Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Language: English
    Pages: 1 Online-Ressource (56 pages)
    Parallel Title: Erscheint auch als Brunckhorst, Ben Tracing Pandemic Impacts in the Absence of Regular Survey Data: What have we Learned from the World Bank's High-Frequency Phone Surveys?
    Keywords: Covid-19 Impacts ; Gender ; Gender and Public Expenditures ; Health, Nutrition and Population ; High-Frequency Phone Survey ; Household Questionnaire Design ; Household Welfare ; Inequality ; Labor Markets ; Macroeconomics and Economic Growth ; Social Protections and Labor ; Survey Method
    Abstract: The World Bank's High-Frequency Phone Surveys were deployed to support the monitoring of household welfare during the COVID-19 pandemic, when most of the regular household survey data collection was suspended. This paper reviews the analytical insights gained from the High-Frequency Phone Survey data, including uneven dynamics of household welfare during the pandemic across and within countries, as well as novel applications to simulate estimates of poverty and intergenerational mobility following the pandemic. The paper further derives lessons from the data collection experience. First, phone surveys, while inexpensive and quick, require reliable sampling frames. The predominant sampling strategies-previous household survey and random digit dialing-each have pros and cons in terms of representativeness, non-response, and post-survey adjustments. Second, on questionnaire design, country customization needs to be carefully balanced against standardization when cross-country comparisons are likely to be important. Finally, baseline metrics are critical for crisis monitoring; this requires more frequent welfare monitoring and better alignment of questions in phone surveys and existing data sources. While phone surveys can be a reliable toolkit for researchers and governments, more research is needed on key questions related to the survey mode effect, and the implications of different sampling frames and questionnaire design
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 2
    Language: English
    Pages: 1 Online-Ressource (63 pages)
    Parallel Title: Erscheint auch als Brunckhorst, Ben Long COVID: The Evolution of Household Welfare in Developing Countries during the Pandemic
    Keywords: COVID and Informal Workers ; COVID-19 Impacts ; Gender ; Gender and Poverty ; Gendered COVID Impact ; Inequality ; Labor Market Impacts ; Phone Survey Data ; Poverty Reduction ; Welfare
    Abstract: This paper examines the welfare impacts of the COVID-19 pandemic, using harmonized data from 343 high-frequency phone surveys conducted in 80 economies during 2020 and 2021, representing more than 2.5 billion people. The analysis focuses on the scarring effects of the initial losses of employment and income by examining their evolution over time across and within countries, as restrictions on mobility and economic activity were introduced and then gradually relaxed. The employment and welfare outcomes of some groups that were impacted to a greater degree initially-including women, informal workers, and those with less education-have been improving at a slower pace. The social protection response in lower-income economies was largely insufficient to protect households from the pandemic shock. Unmitigated welfare losses, as seen for example from the large share of households indicating income losses well into 2021, are highly correlated with food insecurity, which likely led some households to sell physical assets and deplete their savings. Without proper remediation, the uneven welfare impacts associated with COVID-19 may be amplified over the medium to long term, leading to future increases in poverty and inequality
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...