COVID 19, Consumption and Inequality: A Systematic Analysis of Rural Population of India
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Background
COVID19 pandemic has had major impact on consumption levels and inequality within India. Government policy interventions have targeted poor households for cash and food transfers. It is important, however, to study the impact of the pandemic on consumption levels of non-poor in India, and in particular the middle class. In this paper, we aim to quantify the changes in consumption levels and inequality over time, across all groups of rural households in India.
Methods
We analyze three rounds of COVID 19-related shock surveys between May and September 2020. These surveys cover rural households of six large states in India and are representative of more than 442 million (52% of India’s rural population).
Findings
In the early phase of the pandemic, it was the bottom 40% of households that experienced the most severe decline in consumption. But as the pandemic deepened, consumption declined across all classes of households. Besides the poorest, it was particularly severe for the middle class (defined as 40%-80%). We also measure consumption inequality over time and find that the Gini coefficient of consumption distribution increased significantly.
Interpretation
In addition to focusing on poor households, policy responses to alleviate people’s sufferings would have to consider a more comprehensive boost to consumption and compensate for the reduced consumption among middle class families as well.
Funding
None.
Article activity feed
-
SciScore for 10.1101/2021.06.08.21258525: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter:…
SciScore for 10.1101/2021.06.08.21258525: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
-
