About this dataset

Context

The dataset tabulates the Economy population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Economy across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Economy was 8,962, a 0.18% decrease year-by-year from 2022. Previously, in 2022, Economy population was 8,978, a decline of 0.74% compared to a population of 9,045 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Economy decreased by 452. In this period, the peak population was 9,414 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Economy is shown in this column.
• Year on Year Change: This column displays the change in Economy population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Economy Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Economy by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Economy. The dataset can be utilized to understand the population distribution of Economy by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Economy. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Economy.

Key observations

Largest age group (population): Male # 65-69 years (412) | Female # 60-64 years (490). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Economy population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Economy is shown in the following column.
• Population (Female): The female population in the Economy is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Economy for each age group.

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 Economy Population by Gender. You can refer the same here

DMPED is using economic data to drive positive change and build good government for District of Columbia residents. They are focusing on collecting and compiling information about the city, in particular on D.C.’s economic development priorities that create more pathways to the middle class: jobs, quality affordable housing, and community-focused development.This site is an online version of the Deputy Mayor for Planning and Economic Development’s weekly dashboard. This dashboard is also transmitted to the City Administrator, the Mayor, and other senior staff, so they can be aware of economic trends and context. It includes only data that is public, so certain indicators that DMPED uses are not included.

According to a survey conducted in 2023, ** percent of Peruvians agreed that their country's economy is rigged in favor of the rich and powerful. This was the highest percentage of agreement among respondents in selected Latin American countries.

Graph and download economic data for Per Capita Personal Income in Shoshone County, ID (PCPI16079) from 1969 to 2023 about Shoshone County, ID; ID; personal income; per capita; personal; income; and USA.

The City of Port Adelaide Enfield Community Profile provides demographic and economic analysis for the Council area and its suburbs based on results from the 2016, 2011, 2006, 2001, 1996 and 1991 Censuses of Population and Housing. The profile is updated with population estimates when the Australian Bureau of Statistics (ABS) releases new figures. \r This is an interactive query tool where results can be downloaded in various formats. Three reporting types are available from this resource: \r 1. Social atlas that delivers the data displayed on a map showing each SA1 area (approx 200 households), \r 2. Community Profile which delivers data at a District level which contain 2 to 3 suburbs, and \r 3. Economic Profile which reports statistics of an economic indicators.\r The general community profile/social atlas themes available for reporting on are: \r -Age \r -Education \r -Ethnicity \r -Disability \r -Employment/Income \r -Household types \r -Indigenous profile \r -Migration \r -Journey to work \r -Disadvantage \r -Population Estimates \r -Building approvals. \r It also possible to navigate to the Community Profiles of some other Councils as well.

US County Demographics

Social, Health, and Economic Indicators

By Danny [source]

About this dataset

This dataset contains US county-level demographic data from 2016, giving insight into the health and economic conditions of counties in the United States. Aggregated and filtered from various sources such as the US Census Small Area Income and Poverty Estimates (SAIPE) Program, American Community Survey, CDC National Center for Health Statistics, and more, this comprehensive dataset provides information on population as well as desert population for each county. Additionally, data is split between metropolitan and nonmetropolitan areas according to the Office of Management and Budget's 2013 classification scheme. Valuable information pertaining to infant mortality rates and total population are also included in this detailed set of data. Use this dataset to gain a better understanding of one of our nation's most essential regions

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How to use the dataset

• Look at the information within the 'About this Dataset' section to have an understanding of what data sources were used to create this dataset as well as any transformations that may have been done while creating it.
• Familiarize yourself with the columns provided in the data set to understand what information is available for each county such as total population (totpop), parental education level (educationLvl), median household income (medianIncome), etc.,
• Use a combination of filtering and sorting techniques to narrow down results and focus in on more specific county demographics that you are looking for such as total households living below poverty line by state or median household income per capita between two counties etc.,
• Keep in mind any additional transformations/simplifications/aggregations done during step 2 when using your data for analysis. For example, if certain variables were pivoted during step two from being rows into columns because it was easier to work with multiple years of income levels by having them all consolidated into one column then be aware that some states may not appear in all records due to those transformations being applied differently between regions which could result in missing values or other inconsistencies when doing downstream analysis on your selected variables.
• Utilize resources such as Wikipedia and government census estimates if you need more detailed information surrounding these demographic characteristics beyond what's available within our current dataset – these can be helpful when conducting further research outside of solely relying on our provided spreadsheet values alone!

Research Ideas

• Creating a US county-level heat map of infant mortality rates, offering insight into which areas are most at risk for poor health outcomes.
• Generating predictive models from the population data to anticipate and prepare for future population trends in different states or regions.
• Developing an interactive web-based tool for school districts to explore potential impacts of student mobility on their area's population stability and diversity

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Food Desert.csv | Column name | Description | |:--------------------|:----------------------------------------------------------------------------------| | year | The year the data was collected. (Integer) | | fips | The Federal Information Processing Standard (FIPS) code for the county. (Integer) | | state_fips | The FIPS code for the state. (Integer) | | county_fips | The FIPS code for the county. (Integer)...

In 2023, agriculture contributed around 2.57 percent to the GDP of Australia, 27.65 percent came from industry, and 63.57 percent from the services sector. The same year, the Australian inflation rate, another important key indicator for its economic situation, amounted to 2.82 percent. Why is the inflation rate important?Inflation is the steady increase in price levels for consumer goods and services during a certain timespan. The European Central Bank considers a steady inflation rate of two percent a year beneficial for a stable economy – otherwise a country risks economic hardship. In the worst case, a country can experience either hyperinflation (like Venezuela), which is the rapid increase of prices to a point of economic collapse, or deflation, which is the decrease of prices and devaluation of money that can also lead to economic collapse. Up and down under Australia’s inflation has been clawing itself out of a slump in 2016, when it unceremoniously dropped to 1.25 percent due to falling petrol costs and oil prices. The following year, it recovered instantaneously and soared back to just under two percent, and forecasts see it reaching 2.52 percent by 2021. Australians don’t seem too worried about this outlier, and rightly so, since Australia’s economy is still one of the biggest in the Asia-Pacific region and worldwide.

Japan JP: Persistence to Last Grade of Primary: % of Cohort data was reported at 99.838 % in 2011. This records a decrease from the previous number of 99.931 % for 2010. Japan JP: Persistence to Last Grade of Primary: % of Cohort data is updated yearly, averaging 99.879 % from Dec 1971 (Median) to 2011, with 34 observations. The data reached an all-time high of 99.984 % in 2008 and a record low of 99.563 % in 1979. Japan JP: Persistence to Last Grade of Primary: % of Cohort data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank: Education Statistics. Persistence to last grade of primary is the percentage of children enrolled in the first grade of primary school who eventually reach the last grade of primary education. The estimate is based on the reconstructed cohort method.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

About Dataset

https://www.tbsnews.net/sites/default/files/styles/big_2/public/images/2021/03/12/inflation_1.jpg" alt="Inflation hits nine-year high in June | undefined">###

Global Energy, Food, Consumer, and Producer Price Inflation: A Comprehensive Dataset for Understanding Economic Trends

Key Concepts:

1. Energy Consumer Price Inflation data.
2. Food Consumer Price Inflation data.
3. Headline Consumer Price Inflation data.
4. Official Core Consumer Price Inflation data.
5. Producer Price Inflation data.
6. 206 Countries name, Country code and IMF code.
7. 52 Years data from 1970 to 2022.

The global economy is highly complex, and understanding economic trends and patterns is crucial for making informed decisions about investments, policies, and more. One key factor that impacts the economy is inflation, which refers to the rate at which prices increase over time. The Global Energy, Food, Consumer, and Producer Price Inflation dataset provides a comprehensive collection of inflation rates across 206 countries from 1970 to 2022, covering four critical sectors of the economy.

Finally, the Global Producer Price Inflation dataset provides a detailed look at price changes at the producer level, providing insights into supply chain dynamics and trends. This data can be used to make informed decisions about investments in various sectors of the economy and to develop effective policies to manage producer price inflation.

In conclusion, the Global Energy, Food, Consumer, and Producer Price Inflation dataset provides a comprehensive resource for understanding economic trends and patterns across 206 countries. By examining this data, analysts can gain insights into the complex factors that impact the economy and make informed decisions about investments, policies, and more.

Potential User:

1. Economists and economic researchers
2. Policy makers and government officials
3. Investors and financial analysts
4. Agricultural researchers and policymakers
5. Energy analysts and policy makers
6. Food industry professionals
7. Business leaders and decision makers
8. Academics and students in economics, finance, and related fields

Acknowledgements:

The data were collected from the official website of worldbank.org

This statistic shows the Economic Confidence Index, created by Gallup, on a monthly basis for the ongoing year. The survey is conducted doing weekly telephone interviews among approx. 2,499 adults in the U.S. The graph shows the results for the first update each month to depict an annual trend. The Index is computed by adding the percentage of Americans rating current economic conditions to the percentage saying the economy is (getting better minus getting worse), and then dividing that sum by 2. The Index has a value between null and +100. In December 2017, the U.S. Economic Confidence Index stood at 8.

ID: Ease of Doing Business Score: 0=Lowest Performance To 100=Best Performance data was reported at 69.579 NA in 2019. This records an increase from the previous number of 68.184 NA for 2018. ID: Ease of Doing Business Score: 0=Lowest Performance To 100=Best Performance data is updated yearly, averaging 66.868 NA from Dec 2015 (Median) to 2019, with 5 observations. The data reached an all-time high of 69.579 NA in 2019 and a record low of 62.112 NA in 2015. ID: Ease of Doing Business Score: 0=Lowest Performance To 100=Best Performance data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Indonesia – Table ID.World Bank.WDI: Business Environment. The ease of doing business scores benchmark economies with respect to regulatory best practice, showing the proximity to the best regulatory performance on each Doing Business indicator. An economy’s score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance.; ; World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme; Unweighted average; Data are presented for the survey year instead of publication year. Data before 2013 are not comparable with data from 2013 onward due to methodological changes.

Graph and download economic data for Per Capita Personal Income in Bonner County, ID (PCPI16017) from 1969 to 2023 about Bonner County, ID; ID; personal income; per capita; personal; income; and USA.

Graph and download economic data for Real Gross Domestic Product: Government and Government Enterprises in Lewis County, ID (REALGDPGOVT16061) from 2001 to 2023 about Lewis County, ID; enterprises; ID; government; real; GDP; and USA.

Graph and download economic data for Real Gross Domestic Product: Private Goods-Producing Industries in Jefferson County, ID (REALGDPGOODS16051) from 2001 to 2023 about Jefferson County, ID; Idaho Falls; goods-producing; ID; private; real; industry; GDP; and USA.

Graph and download economic data for Real Gross Domestic Product: All Industries in Teton County, ID (REALGDPALL16081) from 2001 to 2023 about Teton County, ID; ID; real; industry; GDP; and USA.

Abstract

Statistical information on all aspects of socio-economic activities is essential for the designing, monitoring, evaluation of development plans and policies. Labour force surveys are one of the important sources of data for assessing the role of the population of the country in the economic and social development process. These surveys provide data on the main characteristics of the work force engaged or available to be engaged in productive activities during a given period and its distribution in the various sectors of the economy. It is also useful to indicate the extent of available and unutilized human resources that must be absorbed by the national economy to ensure full employment and economic well being of the population. Furthermore, the information obtained from such surveys is useful for the purpose of macro-economic monitoring and evaluation of human resource development planning. The other broad objective of statistics on the labour force is for the measurement of relationship between employment, income and other social and economic characteristics of the economically active population for the purpose of formulating, monitoring and evaluation of employment policy and programs. Seasonal and other variations and changes over time in the size and characteristics of the employment and unemployment can be monitored using up-to-date information from labour force surveys.

CSA has been providing labour force and related data at different levels and with varying content details. These include the 1976 Addis Ababa Manpower and Housing Sample Survey, the 1978 Survey on Population and Housing Characteristics of Seventeen Major Towns, the 1980/81 and 1987/88 Rural Labour Force Surveys, and the 1984 & 1994 Population and Housing Census. A comprehensive national labour force result representing both urban and rural areas was also provided based on the 1999 Labour Force Survey. The 1996 and 2002 Surveys of Informal Sector and most of the household surveys also provide limited data on the area. Moreover, some information can be derived from small, large and medium scale establishment surveys.

As the sector is dynamic and sensitive to economic and social changes, it is important to have up to date data that will show current levels and that will be used for trend and comparative analysis. Earlier data in this regard were not regular and up to date. Thus, to fill-in the data gap in this area, a series of current and continuous labour force surveys need to be undertaken. Recognizing this fact and in response to request from different data users, the CSA had launched a Bi-annual Employment and Unemployment Survey program starting October, 2003 G.C.

This survey is the second in the series. Like the first round, it covered only urban areas of all regions with the exception of Gambella.

Objectives of the survey The Bi-annual Employment and Unemployment Survey program was designed to provide statistical data on the size and characteristics of the economically active and the non-active population of the country on continuous basis. The data will be useful for policy makers, planners, researchers, and other institutions and individuals engaged in the design, implementation and monitoring of human resource development projects and the performance of the economy.

The specific objectives of this survey were to: - Up date data on the size of work force that is available to participate in production process; - Determine the status and rate of economic participation of different sub-groups of the population; - Identify those who are actually contributing to the economic development (employed) and those out of the sphere; - Determine the size and rate of unemployed population; - Provide data on the structure of the working population; - Obtain information about earnings from paid employment; - Identify the distribution of employed population in the formal/informal sector of the economy; - Generate data to trace changes over time.

Geographic coverage

The 2004 Urban Bi-annual Employment and Unemployment Survey (UBEUS) covered only urban parts of the country. Except three zones of Afar, six zones of Somali regions, where the residents are pastoralists, and every part of Gambella region, all urban centers of the country were considered in this survey.

Analysis unit

• Household
• Individual aged 10 years and above

Universe

All households in the selected samples, except residents of collective quarters, homeless persons and foreigners.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design and Sample Size: Information from the listing of the 1994 Population and Housing Census was utilized to develop the sampling frame for the 2004 Urban Bi-annual Employment and Unemployment Survey. It was by taking in to account of cost and precision of major variables that determination of sample size was achieved. Moreover, in order to judge precisions of major variables, the 1999 Labor Force Survey result was the main source of information that was taken into consideration.

Except Harari, Addis Ababa and Dire Dawa, where all urban centers of the domain were incorporated in the survey, in other domains a three stage stratified cluster sample design was adopted to select the samples from each domain. The primary sampling units (PSU's) were urban centers selected systematically using probability proportional to size; size being number of households obtained from the 1994 Population and Housing Census. From each selected urban centers enumeration areas (EA's) were selected as a second-stage sampling unit (SSU). The selection of the SSU's was also done using probability proportional to size; size being number of households obtained from the 1994 Population and Housing Census. For each sampled EA a fresh list of households was prepared at the beginning of the survey. Thirty households from each sample EA were selected at the third stage. The survey questionnaire was finally administered to those thirty households selected at the last stage.

The selection scheme for Harari, Addis Ababa and Dire Dawa was similar to the case explained above. However, in these three domains instead of a three-stage design a two-stage stratified cluster sample design with enumeration areas as PSU and households (from the fresh list) as secondary sampling unit was used.

Note: Distribution of sampling units (planned and covered) by domain (reporting level) is given in Summary Table 2.1 of the 2004 Urban Bi-annual Employment Unemployment Survey Round 2 report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Same questionnaire used for the first round survey was administered in this round (round 2).

The questionnaire was organized in to five sections; Section - 1: Area identification of the selected household: this section dealt with area identification of respondents such as region, zone, wereda, etc.,

Section - 2: Demographic characteristics of household: it consisted of the general socio-demographic characteristics of the population such as age, sex, education, states & types of training and marital status.

Section - 3: Economic activity during the last six months: this section covered the usual economic activity status, number of weeks of Employment /Unemployment and reasons for not usually working.

Section - 4: Productive activities during the last seven days: this section dealt with the status and characteristics of employed persons such as hours of work occupation, industry, employment status, and Earnings from employment.

Section - 5: Unemployment and characteristics of unemployed persons: the section focused on the size and characteristics of the unemployed population.

Note: The questionnaire is provided as external resource.

Cleaning operations

Data Editing, Coding and Verification: The filled-in questionnaires that were retrieved from the field were first subjected to manual editing and coding. During the fieldwork the field supervisors, Statisticians and the heads of branch statistical offices have checked the filled-in questionnaires and carried out some editing. However, the major editing and coding operation was carried out at the head office. All the edited questionnaires were again fully verified and checked for consistency before they were submitted to the data entry. After the data was entered, it was again verified using the computer.

Data Entry, Cleaning and Tabulation: Using the computer edit specification prepared earlier for this purpose, the entered data were checked for consistencies and then computer editing or data cleaning was made by referring back to the filled-in questionnaire. This is an important part of data processing operation in attaining the required level of data quality. Consistency checks and re-checks were also made based on tabulation results. Computer programs used in data entry, machine editing and tabulation were prepared using the Integrated Microcomputer Processing System (IMPS).

Response rate

As regards the response rate of the survey, a total of 99 urban centers were selected and incorporated in to the survey. To be covered by the survey, 527 enumeration areas was initially selected, and the survey could successfully be carried out in 507 (96.20%) out of all the 527 of the EA's. The total number of expected households that were to be interviewed was 15810; however, due to different reasons 740 sample households were not interviewed, including households from 20 EAs of Gambella Region. As a result only 15070 households were actually covered by the survey, which made the ultimate response rate of the survey 95.32 %.

Sampling error

About this dataset

Context

The dataset presents the median household income across different racial categories in Economy. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Economy population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 94.13% of the total residents in Economy. Notably, the median household income for White households is $96,548. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $96,548.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Economy.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

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 Economy median household income by race. You can refer the same here