This data package includes the underlying data files to replicate the data and charts presented in Central banks and policy communication: How emerging markets have outperformed the Fed and ECB, PIIE Working Paper 23-10.

If you use the data, please cite as: Evdokimova, Tatiana, Piroska Nagy Mohácsi, Olga Ponomarenko, and Elina Ribakova. 2023. Central banks and policy communication: How emerging markets have outperformed the Fed and ECB. PIIE Working Paper 23-10. Washington, DC: Peterson Institute for International Economics.

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in The Federal Reserve Engages the World (1970-2000): An Insider's Narrative of the Transition to Managed Floating and Financial Turbulence, PIIE Working Paper 14-5. If you use the data, please cite as: Truman, Edwin M. (2014). The Federal Reserve Engages the World (1970-2000): An Insider's Narrative of the Transition to Managed Floating and Financial Turbulence. PIIE Working Paper 14-5. Peterson Institute for International Economics.

The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families balance sheets, pensions, income, and demographic characteristics. Information is also included from related surveys of pension providers and the earlier such surveys conducted by the Federal Reserve Board. No other study for the country collects comparable information. Data from the SCF are widely used, from analysis at the Federal Reserve and other branches of government to scholarly work at the major economic research centers.The survey has contained a panel element over two periods. Respondents to the 1983 survey were re-interviewed in 1986 and 1989. Respondents to the 2007 survey were re-interviewed in 2009.The study is sponsored by the Federal Reserve Board in cooperation with the Department of the Treasury. Since 1992, data have been collected by the National Opinion Research Center (NORC) at the University of Chicago.

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in A program for strengthening the Federal Reserve’s ability to fight the next recession, PIIE Working Paper 20-5.

If you use the data, please cite as: Reifschneider, David, and David Wilcox. (2020). A program for strengthening the Federal Reserve’s ability to fight the next recession. PIIE Working Paper 20-5. Peterson Institute for International Economics.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Federal Heights. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Federal Heights, the median income for all workers aged 15 years and older, regardless of work hours, was $39,520 for males and $27,343 for females.

These income figures highlight a substantial gender-based income gap in Federal Heights. Women, regardless of work hours, earn 69 cents for each dollar earned by men. This significant gender pay gap, approximately 31%, underscores concerning gender-based income inequality in the city of Federal Heights.

- Full-time workers, aged 15 years and older: In Federal Heights, among full-time, year-round workers aged 15 years and older, males earned a median income of $49,339, while females earned $42,046, resulting in a 15% gender pay gap among full-time workers. This illustrates that women earn 85 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Federal Heights.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Federal Heights.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Federal Heights median household income by race. You can refer the same here

View the total value of the assets of all Federal Reserve Banks as reported in the weekly balance sheet.

National Levee DatabaseThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Army Corps of Engineers (USACE), displays levees within the United States. Per USACE, "The National Levee Database captures all known levees in the United States. It provides users with the ability to search for specific data about levees and serves as a national resource to support awareness and preparedness around flooding. The USACE is responsible for maintaining the National Levee Database and works in partnership with the Federal Emergency Management Agency (FEMA), and in close collaboration with other federal, state, and local governments and entities responsible for levees to obtain and share accurate and complete information."Leveed area in Morrisville, PennsylvaniaData downloaded: 4/24/2024Data source: NLD 2 PublicNGDAID: 161 (National Levee Database)OGC API Features Link: (National Levee Database - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: National Levee DatabaseSupport documentation: NLD Data DictionaryFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Water - Inland Theme Community. Per the Federal Geospatial Data Committee (FGDC), Water - Inland is defined as the "interior hydrologic features and characteristics, including classification, measurements, location, and extent. Includes aquifers, watersheds, wetlands, navigation, water quality, water quantity, and groundwater information."For other NGDA Content: Esri Federal Datasets

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Federal Way. The dataset can be utilized to gain insights into gender-based income distribution within the Federal Way population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Federal Way, among individuals aged 15 years and older with income, there were 36,477 men and 33,771 women in the workforce. Among them, 18,981 men were engaged in full-time, year-round employment, while 13,516 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 6.09% fell within the income range of under $24,999, while 8.92% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 26.21% of men in full-time roles earned incomes exceeding $100,000, while 19.66% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Federal Way median household income by race. You can refer the same here

This dataset is about books and is filtered where the book is Competition and monopoly in the Federal Reserve System, 1914-1951 : a micreconomics approach to monetary history, featuring 7 columns including author, BNB id, book, book publisher, and ISBN. The preview is ordered by publication date (descending).

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Federal Way. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Federal Way, the median income for all workers aged 15 years and older, regardless of work hours, was $49,179 for males and $34,280 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 30% between the median incomes of males and females in Federal Way. With women, regardless of work hours, earning 70 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Federal Way.

- Full-time workers, aged 15 years and older: In Federal Way, among full-time, year-round workers aged 15 years and older, males earned a median income of $64,318, while females earned $58,010, resulting in a 10% gender pay gap among full-time workers. This illustrates that women earn 90 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Federal Way.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Federal Way.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Federal Way median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Federal Heights. The dataset can be utilized to gain insights into gender-based income distribution within the Federal Heights population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Federal Heights, among individuals aged 15 years and older with income, there were 5,192 men and 3,880 women in the workforce. Among them, 3,059 men were engaged in full-time, year-round employment, while 1,711 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 11.28% fell within the income range of under $24,999, while 15.37% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 3.76% of men in full-time roles earned incomes exceeding $100,000, while 5.49% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Federal Heights median household income by race. You can refer the same here

Graph and download economic data for Federal Funds Target Range - Upper Limit (DFEDTARU) from 2008-12-16 to 2025-03-26 about federal, interest rate, interest, rate, and USA.

The 2005 Federal Campus-Based Programs Data Book provides comprehensive program funding information for these federal student aid programs: Federal Supplemental Educational Opportunity Grants, Federal Work-Study, and Federal Perkins Loans. Program allocation data is presented for award year 2005-2006. Fiscal and Recipient data are presented for award year 2003-2004. This is the home page for the data book.

About this dataset

Context

The dataset tabulates the Federal Dam population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Federal Dam. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 68 (43.04% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Federal Dam population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Federal Dam is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Federal Dam is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Federal Dam Population by Age. You can refer the same here

This administrative dataset provides descriptive information about the families and children served through the federal Child Care and Development Fund (CCDF). CCDF dollars are provided to states, territories, and tribes to provide assistance to low-income families receiving or in transition from temporary public assistance, to obtain quality child care so they can work, or depending on their state's policy, to attend training or receive education. The Personal Responsibility and Work Opportunity Act of 1996 requires states and territories to collect information on all family units receiving assistance through the CCDF and to submit monthly case-level data to the Child Care Bureau. States are permitted to report case-level data for the entire population, or a sample of the population, under approved sampling guidelines. The Summary Records file contains monthly state-level summary information including the number of families served. The Family Records file contains family-level data including single parent status of the head of household, monthly co-payment amount, date on which child care assistance began, reasons for care (e.g., employment, training/education, protective services, etc.), income used to determine eligibility, source of income, and the family size on which eligibility is based. The Child Records file contains child-level data including ethnicity, race, gender, and date of birth. The Setting Records file contains information about the type of child care setting, the total amount paid to the provider, and the total number of hours of care received by the child. The Pooling Factor file provides state-level data on the percentage of child care funds that is provided through the CCDF, the federal Head Start region the grantee (state) is in and is monitored by, and the State FIPS code for the grantee.

With the overview “GZR data on undeclared work 2019”, the Federal Office of Justice presents the result of evaluations of the fines for administrative offences in the area of undeclared work entered in the sub-registers on natural persons and legal persons and associations of persons in the commercial central register in 2019. The data set shows all fine decisions against natural persons who were entered in the commercial central register in accordance with Section 8(1)(2) in conjunction with § 8(1) No 1d Schwarzarbeitsbekämpfungsgesetz (SchwarzArbG). The entries are differentiated by federal/states. With the overview “GZR data on undeclared work 2019”, the Federal Office of Justice presents the result of evaluations of the fines for administrative offences in the area of undeclared work entered in the sub-registers on natural persons and legal persons and associations of persons in the commercial central register in 2019. The data set shows all fine decisions against natural persons who were entered in the commercial central register in accordance with Section 8(1)(2) in conjunction with § 8(1) No 1d Schwarzarbeitsbekämpfungsgesetz (SchwarzArbG). The entries are differentiated by federal/states. With the overview “GZR data on undeclared work 2019”, the Federal Office of Justice presents the result of evaluations of the fines for administrative offences in the area of undeclared work entered in the sub-registers on natural persons and legal persons and associations of persons in the commercial central register in 2019. The data set shows all fine decisions against natural persons who were entered in the commercial central register in accordance with Section 8(1)(2) in conjunction with § 8(1) No 1d Schwarzarbeitsbekämpfungsgesetz (SchwarzArbG). The entries are differentiated by federal/states. With the overview “GZR data on undeclared work 2019”, the Federal Office of Justice presents the result of evaluations of the fines for administrative offences in the area of undeclared work entered in the sub-registers on natural persons and legal persons and associations of persons in the commercial central register in 2019. The data set shows all fine decisions against natural persons who were entered in the commercial central register in accordance with Section 8(1)(2) in conjunction with § 8(1) No 1d Schwarzarbeitsbekämpfungsgesetz (SchwarzArbG). The entries are differentiated by federal/states. With the overview “GZR data on undeclared work 2019”, the Federal Office of Justice presents the result of evaluations of the fines for administrative offences in the area of undeclared work entered in the sub-registers on natural persons and legal persons and associations of persons in the commercial central register in 2019. The data set shows all fine decisions against natural persons who were entered in the commercial central register in accordance with Section 8(1)(2) in conjunction with § 8(1) No 1d Schwarzarbeitsbekämpfungsgesetz (SchwarzArbG). The entries are differentiated by federal/states. With the overview “GZR data on undeclared work 2019”, the Federal Office of Justice presents the result of evaluations of the fines for administrative offences in the area of undeclared work entered in the sub-registers on natural persons and legal persons and associations of persons in the commercial central register in 2019. The data set shows all fine decisions against natural persons who were entered in the commercial central register in accordance with Section 8(1)(2) in conjunction with § 8(1) No 1d Schwarzarbeitsbekämpfungsgesetz (SchwarzArbG). The entries are differentiated by federal/states. With the overview “GZR data on undeclared work 2019”, the Federal Office of Justice presents the result of evaluations of the fines for administrative offences in the area of undeclared work entered in the sub-registers on natural persons and legal persons and associations of persons in the commercial central register in 2019. The data set shows all fine decisions against natural persons who were entered in the commercial central register in accordance with Section 8(1)(2) in conjunction with § 8(1) No 1d Schwarzarbeitsbekämpfungsgesetz (SchwarzArbG). The entries are differentiated by federal/states. With the overview “GZR data on undeclared work 2019”, the Federal Office of Justice presents the result of evaluations of the fines for administrative offences in the area of undeclared work entered in the sub-registers on natural persons and legal persons and associations of persons in the commercial central register in 2019. The data set shows all fine decisions against natural persons who were entered in the commercial central register in accordance with Section 8(1)(2) in conjunction with § 8(1) No 1d Schwarzarbeitsbekämpfungsgesetz (SchwarzArbG). The entries are differentiated by federal/states. With the overview “GZR data on undeclared work 2019”, the Federal Office of Justice presents the result of evaluations of the fines for

This dataset is about book subjects and is filtered where the books includes An insider's guide to working for the federal government : navigating all levels of government as a civil servant or contractor, featuring 10 columns including authors, average publication date, book publishers, book subject, and books. The preview is ordered by number of books (descending).

Category A4 easements relate to easements of passage in the bed or on the banks of non-federal watercourses.These are easements of passage:- within the meaning of Articles L. 151-37-1 and R. 152-29 of the Rural Code, that is to say, “allowing the execution of the works, the operation and maintenance of the works and the passage over the private property of officials and officials responsible for supervision, contractors or workers, as well as mechanical equipment strictly necessary for carrying out operations”.- and established within the framework of the management of water, whether state or not, to enable “the execution and operation of all works, actions, works or installations of a nature of general interest or emergency” and relating to the powers referred to in Article L. 211-7 (I) — paragraphs 1 to 12 of the Environmental Code.This resource describes the linear generators of easements of category A4, works, works, facilities, streams, canals, lake or water body, including access to that watercourse, canal, lake or water body

The Corona crisis (COVID-19) and the accompanying measures are hitting some companies and their employees hard. Against this backdrop, the survey conducted by the opinion research institute Kantar in February 2021 examines the personal situation and working conditions of employed people in Germany in times of Corona. The analysis builds on a survey conducted in May 2020. importance of work: importance (ranking) of areas of life (family/ partnership, job and work, leisure, recreation and sport, friends and acquaintances, religion and church, politics and public life, (further) education, club work and voluntary work); importance of job (vocation vs. securing livelihood); change in importance of work; importance (ranking) of various work characteristics (e.g. income/security etc.); value of individual occupational groups. 2. Personal situation: change in working hours during the Corona crisis; current work situation (local focus of own work); number of days in home office; preference for home office; preference for future home office; preference for hybrid work model; advantages and disadvantages of home office; financial losses due to the Corona crisis or expectation of financial losses; expected financial situation in one year; worries due to the Corona crisis in personal areas; strength of support from employer during the Corona crisis. 3. Economy and welfare state: political interest; agreement with various statements on the balance of values in the Corona crisis; assessment of the measures taken so far by the federal government against the economic consequences of the Corona crisis; perception of state action in the Corona crisis (e.g. bureaucratic - non-bureaucratic, passive - active, etc.); competence of the state to provide financial support to businesses; competence of the state to provide financial support to private individuals; preferred recipient of state financial aid; assessment of bureaucracy in state financial aid. 4. Measures: awareness of current federal financial assistance measures; assessment of current financial assistance measures; use of financial assistance measures; type of assistance measures used; barriers to use of assistance; assessment of effectiveness of crisis management measures in terms of economic consequences; appropriate additional measures (open); concerns about consequences of measures in terms of economic consequences. 5. Information: information seeking about financial aid measures; awareness of financial aid measures; information gaps about financial aid measures; sources of information used about financial aid offers. Demography: age; sex; formal education; employment; self-placement in social class; net household income; assessment of current household income; assessment of household income before the crisis; occupation; membership of system-relevant professions; size of company; number of persons in household; number of children under 18 in household; city size; party affiliation; migration background. Additionally coded were: serial number; federal state; weighting factor.

View data of the Effective Federal Funds Rate, or the interest rate depository institutions charge each other for overnight loans of funds.