rural vulnerability to food insecurity. Our key policy suggestion is a mix of cash versus in-kind transfers to resolve rural food insecurity related issues, tend towards resiliency, and simultaneously tackle the triple burden of malnutrition and SDG-2 related goals.
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Footnotes
1
One prime reason for the same is that it is expensive to collect panel data, and developing countries generally lack such detailed information. Thus, poverty lines are static measures usually able to identify those who are ‘chronic poor’ and not the ‘transitory poor’ people.
 
2
It may be that a household vulnerable to food insecurity this period, not availing any benefits from food security programs, has an option to resort to Employment Guarantee Schemes (EGS). We can assume that income earned from the EGS will be spent on buying food items. However, there are other factors like whether that income earned is enough to buy food during periods of crises, members of the household eligible to avail benefit from the EGS are in a physical condition to do so, there are no sudden health shocks in the family, etc. Therefore, whether the household has access to any other beneficiary program other than food security programs does not dilute the need to address the issues of the vulnerable. In this context the debate between cash versus in-kind transfers is important. An in-kind transfer may be more useful than cash transfer in crises periods or for poor households as it is inflation proof, used more wisely, cannot be cornered by a few and has a wider reach in remote locations (Drèze, 2011).
 
3
The general assumption is that the poor are food insecure. However, those above the poverty line may or may not be food secure. These households were not eligible for any benefits whether in cash or kind (eligible for food grains from PDS, wages from NREGS, etc.) in the earlier period along with the poor. However, in this period these households are the new poor as defined in the literature due to the sudden shocks. This incurs an additional expenditure on the government as policy makers now need to make provision for these newly formed poor.
 
4
The set of vulnerable are identified as a subset of the ultra-poor for availing the benefits of this programme.
 
5
Methods 1 (Vulnerability as expected poverty: Pritchett et al. 2000; Chaudhuri et al. 2002; Scaramozzino 2006; Sileshi et al. 2019a, 2019b; Tarasuk et al. 2019) and 2 (Vulnerability as uninsured exposure to risk: Hoddinott, and Quisumbing, 2003) are not considered as results can be improved by using a panel, and not a cross-section data. Methods 3 (Decision making under uncertainty: Dutta et al. (2011)) and 4 (Vulnerability measures for both individual and aggregate poverty: Calvo and Dercon 2013), which provide more robust results cannot be adopted due to data limitations. This is because no prior information is available on the probability of the occurrence of a certain event say crop loss or drought.
 
6
Aggregation of household preferences has minimal impact on the measures of vulnerability as discussed in Calvo & Dercon (2013). Aggregation of income at the household level to that at the state-region level will draw similar conclusions.
 
7
Poverty risk can also be decomposed into expected incidence, intensity, and expected variability as in Celidoni (2015), which may be explored further in the context of food security.
 
8
The following conversions are used: 1 L milk = 1 kg; 1 egg = 58 gms; 10 bananas = 1 kg; 1 orange = 150 gms; 1 pineapple = 1.5 kgs; 1 coconut = 1 kg.
 
9
Source: http://​www.​ifpindia.​org/​Built-Up-Areas-in-India-e-GEOPOLIS.​html (Accessed at different time periods since 2013).
 
10
Regions are hierarchical domains of study below the level of State/ Union Territory in the NSS. Source: http://​mospi.​nic.​in/​sites/​default/​files/​publication_​reports/​concepts_​golden.​pdf (Accessed on Jan 8 2019).
 
11
The NSS data is used by the government for all policy and planning purposes and is available for a longer time horizon unlike other datasets like the India Human Development Survey (IHDS) dataset. NSS data is not quite representative at the district-level. Hence, an assessment of vulnerability at the district-level will not be of much relevance.
 
12
Some of the limitations of this analysis well documented in the literature are: (i) NSS or any other large scale household survey collects information on consumption expenditure at the household level only. However the same dataset is used for all government policy analysis and purposes. In case of conversion of food items into different nutrients it is possible to weigh the intake by consumption units which is based on gender differences. However we control for gender, age, etc. in the regression model; (ii) Also, there is no dataset available which collects information on cold-storage or shipping facilities at the district level which can help to separate the Alchian-Allen effects (Gibson, 2016). For understanding the interplay of the Alchian-Allen effects among different food items one needs to conduct a state-region level analysis, which is beyond the scope of this paper. Also, from our understanding and detailed analysis of the NSS data it is obvious that substitutability among the raw food items which NSS provides is limited in nature particularly for rural areas. Regions primarily consume food items based on local production, tastes, and availability. We are also aware of the other biases attributed to available data, which may lead to over/under estimation used as discussed in Aleksandrowicz et al. (2017).
 
13
Information on PDS is obtained from the NSS consumption expenditure survey for different rounds as discussed earlier in the data section.
 
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Metadata
Title
Vulnerability to Food Insecurity: A Decomposition Exercise for Rural India using the Expected Utility Approach
Author
Mousumi Das
Publication date
16-02-2021
Publisher
Springer Netherlands
Published in
Social Indicators Research / Issue 1/2021
Print ISSN: 0303-8300
Electronic ISSN: 1573-0921
DOI
https://doi.org/10.1007/s11205-021-02625-7

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