A. SUMMARY This dataset represents San Francisco COVID-19 related deaths by day. This data may not be immediately available for recently reported deaths. Data updates as more information becomes available. Because of this, death totals for previous days may increase or decrease. More recent data is less reliable.

B. HOW THE DATASET IS CREATED As of January 1, 2023, COVID-19 deaths are defined as persons who had COVID-19 listed as a cause of death or a significant condition contributing to their death on their death certificate. This definition is in alignment with the California Department of Public Health and the national https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">Council of State and Territorial Epidemiologists. Death data is provided by the California Department of Public Health.

It takes time to process this data. Because of this, death totals may increase or decrease over time.

Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths.

C. UPDATE PROCESS Updates automatically at 06:30 and 07:30 AM Pacific Time on Wednesday each week.

Dataset will not update on the business day following any federal holiday.

D. HOW TO USE THIS DATASET This dataset shows new deaths and cumulative deaths by date of death. New deaths are the count of deaths on that specific date. Cumulative deaths are the running total of all San Francisco COVID-19 deaths up to the date listed.

Use the Deaths by Population Characteristics Over Time dataset to see deaths by different subgroups including race/ethnicity, age, and gender.

E. CHANGE LOG

• 9/11/2023 – on this date, we began using an updated definition of a COVID-19 death to align with the California Department of Public Health. This change was applied to COVID-19 deaths retrospectively beginning on 1/1/2023. More information about the recommendation by the Council of State and Territorial Epidemiologists that motivated this change can be found https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">here.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.

A. SUMMARY This dataset shows San Francisco COVID-19 deaths by population characteristics. This data may not be immediately available for recently reported deaths. Data updates as more information becomes available. Because of this, death totals may increase or decrease.

Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how deaths have been distributed among different subgroups. This information can reveal trends and disparities among groups.

B. HOW THE DATASET IS CREATED As of January 1, 2023, COVID-19 deaths are defined as persons who had COVID-19 listed as a cause of death or a significant condition contributing to their death on their death certificate. This definition is in alignment with the California Department of Public Health and the national https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">Council of State and Territorial Epidemiologists. Death certificates are maintained by the California Department of Public Health.

Data on the population characteristics of COVID-19 deaths are from: *Case reports *Medical records *Electronic lab reports *Death certificates

Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths.

To protect resident privacy, we summarize COVID-19 data by only one population characteristic at a time. Data are not shown until cumulative citywide deaths reach five or more.

Data notes on select population characteristic types are listed below.

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases.

Gender * The City collects information on gender identity using these guidelines.

C. UPDATE PROCESS Updates automatically at 06:30 and 07:30 AM Pacific Time on Wednesday each week.

Dataset will not update on the business day following any federal holiday.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a dataset based on the San Francisco Population and Demographic Census dataset.These population estimates are from the 2018-2022 5-year American Community Survey (ACS).

This dataset includes several characteristic types. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of cumulative deaths.

Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed.

To explore data on the total number of deaths, use the COVID-19 Deaths Over Time dataset.

E. CHANGE LOG

In 2020, global gross domestic product declined by 6.7 percent as a result of the coronavirus (COVID-19) pandemic outbreak. In Latin America, overall GDP loss amounted to 8.5 percent.

As of July 2nd, 2024 the COVID-19 Deaths by Population Characteristics Over Time dataset has been retired. This dataset is archived and will no longer update. We will be publishing a cumulative deaths by population characteristics dataset that will update moving forward.

A. SUMMARY This dataset shows San Francisco COVID-19 deaths by population characteristics and by date. This data may not be immediately available for recently reported deaths. Data updates as more information becomes available. Because of this, death totals for previous days may increase or decrease. More recent data is less reliable.

Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how deaths have been distributed among different subgroups. This information can reveal trends and disparities among groups.

B. HOW THE DATASET IS CREATED As of January 1, 2023, COVID-19 deaths are defined as persons who had COVID-19 listed as a cause of death or a significant condition contributing to their death on their death certificate. This definition is in alignment with the California Department of Public Health and the national https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">Council of State and Territorial Epidemiologists. Death certificates are maintained by the California Department of Public Health.

Data on the population characteristics of COVID-19 deaths are from: *Case reports *Medical records *Electronic lab reports *Death certificates

Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths.

To protect resident privacy, we summarize COVID-19 data by only one characteristic at a time. Data are not shown until cumulative citywide deaths reach five or more.

Data notes on each population characteristic type is listed below.

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases.

Gender * The City collects information on gender identity using these guidelines.

C. UPDATE PROCESS Updates automatically at 06:30 and 07:30 AM Pacific Time on Wednesday each week.

Dataset will not update on the business day following any federal holiday.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of deaths on each date.

New deaths are the count of deaths within that characteristic group on that specific date. Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed.

This data may not be immediately available for more recent deaths. Data updates as more information becomes available.

To explore data on the total number of deaths, use the COVID-19 Deaths Over Time dataset.

E. CHANGE LOG

• 9/11/2023 - on this date, we began using an updated definition of a COVID-19 death to align with the California Department o

NOTE: THIS LAYER HAS BEEN DEPRECATED (last updated 5/31/2022). This was formerly a weekly update. Summary The number of cases interviewed who had a completed answer to the question asking if they had physically gone to work in the last 14 days during their covidLINK interviews. Description MD COVID-19 - Contact Tracing Cases Reported Employment layer reflects the number of cases interviewed who had a completed answer to the question asking if they had physically gone to work in the last 14 days during their covidLINK interviews. Respondents may indicate more than one category of employment if they have multiple jobs. For a variety of reasons, some individuals choose not to answer particular questions during the course of their interview. Information about how to prevent and reduce COVID-19 transmission in businesses and workplaces — including for both employers and employees — is available from the Centers for Disease Control and Prevention. Note the following regarding select employment categories: Childcare/Education: Includes teachers, babysitters, school administrators, etc. Commercial Construction and Manufacturing: Includes poultry/meat processors, electricians, carpenters, HVAC workers, welders, contractors, painters Healthcare: Includes home healthcare and administrative positions in a healthcare setting Restaurant/Food Service: Includes cooks, waitstaff, food delivery personnel, alcohol delivery services, etc. Retail, Essential Worker: Includes grocery and pharmacy workers Retail, Other: Includes all retail establishments that do not sell food or medicine Transportation: Includes positions related to transport of people or goods Other, Non-Public-Facing: Includes workers that do not have direct interactions with the public, including warehouse workers, some office workers, some car mechanics, etc. Other, Public-Facing: Includes workers who have direct interactions with the public such as, but not limited to, administrative/front desk workers, home repair workers, lawncare workers, security guards, etc. Unknown: Indicates that the interviewer was unable to ascertain the employment category based on the information provided. Answers to interview questions do not provide strong evidence of cause and effect. Due to the nature of COVID-19 and the wide range of scenarios in which a person can become infected, most of the time it will not be possible to pinpoint exactly how and when a case became infected. Though a person may report employment at a particular location, that does not necessarily imply that transmission happened at that location. The covidLINK interview questionnaire is updated as necessary to capture relevant information related to case exposure and potential onward transmission. These revisions should be taken into consideration when evaluating trends in case responses over time. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

The Paycheck Protection Program (PPP) is a $953-billion business loan program established by the United States federal government, led by the Donald Trump administration in 2020 through the Coronavirus Aid, Relief, and Economic Security Act (CARES Act) to help certain businesses, self-employed workers, sole proprietors, certain non-profit organizations, and tribal businesses continue paying their workers.

The Paycheck Protection Program allows entities to apply for low-interest private loans to pay for their payroll and certain other costs. The amount of a PPP loan is approximately equal to 2.5 times the applicant's average monthly payroll costs. In some cases, an applicant may receive a second draw typically equal to the first. The loan proceeds may be used to cover payroll costs, rent, interest, and utilities. The loan may be partially or fully forgiven if the business keeps its employee counts and employee wages stable. The program is implemented by the U.S. Small Business Administration. The deadline to apply for a PPP loan was March 31, 2021.

Some economists have found that the PPP did not save as many jobs as purported and aided too many businesses that were not at risk of going under. They noted that other programs, such as unemployment insurance, food assistance, and aid to state and local governments, would have been more efficient at strengthening the economy. Opponents to this view note that the PPP functioned well to prevent business closures and cannot be measured on the number of jobs saved alone.

According to a 2022 study, the PPP: cumulatively preserved between 2 and 3 million job-years of employment over 14 months at a cost of $169K to $258K per job-year retained. These numbers imply that only 23 to 34 percent of PPP dollars went directly to workers who would otherwise have lost jobs; the balance flowed to business owners and shareholders, including creditors and suppliers of PPP-receiving firms. Program incidence was ultimately highly regressive, with about three-quarters of PPP funds accruing to the top quintile of households. PPP's breakneck scale-up, its high cost per job saved, and its regressive incidence have a common origin: PPP was essentially untargeted because the United States lacked the administrative infrastructure to do otherwise. Harnessing modern administrative systems, other high-income countries were able to better target pandemic business aid to firms in financial distress. Building similar capacity in the U.S. would enable improved targeting when the next pandemic or other large-scale economic emergency inevitably arises.

Additional Information Field: Value Created: April 5, 2022 Format: CSV License: Other (Public Domain) Size: 428.6 MiB

Abstract

To facilitate comparisons with the Latin America and the Caribbean (LAC) High-Frequency Surveys collected in 2021, harmonized versions of the COVID-19 High Frequency Phone Surveys 2022 Brazil databases have been produced. The databases follow the same structure as those for the countries in the region (for example, see: COVID-19 LAC High Frequency Phone Surveys 2021 (Wave 1)).

The Brazil 2021 COVID-19 Phone Survey was conducted to provide information on how the pandemic had been affecting Brazilian households in 2021, collecting information along multiple dimensions relevant to the welfare of the population (e.g. changes in employment and income, coping mechanisms, access to health and education services, gender inequalities, and food insecurity). A total of 2,166 phone interviews were conducted across all Brazilian states between July 26 and October 1, 2021. The survey followed an Random Digit Dialing (RDD) sampling methodology using a dual sampling frame of cellphone and landline numbers. The sampling frame was stratified by type of phone and state. Results are nationally representative for households with a landline or at least one cell phone and of individuals of ages 18 years and above who have an active cell phone number or a landline at home.

Geographic coverage

National level.

Analysis unit

Households and individuals of 18 years of age and older.

Sampling procedure

The sample is based on a dual frame of cell phone and landline numbers that was generated through a Random Digit Dialing (RDD) process and consisted of all possible phone numbers under the national phone numbering plan. Numbers were screened through an automated process to identify active numbers and cross-checked with business registries to identify business numbers not eligible for the survey. This method ensures coverage of all landline and cellphone numbers active at the time of the survey. The sampling frame was stratified by type of phone and state. See Sampling Design and Weighting document for more detail.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

Available in Portuguese. The questionnaire followed closely the LAC HFPS Questionnaire of Phase II Wave I but had some critical variations.

Displays a representation of where all the surveyed businesses across York Region are located. This data is collected through the Region’s annual comprehensive employment survey and each record contains employment and business contact information about each business with the exception of home and farm-based businesses. Home-based businesses are not included as they are distributed throughout residential communities within the Region and are difficult to survey. Employment data for farm-based businesses are collected through the Census of Agriculture conducted by Statistics Canada, and are not included in the York Region Employment Survey dataset.Update Frequency: Not PlannedDate Created: 17/03/2023Date Modified: 17/03/2023Metadata Date: 17/03/2023Citation Contacts: York Region, Long Range Planning Attribute Definitions BUSINESSID: Unique key to identify a business.NAME: The common business name used in everyday transactions. FULL_ADDRESS: Full street address of the physical address. (This field concatenates the following fields: Street Number, Street Name, Street Type, Street Direction)STREET_NUM: Street number of the physical addressSTREET_NAME: Street name of the physical addressSTREET_TYPE: Street type of the physical addressSTREET_DIR: Street direction of the physical addressUNIT_NUM: Unit number of the physical addressCOMMUNITY: Community name where the business is physically locatedMUNICIPALITY: Municipality where the business is physically locatedPOST_CODE: Postal code corresponding to the physical street addressEMPLOYEE_RANGE: The numerical range of employees working in a given firm. PRIM_NAICS, PRIM_NAICS_DESC: The Primary 5-digit NAIC code defines the main business activity that occurs at that particular physical business location.SEC_NAICS, SEC_NAICS_DESC: If there is more than one business activity occurring at a particular business location (that is substantially different from the primary business activity), then a secondary NAIC is assigned.PRIM_BUS_CLUSTER, SEC_BUS_CLUSTER: A business cluster is defined as a geographic concentration of interconnected businesses and institutions in a common industry that both compete and cooperate. As defined by York Region, this field indicates the primary business cluster that this business belongs to.BUS_ACTIVITY_DESC: This is a comment box with a detailed text description of the business activity. TRAFFIC_ZONE: Specifies the traffic zone in which the business is located. MANUFACTURER: Indicates whether or not the business manufactures at the physical business location. CAN_HEADOFFICE: The business at this location is considered the Canadian head office.HEADOFFICEPROVSTATE: Indicates which state or province the head office is located if the head office is located in Canada (outside of Ontario) or in the United StatesHEADOFFICECOUNTRY: Indicates which country the head office is locatedYR_CURRENTLOC: Indicates the year that the business moved into its current address.MAIL_FULL_ADDRESS: The mailing address is the address through which a business receives postal service. This may or may not be the same as the physical street address.MAIL_STREET_NUM, MAIL_STREET_NAME, MAIL_STREET_TYPE, MAIL_STREET_DIR, MAIL_UNIT_NUM, MAIL_COMMUNITY, MAIL_MUNICIPALITY, MAIL_PROVINCE, MAIL_COUNTRY, MAIL_POST_CODE, MAIL_POBOX: Mailing address fields are similar to street address fields and in most cases will be the same as the Street Address. Some examples where the two addresses might not be the same include, multiple location businesses, home-based businesses, or when a business receives mail through a P.O. Box.WEBSITE: The General/Main business website.GEN_BUS_EMAIL: The general/main business e-mail address for that location.PHONE_NO: The general/main phone number for the business location.PHONE_EXT: The extension (if any) for the general/main business phone number.LAST_SURVEYED: The date the record was last surveyedLAST_UPDATED: The date the record was last updatedUPDATEMETHOD: Displays how the business was last updated, based on a predetermined list.X_COORD, Y_COORD: The x,y coordinates of the surveyed business location Frequently Asked QuestionsHow many businesses are included in the 2022 York Region Business Directory? The 2022 York Region Business Directory contains just over 34,000 business listings. In the past, businesses were annually surveyed, either in person or by telephone to improve the accuracy of the directory. Due to the COVID-19 Pandemic, a survey was not complete in 2020 and 2021. The Region may return to annual surveying in future years, however the next employment survey will be in 2024. This listing also includes home-based businesses that participated in the 2022 employment survey. What is a NAIC code?The North American Industrial Classification (NAIC) coding system is a hierarchical classification system developed in Canada, Mexico and the United States. It was developed to allow for the comparison of business and employment information across a variety of industry categories. The NAICS has a hierarchical structure, designed as follows: Two-digits = sector (e.g., 31-33 contain the Manufacturing sectors) Three-digits = subsector (e.g., 336 = Transportation Equipment Manufacturing) Four-digits = industry group (e.g., 3361 = Motor Vehicle Manufacturing) Five-digits = industry (e.g., 33611 = Automobile and Light Duty Motor Vehicle Manufacturing) For more information on the NAIC coding system click here How do I add or update my business information in the York Region Business Directory? To add or update your business information, please select one of the following methods: • Email: Please email businessdirectory@york.ca to request to be added to the Business Directory.• Online: Go to www.york.ca/employmentsurvey and participate in the employment survey - note, this will only be active in 2024 when the Region performs its next employment surveyThere is no charge for obtaining a basic listing of your business in the York Region Business Directory. How up-to-date is the information?This directory is based on the 2022 York Region Employment Survey, a survey of businesses which attempts to gather information from all businesses across York Region. In instances where we were unable to gather information, the most recent data was used. Farm-based businesses have not been included in the survey and home-based businesses that participated in the 2022 survey are included in the dataset. The date that the business listing was last updated is located in the LastUpdate column in the attached spreadsheet. Are different versions of the York Region Business Directory available?Yes, the directory is available in two online formats:• An interactive, map-based directory searchable by company name, street address, municipality and industry sector.• The entire dataset in downloadable Microsoft Excel format via York Region's Open Data Portal. This version of the York Region Business Directory 2022 is offered free of charge. The Directory allows for the detailed analysis of business and employment trends, as well as the construction of targeted contact lists. To view the map-based directory and dataset, go to:2022 Business Directory - Map Is there any analysis of business and employment trends in York Region?Yes. The "2022 Employment and Industry Report" contains information on employment trends in York Region and is based on results from the employment survey. please visit www.york.ca/york-region/plans-reports-and-strategies/employment-and-industry-report to view the report. What other resources are available for York Region businesses?York Region offers an export advisory service and a number of other business development programs and seminars for interested individuals.For details, consult the York Region Economic Strategy Branch. Who do I contact to obtain more information about the Directory?For more information on the York Region Business Directory, contact the Planning and Economic Development Branch at:businessdirectory@york.ca.

This dataset contains detailed records of India’s principal commodity-wise exports to various countries from 2017–18 to 2022–23 (skipping 2020–21 due to COVID-related data gaps). The data is structured annually and provides insights into:

Exported commodity names Destination countries Export quantity Export value (in USD million) Measurement units

These records are sourced from https://www.data.gov.in/ and are valuable for analysts, researchers, and policymakers studying international trade trends, market demand, and sector-wise export performance.

📁 Files Included File Name Year Format Principal_Commodity_wise_export_201718.csv 2017–18 CSV Principal_Commodity_wise_export_201819.csv 2018–19 CSV Principal_Commodity_wise_export_201920.csv 2019–20 CSV Principal_Commodity_wise_export_202122.csv 2021–22 CSV Principal_Commodity_wise_export_202223.csv 2022–23 CSV

📌 Columns Each file includes the following columns: COMMODITY – Name of the exported product COUNTRY – Country to which the product was exported UNIT – Unit of measurement (e.g., KGS, NOS, LITRES) QUANTITY – Total exported quantity VALUE (US$ Million) – Export value in USD millions

💡 Use Cases Time-series analysis of export performance Identifying high-value export commodities Understanding trade relationships with countries Policy and strategy development for boosting exports Comparative analysis across years and regions

📊 Sample Questions You Can Explore What are the top 10 exported commodities from India over the last 5 years? How has India's export value to the USA evolved since 2017? Are there any emerging markets for Indian goods? What commodities saw a decline in exports after COVID-19?

Many households struggled both physically and financially during the COVID-19 crisis. In a time of such uncertainty, one might expect households to respond differently to financial instruments considered risker than others. Given the nature and general feelings around cryptocurrency, we expected there might be differences in how households that owned cryptocurrencies fared during the COVID-19 crisis as compared to those that did not own cryptocurrency. Our research found that cryptocurrency-owning households reported fewer financial challenges during the pandemic than households that did not own cryptocurrency. Specifically, they were less likely to experience food insecurity or miss payments on a variety of bills, including medical expenses and utilities. Crypto households experienced less unemployment, as both the head of the household and the partner more readily adapted to working from home. Crypto households were also less likely to experience death from COVID-19 than their counterparts were. Data from the Federal Reserve’s 2022 Survey of Consumer Finances (SCF) reveal that cryptocurrency-owning households in fact fared better than those who did not. The linear probability model results hold after correction for data imputation and controlling for financial literacy, willingness to take risks in the short- and long-term, income, wealth, gender, age, education level, work status, and race. These findings suggest a counternarrative to the mainstream opinion of cryptocurrency owners as risk-loving, irrational, retail day traders. This research contributes to the overall literature by showing households working with cryptocurrency make financially savvy decisions and are better off generally than their counterparts. As cryptocurrency continues to gain traction and assuming it grows at current rates, society will be greatly affected. First, more households may consider expanding their portfolios with cryptocurrency. Assuming this occurs, more individuals and companies will need to become more familiar with this risky asset and other mechanisms through which one can invest in crypto assets, such as exchange-traded funds. Second, cryptocurrency usage is not only increasing among households but businesses too, including public companies. Investors may want to review their other investments, particularly stocks, to see how they may be indirectly invested in cryptocurrency. This consideration may affect other investment motivation considerations in the impact investing space. Last, we demonstrated households had different experiences with the COVID-19 shock event. Individuals may consider cryptocurrency as another asset to diversify in moving forward depending on other potential shock events besides pandemics, such as global or regional recessions or country currency changes in international markets due to political risk.