2 datasets found
  1. COVID-19-Related Shocks in Rural India 2020, Rounds 1-3 - India

    • microdata.worldbank.org
    • datacatalog.ihsn.org
    • +1more
    Updated Jan 15, 2021
    + more versions
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    World Bank (2021). COVID-19-Related Shocks in Rural India 2020, Rounds 1-3 - India [Dataset]. https://microdata.worldbank.org/index.php/catalog/3830
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    Dataset updated
    Jan 15, 2021
    Dataset authored and provided by
    World Bankhttp://worldbank.org/
    Time period covered
    2020
    Area covered
    India
    Description

    Abstract

    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, the World Bank, IDinsight, and the Development Data Lab sought to produce rigorous and responsive data for policymakers across six states in India: Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh.

    Geographic coverage

    Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajasthan, and Uttar Pradesh

    Analysis unit

    Household

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    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.

    Mode of data collection

    Computer Assisted Telephone Interview [cati]

    Research instrument

    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).

    Response rate

    Round 1: ~55% Round 2: ~46% Round 3: ~55%

  2. f

    COVID-19 Related Shocks Survey (CRSS) in Rural India 2020 - India

    • microdata.fao.org
    Updated Nov 8, 2022
    + more versions
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    The World Bank (2022). COVID-19 Related Shocks Survey (CRSS) in Rural India 2020 - India [Dataset]. https://microdata.fao.org/index.php/catalog/1768
    Explore at:
    Dataset updated
    Nov 8, 2022
    Dataset authored and provided by
    The World Bank
    Time period covered
    2020
    Area covered
    India
    Description

    Abstract

    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.

    Geographic coverage

    Regional coverage

    Analysis unit

    Households

    Universe

    Households located in Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    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.

    Sampling deviation

    Details will be made available after all rounds of data collection and analysis is complete.

    Mode of data collection

    Computer Assisted Telephone Interview [cati]

    Research instrument

    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).

    Cleaning operations

    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.

    Response rate

    Round 1: ~55% Round 2: ~46% Round 3: ~55%

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Share
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TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
World Bank (2021). COVID-19-Related Shocks in Rural India 2020, Rounds 1-3 - India [Dataset]. https://microdata.worldbank.org/index.php/catalog/3830
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COVID-19-Related Shocks in Rural India 2020, Rounds 1-3 - India

Explore at:
16 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Jan 15, 2021
Dataset authored and provided by
World Bankhttp://worldbank.org/
Time period covered
2020
Area covered
India
Description

Abstract

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, the World Bank, IDinsight, and the Development Data Lab sought to produce rigorous and responsive data for policymakers across six states in India: Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh.

Geographic coverage

Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajasthan, and Uttar Pradesh

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

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.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

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).

Response rate

Round 1: ~55% Round 2: ~46% Round 3: ~55%

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