COVID-19 Related Shocks Survey (CRSS) in Rural India 2020
Other Household Survey [hh/oth]
This is the COVID-19 Related Shocks Survey 2020, covering rounds 1 -3.
An effective policy response to the economic impacts of the COVID-19 pandemic requires an enormous range of data to inform the design and response of programs. Public health measures require data on the spread of the disease, beliefs in the population, and capacity of the health system. Relief efforts depend on an understanding of hardships being faced by various segments of the population. Food policy requires measurement of agricultural production and hunger. In such a rapidly evolving pandemic, these data must be collected at a high frequency. Given the unexpected nature of the shock and urgency with which a response was required, Indian policymakers needed to formulate policies affecting India's 1.4 billion people, without the detailed evidence required to construct effective programs. To help overcome this evidence gap, researchers from the World Bank, in collaboration with IDinsight, the Development Data Lab, and John Hopkins University sought to produce rigorous and responsive data for policymakers across six states in India: Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
The COVID-19-Related Shocks Surveys in Rural India cover the following subjects; agriculture, income and consumption, migration, access to relief and health.
Agriculture & Rural Development
Fragile & Conflict-affected States
Migration & Remittances
Access to Finance
Households located in Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh
Producers and sponsors
The World Bank
The World Bank
Funded the survey
This dataset includes observations covering six states (Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajasthan, Uttar Pradesh) and three survey rounds. The survey did not have a single, unified frame from which to sample phone numbers. The final sample was assembled from several different sample frames, and the choice of frame sample frames varied across states and survey rounds.
These frames comprise four prior IDinsight projects and from an impact evaluation of the National Rural Livelihoods project conducted by the Ministry of Rural Development. Each of these surveys sought to represent distinct populations, and employed idiosyncratic sample designs and weighting schemes.
A detailed note covering key features of each sample frame is available for download.
Deviations from the Sample Design
Details will be made available after all rounds of data collection and analysis is complete.
Round 1: ~55%
Round 2: ~46%
Round 3: ~55%
In order to create comparable state-level estimates from the successfully interviewed households - as well as to create correctly pooled estimates across the six states- weights were applied to the information provided by the sampled households.
The weights were calculated in several steps. Due to the variation in sampling frames and sampling procedures across states and across rounds, the precise weight procedures tend to be idiosyncratic to a given state/frame/round combination.
A detailed note on the weighting methodology adopted with a generalized set of steps and significant state/frame deviations from the process is available for download.
Dates of Data Collection
Data Collection Mode
Computer Assisted Telephone Interview [cati]
Data Collection Notes
Data was collected by IDinsight’s data on demand team using CATI.
The survey questionnaires covered the following subjects:
1. Agriculture: COVID-19-related changes in price realisation, acreage decisions, input expenditure, access to credit, access to fertilisers, etc.
2. Income and consumption: Changes in wage rates, employment duration, consumption expenditure, prices of essential commodities, status of food security etc.
3. Migration: Rates of in-migration, migrant income and employment status, return migration plans etc.
4. Access to relief: Access to in-kind, cash and workfare relief, quantities of relief received, and constraints on the access to relief.
5. Health: Access to health facilities and rates of foregone healthcare, knowledge of COVID-19 related symptoms and protective behaviours.
While a number of indicators were consistent across all three rounds, questions were added and removed as and when necessary to account for seasonal changes (i.e: in the agricultural cycle).
The India COVID-19 surveys were conducted using Computer Assisted Telephone Interview (CATI) techniques. The household questionnaire was implemented using the CATI software, SurveyCTO. The software was deployed through surveyors’ smartphones, who called respondents via mobile, and recorded their responses over the phone. If unreached, surveyors would attempt to call back respondents up to 7 times, often seeking explicit appointments for suitable times to avoid non-responses.
Validation and consistency checks were incorporated into the SurveyCTO software to avoid human error. Extreme values and outliers were scrutinised through a real time dashboard set up by IDinsight. Surveys were also audio audited by monitors to check for consistency and accuracy of question phrasing and answer recording. Finally, supervisors also randomly back-checked a subset of interviews to further ensure data accuracy.
IDinsight cleaned and labelled the data for further processing and analysis. The Development Data Lab examined the data for discrepancies and errors and merged the dataset with their proprietary spatial data.
All personally identifiable information has been removed from the datasets.
Use of the dataset must be acknowledged using a citation which would include:
- the Identification of the Primary Investigator
- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
- the source and date of download
Disclaimer and copyrights
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses