The data set provides an overview of the economic effects of the COVID-19 epidemic in Vietnam. The data on total per capita income for the first half of 2020 are compared with the same period in the previous years. In parallel, Vietnam's export turnover has also decreased.

The indicators and analysis presented in this bulletin are based on responses from the new voluntary fortnightly business survey, which captures businesses responses on how their turnover, workforce prices, trade and business resilience have been affected in the two week reference period. These data relate to the period 6 April 2020 to 19 April 2020.

This paper uses a panel data approach to assess the evolution of economic consequences of the drastic lockdown policy in the epicenter of COVID-19-the Hubei Province of China during worldwide curbs on economic activity. We find that the drastic 76-day COVID-19 lockdown policy brought huge negative impacts on Hubei's economy. In 2020:q1, the lockdown quarter, the treatment effect on GDP was about 37% of the counterfactual. However, the drastic lockdown also brought the spread of COVID-19 under control in little more than two months. After the government lifted the lockdown in early April, the economy quickly recovered with the exception of passenger transportation sector which rebounded not as quickly as the rest of the general economy.

Abstract

The World Bank and UNHCR in collaboration with the Kenya National Bureau of Statistics and the University of California, Berkeley are conducting the Kenya COVID-19 Rapid Response Phone Survey to track the socioeconomic impacts of the COVID-19 pandemic, the recovery from it as well as other shocks to provide timely data to inform a targeted response. This dataset contains information from eight waves of the COVID-19 RRPS, which is part of a panel survey that targets refugee household and started in May 2020. The same households were interviewed every two months for five survey rounds, in the first year of data collection, and every four months thereafter, with interviews conducted using Computer Assisted Telephone Interviewing (CATI) techniques. The sample aims to be representative of the refugee and stateless population in Kenya. It comprises five strata: Kakuma refugee camp, Kalobeyei settlement, Dadaab refugee camp, urban refugees, and Shona stateless. Waves 1-7 of this survey include information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge. Wave 8 focused on how households were exposed to shocks, in particular adverse weather shocks and the increase in the price of food and fuel, but also included parts of the previous modules on household background, service access, employment, food security, income loss, and subjective wellbeing. The data is uploaded in three files. The first is the hh file, which contains household level information. The 'hhid', uniquely identifies all household. The second is the adult level file, which contains data at the level of adult household members. Each adult in a household is uniquely identified by the 'adult_id'. The third file is the child level file, available only for waves 3-7, which contains information for every child in the household. Each child in a household is uniquely identified by the 'child_id'. The duration of data collection and sample size for each completed wave was: Wave 1: May 14 to July 7, 2020; 1,328 refugee households Wave 2: July 16 to September 18, 2020; 1,699 refugee households Wave 3: September 28 to December 2, 2020; 1,487 refugee households Wave 4: January 15 to March 25, 2021; 1,376 refugee households Wave 5: March 29 to June 13, 2021; 1,562 refugee households Wave 6: July 14 to November 3, 2021; 1,407 refugee households Wave 7: November 15, 2021, to March 31, 2022; 1,281 refugee households Wave 8: May 31 to July 8, 2022: 1,355 refugee households The same questionnaire is also administered to nationals in Kenya, with the data available in the WB microdata library: https://microdata.worldbank.org/index.php/catalog/3774

Geographic coverage

National coverage covering rural and urban areas

Analysis unit

Individual and Household

Universe

All persons of concern for UNHCR

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample aims to be representative of the refugee and stateless population in Kenya. It comprises five strata: Kakuma refugee camp, Kalobeyei settlement, Dadaab refugee camp, urban refugees, and Shona stateless, where sampling approaches differ across strata. For refugees in Kakuma and Kalobeyei, as well as for stateless people, recently conducted Socioeconomic Surveys (SES), were used as sampling frames. For the refugee population living in urban areas and the Dadaab camp, no such household survey data existed, and sampling frames were based on UNHCR's registration records (proGres), which include phone numbers. For Kakuma, Kalobeyei, Dadaab and urban refugees, a two-step sampling process was used. First, 1,000 individuals from each stratum were selected from the corresponding sampling frames. Each of these individuals received a text message to confirm that the registered phone was still active. In the second stage, implicitly stratifying by sex and age, the verified phone number lists were used to select the sample. Until wave 7 sampled households that were not reached in earlier waves were also contacted along with households that were interviewed before. In wave 8 only households that had previously participated in the survey were contacted for interview. The “wave” variable represents in which wave the households were interviewed in. For the stateless population, all the participants of the Shona socioeconomic survey (n=400) were included in the RRPS, because of limited sample size. The sampling frames for the refugee and Shona stateless communities are thus representative of households with active phone numbers registered with UNHCR.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire included 12 sections Section 1: Introduction Section 2: Household background Section 3: Travel patterns and interactions Section 4: Employment Section 5: Food security Section 6: Income Loss Section 7: Transfers Section 8: Subjective welfare (50% of sample) Section 9: Health Section 10: COVID Knowledge Section 11: Household and Social Relations (50% of sample) Section 12: Conclusion

Cleaning operations

Variable names were kept constant across survey waves. For questions that remained exactly the same across survey waves, data points for all waves can be found under one variable name. For questions where the phrasing changed (even in a minimal way) across waves, variable names were also changed to reflect the change in phrasing. Extended missing values are used to indicate why a value is missing for all variables. The following extended missing values are used in the dataset: · .a for 'Don't know' · .b for 'Refused to respond' · .c for 'Outliers set to missing' · .d for 'Inconsistency set to missing' (used for employment data as explained below) · .e for 'Field Skipped' (where an error in the survey tool caused the question to be missed) · .z for 'Not administered' (as the variable was not relevant to the observation) More detailed data on children was collected between waves 3 and 7, compared to waves 1, 2 and 8. In waves 1 and 2, data on children, e.g. on their learning activities, was collected for all children in a household with one question. Therefore, variables related to children are part of the 'hh' data for waves 1 and 2. Between waves 3 and 7, questions on children in the household were asked for specific children. Some questions covered all children, while others were only administered to one randomly selected child in the household. This approach allows to disaggregate data at the level of the child household members, and the data can be found in the 'child' data set. The household level weights can be used for analysis of the children's data. In wave 8, detailed information on children was dropped, as the questionnaire focused on other topics. The education status of household members, except for the respondent, was imputed for rounds 1 and 2. For rounds 1 and 2, only the education status of the respondent was elicited, while for later rounds the education status for each household member was asked. In order to evaluate outcomes by the household member's education status, information on education was imputed for waves 1 and 2, using the information provided for all household members in waves 3, 4, and 5. This resulted in additional information on the education status for household members in round 1 and 2, which was not yet available for earlier versions of this data. Some questions are not asked repeatedly across waves such that their values were imputed. For some questions, answers are not possible or unlikely to change within two months between survey waves such that households were not asked about them in all waves. The questions on assets owned before March 2020 were only asked to households when they are interviewed for the first time. The questions on the dwelling's wall and floor material as well as the household's connection to the power grid was not asked for all households in wave 2 and 3, where only new households and those who moved were covered by these questions. Questions on the main source of electricity in the households and types of assets owned were not asked in wave 8. The missing values those variables have when they were not asked, are imputed from the answers given in earlier waves. Improved quality insurance algorithms lead to minor revisions to wave 1 to 5 data. Based on additional data checks, the team has made minor refinements to wave 1 to 5 data. The identification of the household members that were the respondent or the household head was refined in the rare cases where it was not possible to interview the same respondent as in previous waves for a given household such that another adult was interviewed. For this reason, for about 2 percent of observations the household head status was assigned to an incorrect household member, which was corrected. For <1 percent of households the respondent did not appear in adult level dataset. For about 1 percent of observations in wave 5 the respondent appeared twice in the adult level dataset. Data from questions on COVID-19 vaccinations from wave 7 was dropped from the dataset. Due to significantly higher self-reported vaccination rates compared to official administrative records, data on vaccinations was deemed unreliable, most likely due to social desirability bias. Consequently, questions on vaccination status and questions using the vaccination data as a validation criterion were dropped from the datasets.

Context

The objective of the dataset is to provide information that enables decision makers to better direct their efforts in addressing the wider effects of the COVID-19 pandemic. The dataset will track secondary impacts across a wide range of relevant themes: economy, health, migration, education to name a few.

https://data.humdata.org/dataset/global-covid-19-secondary-impacts

Content

A set of impact indicators anticipated to be impacted by COVID-19 have been identified and organised across pillars and thematic blocks. Additionally, a set of pre-COVID-19 baseline indicators have been selected for each pillar.

The data collection is conducted on a country-level and identifies the secondary impacts the COVID- 19 pandemic. Data comes from a range of available sources, including international organisations, research centres, and media analysis.

Note: These are the preliminary results of the data collection on secondary impacts. This dataset is currently in the beta-testing phase.

Acknowledgements

https://data.humdata.org/dataset/global-covid-19-secondary-impacts

Photo by Mick Haupt on Unsplash

Inspiration

Covid-19 Pandemic.

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

The data hub publishes a broad set of measures across pertinent topics such as public health, government spending, personal finances, and employment to assess the longer-term economic impact of the pandemic and the efficacy of recovery efforts. It includes indicators on health (COVID-19 cases, deaths), economy (unemployment claims, retail sales, air travel passengers, etc), standard of living (household spending, personal income, food scarcity, housing insecurity, etc), and government (federal government spending, federal reserve assets, state tax revenue, federal deficit).

Note: Blueprint has been retired as of June 15, 2021. This dataset will be kept up for historical purposes, but will no longer be updated.

California has a new blueprint for reducing COVID-19 in the state with revised criteria for loosening and tightening restrictions on activities. Every county in California is assigned to a tier based on its test positivity and adjusted case rate for tier assignment. Additionally, a new health equity metric took effect on October 6, 2020. In order to advance to the next less restrictive tier, each county will need to meet an equity metric or demonstrate targeted investments to eliminate disparities in levels of COVID-19 transmission, depending on its size. The California Health Equity Metric is designed to help guide counties in their continuing efforts to reduce COVID-19 cases in all communities and requires more intensive efforts to prevent and mitigate the spread of COVID-19 among Californians who have been disproportionately impacted by this pandemic.

Please see https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/COVID19CountyMonitoringOverview.aspx for more information.

Also, in lieu of a Data Dictionary, please refer to the detailed explanation of the data columns in Appendix 1 of the above webpage.

Because this data is in machine-readable format, the merged headers at the top of the source spreadsheet have not been included:

• The first 8 columns are under the header "County Status as of Tier Assignment"

• The next 3 columns are under the header "Current Data Week Tier and Metric Tiers for Data Week"

• The next 4 columns are under the header "Case Rate Adjustment Factors"

• The next column is under the header "Small County Considerations"

• The last 5 columns are under the header "Health Equity Framework Parameters"

This data package includes the underlying data files to replicate the data, tables, and charts presented in How did Korea’s fiscal accounts fare during the COVID-19 pandemic? PIIE Policy Brief 23-8.

If you use the data, please cite as: Gagnon, Joseph E., and Asher Rose. 2023. How did Korea’s fiscal accounts fare during the COVID-19 pandemic? PIIE Policy Brief 23-8. Washington, DC: Peterson Institute for International Economics.

The objective of the dataset is to provide information that enables decision makers to better direct their efforts in addressing the wider effects of the COVID-19 pandemic. The dataset will track secondary impacts across a wide range of relevant themes: economy, health, migration, education to name a few.

A set of around 80 impact indicators anticipated to be impacted by COVID-19 have been identified and organised across 4 pillars and 13 thematic blocks. Additionally, a set of around 25 pre-COVID-19 baseline indicators have been selected for each pillar.

The data collection is conducted on a country-level and identifies the secondary impacts the COVID- 19 pandemic is having in more than 190 countries. Data comes from a range of available sources, including international organisations, research centres, and media analysis.

Note: These are the preliminary results of the data collection on secondary impacts. This dataset is currently in the beta-testing phase, we will keep improving and updating in the coming weeks.

This data package includes the underlying data to replicate the charts presented in Lessons from China's fiscal policy during the COVID-19 pandemic, PIIE Working Paper 24-7.

If you use the data, please cite as: Huang, Tianlei. 2024. Lessons from China's fiscal policy during the COVID-19 pandemic. PIIE Working Paper 24-7. Washington: Peterson Institute for International Economics.

UPDATE: As of June 1, 2020

List of developing economies included ,

Bangladesh Bhutan Brunei Darussalam Cambodia Fiji Hong Kong, China India Indonesia Kazakhstan Kyrgyz Republic Lao PDR Malaysia Maldives Mongolia Nepal Pakistan Philippines Republic of Korea Singapore Sri Lanka Taipei,China Thailand Viet Nam

Abiad, Abdul, Mia Arao, Editha Lavina, Reizle Platitas, Jesson Pagaduan, and Christian Jabagat, 2020. “The Impact of COVID-19 on Developing Asian Economies: The Role of Outbreak Severity, Containment Stringency, and Mobility Declines,” in COVID in Emerging and Developing Countries, Simeon Djankov and Ugo Panizza (eds.). London: Centre for Economic Policy Research.

BackgroundThe spread of COVID-19 has been characterized by unprecedented global lock-downs. Although, the extent of containment policies cannot be explained only through epidemic data. Previous studies already focused on the relationship between the economy and healthcare, focusing on the impact of diseases in countries with a precarious economic situation. However, the pandemic caused by SARS-CoV-2 drew most countries of the world into a precarious economic situation mostly caused by the global and local lock-downs policies.MethodsA discriminant analysis performed via partial least squares procedure was applied to evaluate the impact of economic and healthcare variables on the containment measures adopted by 39 countries. To collect the input variables (macroeconomic, healthcare, and medical services), we relied on official databases of international organizations, such as The World Bank and WHO.ResultsThe stringency lock-down policies could not only be influenced by the epidemical data, but also by previous features of the selected countries, such as economic and healthcare conditions.ConclusionsIndeed, economic and healthcare variables also contributed to shaping the implemented lock-down policies.

Ireland DE: HU: Change in Personal Income Tax Revenue data was reported at -1,416.000 EUR mn in Dec 2021. This records an increase from the previous number of -1,712.000 EUR mn for Sep 2021. Ireland DE: HU: Change in Personal Income Tax Revenue data is updated quarterly, averaging -1,594.000 EUR mn from Mar 2021 (Median) to Dec 2021, with 4 observations. The data reached an all-time high of -1,416.000 EUR mn in Dec 2021 and a record low of -1,716.000 EUR mn in Mar 2021. Ireland DE: HU: Change in Personal Income Tax Revenue data remains active status in CEIC and is reported by Economic and Social Research Institute. The data is categorized under Global Database’s Ireland – Table IE.F013: Potential Costs and Distributional Effect: COVID-19 Related Unemployment. [COVID-19-IMPACT]

Dataset

This dataset was created by Sofia Rebrey

Released under Data files © Original Authors

Contents

This data package includes the underlying data to replicate the charts, tables, and calculations presented in Labor market tightness and inflation before and after the COVID-19 pandemic, PIIE Working Paper 24-23.

If you use the data, please cite as:

Bloesch, Justin. 2024. Labor market tightness and inflation before and after the COVID-19 pandemic. PIIE Working Paper 24-23. Washington: Peterson Institute for International Economics.

This paper summarises the latest evidence and analysis on the impacts of COVID-19 on London’s economy so far and on the economic outlook so that key actors and stakeholders engaged in responding to the pandemic can have a readily available evidence base to inform policy responses.

The American reaction to the COVID-19 pandemic is polarized, with conservatives often less willing to engage in risk-mitigation strategies such as mask-wearing and vaccination. COVID-19 narratives are also polarized, as some conservative elites focus on the economy over public health. In this registered report, we test whether combining economic and public health messages can persuade individuals to increase support for COVID-19 risk mitigation. We present preliminary evidence that the combination of messages is complementary, rather than competing or polarizing. When given a message emphasizing COVID-19’s negative health and economic effects in a pilot study, conservatives increased their support for a broad range of risk-mitigation strategies, while liberals maintained high levels of support. A preregistered larger-n follow-up study, however, failed to replicate this effect. While complementary frames may be a promising way to persuade voters on some issues, they may also struggle to overcome high levels of existing polarization.

Replication Code and Data for: "How Has COVID-19 Impacted Research Production in Economics and Finance?" Review of Financial Studies, Forthcoming.

505 Economics is on a mission to make academic economics accessible. We've developed the first monthly sub-national GDP data for EU and UK regions from January 2015 onwards.

Our GDP dataset uses luminosity as a proxy for GDP. The brighter a place, the more economic activity that place tends to have.

We produce the data using high-resolution night time satellite imagery and Artificial Intelligence.

This builds on our academic research at the London School of Economics, and we're producing the dataset in collaboration with the European Space Agency BIC UK.

We have published peer-reviewed academic articles on the usage of luminosity as an accurate proxy for GDP.

Key features:

• Granular: Data is provided at the following geographical units:
  • NUTS3 (e.g. German Districts/Kreise),
  • NUTS2 (e.g. Regions/Regierungsbezirke),
  • NUTS1 (e.g. States/Länder), and
  • NUTS0 (e.g. Germany) levels.
• Frequent: Data is provided every month from January 2015. This is more frequent than the annualised official datasets.
• Timely: Data is provided with a one month lag (i.e. the data for January 2021 was published at the end of February 2021). This is substantially quicker than the 18 month lag of official datasets.
• Accurate: Our dataset uses Deep Learning to maximise accuracy (RMSE 1.2%).

The dataset can be used by:

• Governments and policy makers - to monitor the performance of local economies, to measure the localised impact of policies, and to get a real-time indication of economic activity.
• Financial services - to get an indication of national-level GDP before official GDP statistics are released
• Engineering companies - to monitor and evaluate the localised impact of infrastructure projects
• Consultancies - to forecast the localised impact of specific projects, to retrospectively monitor and evaluate the localised impact of existing projects
• Economics firms - to create macro forecasts at the national and sub-national level, to assess the impact of policy interventions.
• Academia / Think Tanks - to conduct novel research at the local level. E.g. our dataset can be used to measure the impact of localised COVID-19 lockdowns.

We have created this dataset for all UK sub-national regions, 28 EU Countries and Switzerland.