About this dataset

Context

The dataset presents median household incomes for various household sizes in Ponce Inlet, FL, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Ponce Inlet did not include 5, 6, or 7-person households. Across the different household sizes in Ponce Inlet the mean income is $127,760, and the standard deviation is $94,684. The coefficient of variation (CV) is 74.11%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $51,175. It then further increased to $265,668 for 4-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/ponce-inlet-fl-median-household-income-by-household-size.jpeg" alt="Ponce Inlet, FL median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

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

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

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 Ponce Inlet median household income. You can refer the same here

About this dataset

Context

The dataset presents median household incomes for various household sizes in Jupiter Inlet Colony, FL, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Jupiter Inlet Colony did not include 1, 3, 6, or 7-person households. Across the different household sizes in Jupiter Inlet Colony the mean income is $168,082, and the standard deviation is $144,539. The coefficient of variation (CV) is 85.99%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households. Please note that the U.S. Census Bureau uses $250,001 as a JAM value to report incomes of $250,000 or more. In the case of Jupiter Inlet Colony, there were 1 household sizes where the JAM values were used. Thus, the numbers for the mean and standard deviation may not be entirely accurate and have a higher possibility of errors. However, to obtain an approximate estimate, we have used a value of $250,001 as the income for calculations, as reported in the datasets by the U.S. Census Bureau.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 2-person households, with an income of $231,315. It then further decreased to 2,701 for 5-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/jupiter-inlet-colony-fl-median-household-income-by-household-size.jpeg" alt="Jupiter Inlet Colony, FL median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

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

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

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 Jupiter Inlet Colony median household income. You can refer the same here

Abstract

SUSENAS (National Socio-economic Survey) was held for the first time in year 1963. In the last two decades, up to year 2010, SUSENAS was conducted every year. SUSENAS was designed to have 3 modules (Module of Household Consumption/Expenditure, Module of Education and Socio-culture, and also Module of Health and Housing) and each module should be conducted every 3 years. Household Consumption/ Expenditure Module of SUSENAS shall be conducted in year 2011.

To improve the accuracy of data result and in line with the increased frequency of household consumption/expenditure data request for quarterly GDP/GRDP and poverty calculation, data collection of household consumption/expenditure, it is planned that starting in 2011 it should be held quarterly. Each year, collecting data shall be conducted in March, June, September, and December.

In accordance with the 5-year cycle, in year 2012, BPS (Central Statistical Agency) shall have planned Survei Biaya Hidup-SBH (Cost of Living Survey) with the aim to generate a commodity package and a weigh diagram in the calculation of Consumer Price Index (CPI). Data of food and non-food consumption expenditures as well as household characteristics collected in SBH and SUSENAS has the same concept/definition, but different implementation time. In order to be more efficient in the utilization of resources of the two surveys and to have a better quality of results achieved, in year 2011 a trial of SUSENAS and SBH integration shall be conducted in 7 cities (Medan, Sampit, Denpasar, Kudus, Bulukumba, Tual, and South Jakarta).

Poverty data, CPI/Inflation data, GDP/GRDP are BPS strategic data that have to be released on time. Therefore, planning, field preparation, processing, and presentation of data SUSENAS 2011 activities and trial of integrating SUSENAS and SBH must be in accordance with the set schedule.

Activities of SUSENAS 2011 preparation shall be conducted in year 2010, covering activities of workshop/training of chief instructor with the aim to synchronize the perception toward the concept/definition as well as procedure and protocol of survey implementation. National instructor training will also be conducted in year 2010.

Geographic coverage

National coverage, representative to the district level

Analysis unit

Household Members (Individual) and Household

Universe

Susenas 2011 cover 300,000 household sample spread all over Indonesia where each quarter distribute about 75,000 household sample (including 500 households additional sample for Survey in Maluku Province). The result from each quarter can produce national and provincial level estimates. Meanwhile from the cummulative four quarter, the data can be presented until the district/municipality level.

Sampling procedure

Sampling method is the structured three phase sampling with the following method:

a. First phase, selection of nh census area from Nh with pps (Probability Proportional to Size)with sizeas the total households of SP2010 (M i ).The census area is then randomly allocated into four quarters. Total sampling will be nh= 30,000 census areas thus there will be 7,500 census areas for each quarter. From 7,500 census areas of the First Quarter of the National Socio-Economic Survey (Susenas), some 5,000 census areas are systemically selected for the First Quarter of the 2011 National Labor Force Survey (Sakernas) and will be used again for the second, third and fourth quarter

b. Second phase, to select: - two BS from each selected census area of the second and third quarter of Susenas, and the first quarter which is also selected for the first quarter of Sakernas, which then from the selected census blocks, is randomly allocated one for Susenas/SBH, and one [for] Sakernas, or - one BS from each selected census area of the fourth quarter and first quarter only for Susenas with pps with a household size of SP2010-RBL1.

c. Third phase, from each selected census block for Susenas, a number of regular households are systemically selected (m=10) based on the updated SP2010-C1 household listing by using the VSEN11-P List. Names of household head (KRT) are extracted from SP2010-C1 for name, address and education level variables, followed by field updates.

Mode of data collection

Face-to-face

About this dataset

Context

The dataset tabulates the Inlet town 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 Inlet town. 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 143 (74.09% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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 Inlet town population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Inlet town is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Inlet town 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 Inlet town Population by Age. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Ponce Inlet, FL, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 25,010, while the mean income for the highest quintile (20% of households with the highest income) is 416,004. This indicates that the top earners earn 17 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 879,347, which is 211.38% higher compared to the highest quintile, and 3515.98% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

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 Ponce Inlet median household income. You can refer the same here

Abstract

Susenas is a survey designed to collect socio-demographic data in large area. The data collected were related to the fields of education, health / nutrition, housing / environmental, socio-cultural activities, consumption and household income, trips, and public opinion about the welfare of household. In 1992, Susenas data collection system has been updated, the information used to develop indicators of welfare (Welfare) contained in the module (information collected once every three years) drawn into the core (group information is collected each year).

In 2005 Susenas implement the module consumption / expenditure and household income. The data collected is the basic ingredient for calculating estimates of poverty based on consumption module Susenas three years (the latest data of 2002). However, given the poverty alleviation is a priority program of the current government; the Central bureau of statistic (BPS) attempted to provide data-poor national estimates on an annual basis. With the collecting data consumption / expenditure details every year it will be estimated annual number of poor people.

To meet the data needs of the government about the development of poor people every year, Panel Susenas collected the consumption and expenditure module data with the total sample of 10,000 households in 2003. The number of samples is only able to estimate the national poverty, while the demands of the availability data of poverty rate up to provincial level is increasing.

Geographic coverage

National coverage, representative to the district level

Analysis unit

Household Members (Individual) and Household

Universe

Sample size core and module panel Susenas consumption in 2005 include 10,640 households spread across the geographic regions of Indonesia. Sample frame used for module consumption Susenas Panel 2005 is selected block census of Susenas consumption module 2005. Due to the implementation of Susenas 2005 June-July 2005, whereas Susenas Panel 2005 in February, then subsample Susenas 2005 of enumerated first and then enumeration all samples Susenas 2005 in June-July 2005. The power of estimate from result 2005 Panel Susenas only at the national level and can be differentiated according to the type of area (urban and rural).

Kind of data

Sample survey data [ssd]

Sampling procedure

• Sample Core 2005 Susenas designed to estimate until the district / city level. The design of samples core Susenas 2004 was sample designs phased two for block census that are not formed sub-block census and sample design phased three for census block formed census sub block, both for urban and rural areas. Sample selection for urban and rural areas is done separately.

Census block was not formed census sub block

1. The first phase, from the sample frame census blocks selected a number of census block by Probability Proportional to Size (PPS) - systemic linear sampling whereas size is the number of households from P4B census result.

2. The second phase, from a number of households of listing results in each census block selected and selected 16 household by systematic linear sampling.

Block census was formed sub block census

1. The first phase, from the sample frame of census blocks selected a number of census blocks in PPS method - systemic linear systematic whereas size is the number of household of listing results from P4B census result.

2. The second phase, from each selected census block was formed segment group (kelseg), then selected one segment group in PPS Sampling whereas size is the number of household of listing results from P4B census result in each sub block census.

3. The third phase, from number of households of listing results in each selected enumeration areas selected 16 households by systemic linear sampling.

Mode of data collection

Face-to-face

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE NATIONAL INSTITUTE OF STATISTICS - TUNISIA (INS)

The National Survey on Household Budget, Consumption, and Standard of Living is a quinquennial survey. The 2010 survey is the ninth of its kind that was carried out by the National Institute of Statistics (INS) in Tunisia. The eight previous surveys were conducted in 1968, 1975, 1980, 1985, 1990, 1995, 2000 and 2005, concurrently with the preparatory work for the Tunisian development plans.

The survey aims at providing detailed information on the procurement of goods and services for consumption. Its data was collected from direct observation of household consumption to allow for having the necessary elements to assess the situation & changes in the living standards & conditions of the households.

The National Survey on Household Budget, Consumption, and Standard of Living consists of three fundamental parts; the budget survey, the nutrition survey and the access to community services survey. Thus, it tackles three areas of study: 1- Households expenses and acquisitions during the survey period. 2 - Food consumption and nutritional status of households. 3 - Household access to health and education community services.

The main objectives of the "budget survey" are: a- Estimate the levels of expenditure on the household level: The total expenditure of the household is not only an indicator on household income, but it is also a quantitative assessment of the standard of living index. b- Evaluate the income distribution: Due to the absence of data on income distribution, the mass distribution of expenditure between the different categories of the population constitutes a first sketch for the income distribution in the country. c- Assess the structure of expenditure: Detailed information collected on expenditures per product are used to establish the structures of the household expenditure, as well as the budget coefficients according to different levels of classifications of goods and services. These coefficients are particularly useful in the revision and development of the Consumer Prices Index (CPI) weights. d- Predict the demand of households: The household behavior, assessed in terms of product demand, is synthesized by the coefficients of income elasticity, which, according to the model of consumption retained and under the assumptions of the growth of income and population, allows predicting future household demand. e- Analyze the importance of consumer subsidies: analysis of the consumption of subsidized goods by expenditure deciles allows identifying the impact of direct consumer subsidies. It also allows evaluating the effectiveness of public policies grants.

The main objectives of "the nutrition survey" are: a- Provide estimates of food consumption by product for different groups of households according to their demographic and socio-economic characteristics. b- Estimate food consumption of each product by collecting data on the quantities consumed of each product by source, whether purchased or own produced. c- Identify the nutritional status of the population according to its demographic, geographic and socio-economic level. The comparison between the standards needs of nutrients to those acquired by the household enables assessing of the nutritional status and thus deficits in different nutrients such as calories, protein, vitamins, calcium, ... can also be captured. d- Estimate the calorie intake and energy needs of the Tunisian population: This estimate is indispensible in the calculation of the food component of the poverty line and, in consequence, the threshold of global poverty.

The main objective of "the access to community services survey" is to provide an overview on the state of morbidity of the Tunisian population, from one hand, and on the households' access to various health and education public services on other hand.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing household surveys in several Arab countries.

Geographic coverage

Covering a sample of all urban, small and medium towns and rural areas.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling method

The National Survey on Household Budget, Consumption and Standard of Living, 2010 has focused initially on a sample of 13,392 households drawn using a two stages stratified random sampling in each governorate. The sampling frame follows that of the General Census of Population and Housing in 2004 which was updated during the implementation of the National Population and Employment Survey in 2009.

Stratification criteria: The sampling frame is stratified by two geographical criteria: namely the governorate and the living area. The latter is stratified as follows: large cities, medium and small cities, and non-communal areas.

These stratification criteria (governorate, living area and size of city) represent variables that differentiate between surveyed households' lifestyles. Thus, the 3 strata types used are as follows:

Stratum of large cities (stratum 1): This stratum is formed of large urban centers corresponding to municipalities with more than 100.000 inhabitants and neighboring municipalities.

Stratum of medium and small cities (stratum 2): This stratum includes all medium and small sized cities other than those classified in the stratum of large cities.

Stratum of non-communal areas (stratum 3): It includes agglomerations in rural areas that are classified as major agglomerations in the General Census of Population and Housing 2004 and the National Population and Employment Survey in 2009. In addition to other areas that are located outside the territory of main municipalities and cities. 

Households in these areas reside in scattered dwellings or are grouped in small agglomerations.

Survey type

The sampling frame is divided on the level of each governorate according to strata previously defined. On the stratum level, a two-stage random sampling is planned for the selection of the survey sample of households. This process allows to breakdown the sample into clusters of 12 households relatively little distant from each other, thereby facilitating the conduct of the survey at the time of the information collection in the field.

In the first stage, a sample of 1,116 primary units is drawn in proportion to the number of households identified in the 2009National Population and Employment Survey. Taking into consideration that the primary units correspond to the districts that have been defined in the General Census of Population and Housing in 2004, which are geographic areas comprising on average 70 households.

In the second stage, from each primary unit (or cluster), twelve households are drawn through a simple random sampling technique. A substitutive sample of 12 additional households is further drawn from each primary unit. Those additional households constituting a substitutive list are used to cover for unidentified households at the time of the survey, given the mobility of households and the period between the date on which the sample is drawn and the date on which the survey is conducted.

Sample size

The size of the sample drawn in the first stage is 1,116 primary sampling units (PSU) corresponding to 13,392 households. The samples in the second stage are 12 households per primary unit. To optimize the use of logistic and material resources available, a sample of at least 36 PSU was selected from the less populated governorates, 3 PSU per month (the survey is conducted over a 12 months period). This represents the monthly work of the survey team (3 interviews and 1 supervisor to whom a car is assigned). Moreover, as the number of households varies from one governorate to another, it was agreed to adopt different rate of sampling from one governorate to another.

The following table shows the regional distribution of the sample and the corresponding sampling rates.

Regional Distribution of the Survey Sample

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Middle Inlet, Wisconsin, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 20,755, while the mean income for the highest quintile (20% of households with the highest income) is 186,714. This indicates that the top earners earn 9 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 350,363, which is 187.65% higher compared to the highest quintile, and 1688.09% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 Middle Inlet town median household income. You can refer the same here

This statistic shows the consumption of bagels in the United States in 2020. The data has been calculated by Statista based on the U.S. Census data and Simmons National Consumer Survey (NHCS). According to this statistic, 202.07 million Americans consumed bagels in 2020.

Results presented were weighted to be representative of the elderly population of São Paulo based on the 2000 Census. Brazil.& Frequency of alcohol intake in previous three months: low intake = < one day a week; moderate intake = one to three days a week; high intake = four or more days a week.Sample size;# Rao-Scott chi-square test; income was categorized by quartiles;1 to 4 missing responses for these variables;Accidental falls in previous 12 months.

Abstract

The energy statistics program has implemented fifteen rounds of the Household Energy Survey during 1999-2009.

Because of the importance of the household sector and due to its large contribution to energy consumption in the Palestinian Territory, PCBS decided to conduct a special Household Energy Survey to cover energy indicators in the household sector. To achieve this, a questionnaire was attached to the Labor Force Survey.

This survey aimed to provide data on energy consumption in the household sector and to provide data on energy consumption behavior and patterns in the society by type of energy.

The survey presents data on energy indicators pertaining to households in the Palestinian Territory. This includes statistical data on electricity and other fuel consumption by households covering type of fuel for different activities (cooking, baking, conditioning, lighting, and water heating).

Geographic coverage

Palestinian Territory

Analysis unit

households

Universe

The target population was all Palestinian households living in the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Frame The sample is a two-stage stratified cluster random sample.

Target Population The target population was all Palestinian households living within the Palestinian Territory.

Sampling Frame The sampling frame is a master sample from the overall sample that were updated in 2003 for the households that were visited a third or fourth time, while the households to be visited for the first and second time were chosen from the general frame of Population, Housing and Establishment Census 2007. It consists of a list of enumeration areas used as PSU's in the first stage of selection, and the household frame was used in the enumerator areas to choose households in the second level. The frame of the households has been updated in the enumerator areas for the new general sample at the end of year 2003.

Sampling Design The sample of this survey is a sub-sample of the Labour Force Survey (LFS) sample, which is conducted every 13 weeks. The sample of LFS is distributed over 13 weeks. The sample of the Household Energy Survey occupies six weeks of the third quarter of 2009 of the LFS.

Stratification: In designing the sample of the LFS, three levels of stratification were made: Stratification by number of households. Stratification by place of residence which comprises: (a) Urban (b) Rural (c) Refugee camps Stratification by locality size.

Sample Unit: In the first stage, the sampling units are the enumerator areas (clusters) in the master sample. In the second stage, the sampling units are households.

Analysis Unit: Analysis units are composed of households.

Sample Size: The sample size is of (3,234) Palestinian households in the West Bank and Gaza Strip, where this sample has been distributed according to the locality in urban areas, in rural areas and in refugee camps.

Mode of data collection

Face-to-face [f2f]

Research instrument

The design of the questionnaire for the Household Energy Survey was based on the experiences of similar countries as well as on international standards and recommendations for the most important indicators, taking into account the special situation of the Palestinian Territory.

Cleaning operations

The data processing stage consisted of the following operations: Editing and coding before data entry: All questionnaires were edited and coded in the office using the same instructions adopted for editing in the field.

Data entry: At this stage, data was entered into the computer using a data entry template developed in Access. The data entry program was prepared to satisfy a number of requirements such as: · To prevent the duplication of the questionnaires during data entry. · To apply integrity and consistency checks of entered data. · To handle errors in user friendly manner. · The ability to transfer captured data to another format for data analysis
using statistical analysis software such as SPSS.

Response rate

During fieldwork 3,234 Households were visited in the Palestinian Territory, the end results for the interview become as following: (2,846) Complete questionnaire
(43) Traveling households
(19) Housing unit not existed
(92) Cases no body in the house
(45) Refused cases
(130) Housing unit abandoned
(32) Household can't give data
(27) Other cases The percent of completed questioner were 88% of the total questioner

Sampling error estimates

It includes many aspects of the survey, mainly statistical errors due to the sample, and non statistical errors referring to the workers and survey tools. It includes also the response rates in this survey and their effect on the assumptions. This section includes:

1. Sampling Errors These types of errors evolved as a result of studying a part of the society and not all of it. Because this survey is a sample, the data of this survey will be affected by sampling errors due to using a sample and not the whole frame of the society. Differences appear compared with the actual values that could be obtained through a census. For this survey, variance calculations were made for average household consumption and total consumption for the different types of energy in the Palestinian Territory.

2. Non Sampling Errors These errors are due to non-response cases as well as the implementation of surveys. In this survey, these errors emerged because of (a) the special situation of the questionnaire itself, which depends on a type of estimation, (b) diversity of sources (e.g., the interviewers, respondents, editors, coders, data entry operator, etc).

The sources of these errors can be summarized as: Some of the households were not in their houses and the interviewers could not meet them. Some of the households did not give attention to the questionnaire.
Some errors occurred due to the way the questions were asked by interviewers. Misunderstanding of the questions by the respondents. Answering the questions related to consumption by making estimations.

Data appraisal

The data of the Household Energy Survey is comparable geographically and over time by comparing the data between different geographical areas with the data of previous surveys and census 2007.

About this dataset

Context

The dataset tabulates the Middle Inlet town 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 Middle Inlet town. 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 364 (42.13% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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 Middle Inlet town population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Middle Inlet town is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Middle Inlet town 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 Middle Inlet town Population by Age. You can refer the same here

Abstract

The Integrated Household Survey is one of the primary instruments implemented by the Government of Malawi through the National Statistical Office (NSO) roughly every 3-5 years to monitor and evaluate the changing conditions of Malawian households. The IHS data have, among other insights, provided benchmark poverty and vulnerability indicators to foster evidence-based policy formulation and monitor the progress of meeting the Millennium Development Goals (MDGs), the goals listed as part of the Malawi Growth and Development Strategy (MGDS) and now the Sustainable Development Goals (SDGs).

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Consumption expenditure commodities/items
• Communities
• Agricultural household/ Holder/ Crop
• Market

Universe

Members of the following households are not eligible for inclusion in the survey: • All people who live outside the selected EAs, whether in urban or rural areas. • All residents of dwellings other than private dwellings, such as prisons, hospitals and army barracks. • Members of the Malawian armed forces who reside within a military base. (If such individuals reside in private dwellings off the base, however, they should be included among the households eligible for random selection for the survey.) • Non-Malawian diplomats, diplomatic staff, and members of their households. (However, note that non-Malawian residents who are not diplomats or diplomatic staff and are resident in private dwellings are eligible for inclusion in the survey. The survey is not restricted to Malawian citizens alone.) • Non-Malawian tourists and others on vacation in Malawi.

Kind of data

Sample survey data [ssd]

Sampling procedure

The IHS5 sampling frame is based on the listing information and cartography from the 2018 Malawi Population and Housing Census (PHC); includes the three major regions of Malawi, namely North, Center and South; and is stratified into rural and urban strata. The urban strata include the four major urban areas: Lilongwe City, Blantyre City, Mzuzu City, and the Municipality of Zomba. All other areas are considered as rural areas, and each of the 27 districts were considered as a separate sub-stratum as part of the main rural stratum. The sampling frame further excludes the population living in institutions, such as hospitals, prisons and military barracks. Hence, the IHS5 strata are composed of 32 districts in Malawi.

A stratified two-stage sample design was used for the IHS5.

Note: Detailed sample design information is presented in the "Fifth Integrated Household Survey 2019-2020, Basic Information Document" document.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

HOUSEHOLD QUESTIONNAIRE The Household Questionnaire is a multi-topic survey instrument and is near-identical to the content and organization of the IHS3 and IHS4 questionnaires. It encompasses economic activities, demographics, welfare and other sectoral information of households. It covers a wide range of topics, dealing with the dynamics of poverty (consumption, cash and non-cash income, savings, assets, food security, health and education, vulnerability and social protection). Although the IHS5 household questionnaire covers a wide variety of topics in detail it intentionally excludes in-depth information on topics covered in other surveys that are part of the NSO’s statistical plan (such as maternal and child health issues covered at length in the Malawi Demographic and Health Survey).

AGRICULTURE QUESTIONNAIRE All IHS5 households that are identified as being involved in agricultural or livestock activities were administered the agriculture questionnaire, which is primarily modelled after the IHS3 counterpart. The modules are expanding on the agricultural content of the IHS4, IHS3, IHS2, AISS, and other regional agricultural surveys, while remaining consistent with the NACAL topical coverage and methodology. The development of the agriculture questionnaire was done with input from the aforementioned stakeholders who provided input on the household questionnaire as well as outside researchers involved in research and policy discussions pertaining to the Malawian agriculture. The agriculture questionnaire allows, among other things, for extensive agricultural productivity analysis through the diligent estimation of land areas, both owned and cultivated, labor and non-labor input use and expenditures, and production figures for main crops, and livestock. Although one of the major foci of the agriculture data collection effort was to produce smallholder production estimates for major crops, it is also possible to disaggregate the data by gender and main geographical regions. The IHS5 cross-sectional households supply information on the last completed rainy season (2017/2018 or 2018/2019) and the last completed dry season (2018 or 2019) depending on the timing of their interview.

FISHERIES QUESTIONNAIRE The design of the IHS5 fishery questionnaire is identical to the questionnaire designed for IHS3. The IHS3 fisheries questionnaire was informed by the design and piloting of a fishery questionnaire by the World Fish Center (WFC), which was supported by the LSMS-ISA project for the purpose of assembling a fishery questionnaire that could be integrated into multi-topic household-surveys. The WFC piloted the draft instrument in November 2009 in the Lower Shire region, and the NSO team considered the revised draft in designing the IHS5 fishery questionnaire.

COMMUNITY QUESTIONNAIRE The content of the IHS5 Community Questionnaire follows the content of the IHS3 & IHS4 Community Questionnaires. A “community” is defined as the village or urban location surrounding the enumeration area selected for inclusion in the sample and which most residents recognize as being their community. The IHS5 community questionnaire was administered to each community associated with the cross-sectional EAs interviewed. Identical to the IHS3 and IHS4 approach, to a group of several knowledgeable residents such as the village headman, the headmaster of the local school, the agricultural field assistant, religious leaders, local merchants, health workers and long-term knowledgeable residents. The instrument gathers information on a range of community characteristics, including religious and ethnic background, physical infrastructure, access to public services, economic activities, communal resource management, organization and governance, investment projects, and local retail price information for essential goods and services.

MARKET QUESTIONNAIRE The Market Survey consisted of one questionnaire which is composed of four modules. Module A: Market Identification, Module B: Seasonal Main Crops, Module C: Permanents Crops, and Module D: Food Consumption.

Cleaning operations

DATA ENTRY PLATFORM To ensure data quality and timely availability of data, the IHS5 was implemented using the World Bank’s Survey Solutions CAPI software. To carry out IHS5, 1 laptop computer and a wireless internet router were assigned to each team supervisor, and each enumerator had an 8–inch GPS-enabled Lenovo tablet computer. The use of Survey Solutions allowed for the real-time availability of data as the completed data was completed, approved by the Supervisor and synced to the Headquarters server as frequently as possible. While administering the first module of the questionnaire the enumerator(s) also used their tablets to record the GPS coordinates of the dwelling units. In Survey Solutions, Headquarters can then see the location of the dwellings plotted on a map of Malawi to better enable supervision from afar – checking both the number of interviews performed and the fact that the sample households lie within EA boundaries. Geo-referenced household locations from that tablet complemented the GPS measurements taken by the Garmin eTrex 30 handheld devices and these were linked with publically available geospatial databases to enable the inclusion of a number of geospatial variables - extensive measures of distance (i.e. distance to the nearest market), climatology, soil and terrain, and other environmental factors - in the analysis.

The range and consistency checks built into the application was informed by the LSMS-ISA experience in previous IHS waves. Prior programming of the data entry application allowed for a wide variety of range and consistency checks to be conducted and reported and potential issues investigated and corrected before closing the assigned enumeration area. Headquarters (NSO management) assigned work to supervisors based on their regions of coverage. Supervisors then made assignments to the enumerators linked to their Supervisor account. The work assignments and syncing of completed interviews took place through a Wi-Fi connection to the IHS5 server. Because the data was available in real time it was monitored closely throughout the entire data collection period and upon receipt of the data at headquarters, data was exported to STATA for other consistency checks, data cleaning, and analysis.

DATA MANAGEMENT The IHS5 Survey Solutions CAPI based data entry application was designed to stream-line the data collection process from the field. IHS5 Interviews were collected in “sample” mode (assignments generated from headquarters) as opposed to “census” mode (new interviews created by interviewers from a template) for the NSO to have more control over the sample.

The range and consistency checks built into the application was informed by the LSMS-ISA experience in previous IHS waves. Prior programming of the data

Abstract

Survey based Harmonized Indicators (SHIP) files are harmonized data files from household surveys that are conducted by countries in Africa. To ensure the quality and transparency of the data, it is critical to document the procedures of compiling consumption aggregation and other indicators so that the results can be duplicated with ease. This process enables consistency and continuity that make temporal and cross-country comparisons consistent and more reliable.

Four harmonized data files are prepared for each survey to generate a set of harmonized variables that have the same variable names. Invariably, in each survey, questions are asked in a slightly different way, which poses challenges on consistent definition of harmonized variables. The harmonized household survey data present the best available variables with harmonized definitions, but not identical variables. The four harmonized data files are

a) Individual level file (Labor force indicators in a separate file): This file has information on basic characteristics of individuals such as age and sex, literacy, education, health, anthropometry and child survival. b) Labor force file: This file has information on labor force including employment/unemployment, earnings, sectors of employment, etc. c) Household level file: This file has information on household expenditure, household head characteristics (age and sex, level of education, employment), housing amenities, assets, and access to infrastructure and services. d) Household Expenditure file: This file has consumption/expenditure aggregates by consumption groups according to Purpose (COICOP) of Household Consumption of the UN.

Geographic coverage

National

Analysis unit

• Individual level for datasets with suffix _I and _L
• Household level for datasets with suffix _H and _E

Universe

The survey covered all de jure household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Frame The list of households obtained from the 2001/2 Ethiopian Agricultural Sample Enumeration (EASE) was used as a frame to select EAs from the rural part of the country. On the other hand, the list consisting of households by EA, which was obtained from the 2004 Ethiopian Urban Economic Establishment Census, (EUEEC), was used as a frame in order to select sample enumeration areas for the urban HICE survey. A fresh list of households from each urban and rural EA was prepared at the beginning of the survey period. This list was, thus, used as a frame in order to select households from sample EAs.

Sample Design For the purpose of the survey the country was divided into three broad categories. That is; rural, major urban center and other urban center categories.

Category I: Rural: - This category consists of the rural areas of eight regional states and two administrative councils (Addis Ababa and Dire Dawa) of the country, except Gambella region. Each region was considered to be a domain (Reporting Level) for which major findings of the survey are reported. This category comprises 10 reporting levels. A stratified two-stage cluster sample design was used to select samples in which the primary sampling units (PSUs) were EAs. Twelve households per sample EA were selected as a Second Stage Sampling Unit (SSU) to which the survey questionnaire were administered.

Category II:- Major urban centers:- In this category all regional capitals (except Gambella region) and four additional urban centers having higher population sizes as compared to other urban centers were included. Each urban center in this category was considered as a reporting level. However, each sub-city of Addis Ababa was considered to be a domain (reporting levels). Since there is a high variation in the standards of living of the residents of these urban centers (that may have a significant impact on the final results of the survey), each urban center was further stratified into the following three sub-strata. Sub-stratum 1:- Households having a relatively high standards of living Sub-stratum 2:- Households having a relatively medium standards of living and Sub-stratum 3:- Households having a relatively low standards of living. The category has a total of 14 reporting levels. A stratified two-stage cluster sample design was also adopted in this instance. The primary sampling units were EAs of each urban center. Allocation of sample EAs of a reporting level among the above mentioned strata were accomplished in proportion to the number of EAs each stratum consists of. Sixteen households from each sample EA were inally selected as a Secondary Sampling Unit (SSU).

Category III: - Other urban centers: - Urban centers in the country other than those under category II were grouped into this category. Excluding Gambella region a domain of "other urban centers" is formed for each region. Consequently, 7 reporting levels were formed in this category. Harari, Addis Ababa and Dire Dawa do not have urban centers other than that grouped in category II. Hence, no domain was formed for these regions under this category. Unlike the above two categories a stratified three-stage cluster sample design was adopted to select samples from this category. The primary sampling units were urban centers and the second stage sampling units were EAs. Sixteen households from each EA were lastly selected at the third stage and the survey questionnaires administered for all of them.

Mode of data collection

Face-to-face [f2f]

We examined the spatial distribution of Per- and Polyfluoroalkyl Substances (PFAS) in the US drinking water and explored the relationship between PFAS contamination, public water systems (PWS) characteristics, and socioeconomic attributes of the affected communities. Using data from the EPA’s third Unregulated Contaminant Rule, the Census Bureau, and the Bureau of Labor Statistics, we identified spatial contamination hot spots and found that PFAS contamination was correlated with PWSs size, non-surface raw water intake sources, population, and housing density. We also found that non-white communities had less PFAS in drinking water. Lastly, we observed that PFAS contamination varied depending on regional industrial composition. The results showed that drinking water PFAS contamination was an externality of not only some industrial activities but also household consumption.

Abstract

The Pakistan Integrated Household Survey (PIHS) was conducted jointly by the Federal Bureau of Statistics (FBS), Government of Pakistan, and the World Bank. The survey was part of the Living Standards Measurement Study (LSMS) household surveys that have been conducted in a number of developing countries with the assistance of the World Bank. The purpose of these surveys is to provide policy makers and researchers with individual, household, and community level data needed to analyze the impact of policy initiatives on living standards of households.

The Pakistan Integrated Household Survey was carried out in 1991. This nationwide survey gathered individual and household level data using a multi-purpose household questionnaire. Topics covered included housing conditions, education, health, employment characteristics, selfemployment activities, consumption, migration, fertility, credit and savings, and household energy consumption. Community level and price data were also collected during the course of the survey.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Communities

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the PIHS was drawn using a multi-stage stratified sampling procedure from the Master Sample Frame developed by FBS based on the 1981 Population Census.

SAMPLE FRAME:

This sample frame covers all four provinces (Punjab, Sindh, NWFP, and Balochistan) and both urban and rural areas. Excluded, however, are the Federally Administered Tribal Areas, military restricted areas, the districts of Kohistan, Chitral and Malakand and protected areas of NWFP. According to the FBS, the population of the excluded areas amounts to about 4 percent of the total population of Pakistan. Also excluded are households which depend entirely on charity for their living.

The sample frame consists of three main domains: (a) the self-representing cities; (b) other urban areas; and (c) rural areas. These domains are further split up into a number of smaller strata based on the system used by the Government to divide the country into administrative units. The four provinces of Pakistan mentioned above are divided into 20 divisions altogether; each of these divisions in turn is then further split into several districts. The system used to divide the sample frame into the three domains and the various strata is as follows: (a) Self-representing cities: All cities with a population of 500,000 or more are classified as self-representing cities. These include Karachi, Lahore, Gujranwala, Faisalabad, Rawalpindi, Multan, Hyderabad and Peshawar. In addition to these cities, Islamabad and Quetta are also included in this group as a result of being the national and provincial capitals respectively. Each self-representing city is considered as a separate stratum, and is further sub-stratified into low, medium, and high income groups on the basis of information collected at the time of demarcation or updating of the urban area sample frame. (b) Other urban areas: All settlements with a population of 5,000 or more at the time of the 1981 Population Census are included in this group (excluding the self-representing cities mentioned above). Urban areas in each division of the four provinces are considered to be separate strata. (c) Rural areas: Villages and communities with population less than 5,000 (at the time of the Census) are classified as rural areas. Settlements within each district of the country are considered to be separate strata with the exception of Balochistan province where, as a result of the relatively sparse population of the districts, each division instead is taken to be a stratum.

Main strata of the Master Sample frame

Domain / Punjab / Sindh / NWFP / Balochistan / PAKISTAN Self-representing cities / 6 / 2 / 1 / 1 / 10 Other urban areas / 8 / 3 / 5 / 4 / 20 Rural areas / 30 / 14 / 10 / 4 / 58 Total 44 / 19 / 16 / 9 / 88

As the above table shows, the sample frame consists of 88 strata altogether. Households in each stratum of the sample frame are exclusively and exhaustively divided into PSUs. In urban areas, each city or town is divided into a number of enumeration blocks with welldefined boundaries and maps. Each enumeration block consists of about 200-250 households, and is taken to be a separate PSU. The list of enumeration blocks is updated every five years or so, with the list used for the PIHS having been modified on the basis of the Census of Establishments conducted in 1988. In rural areas, demarcation of PSUs has been done on the basis of the list of villages/mouzas/dehs published by the Population Census Organization based on the 1981 Census. Each of these villages/mouzas/dehs is taken to be a separate PSU. Altogether, the sample frame consists of approximately 18,000 urban and 43,000 rural PSUs.

SAMPLE SELECTION:

The PIHS sample comprised 4,800 households drawn from 300 PSUs throughout the country. Sample PSUs were divided equally between urban and rural areas, with at least two PSUs selected from each of the strata. Selection of PSUs from within each stratum was carried out using the probability proportional to estimated size method. In urban areas, estimates of the size of PSUs were based on the household count as found during the 1988 Census of Establishments. In rural areas, these estimates were based on the population count during the 1981 Census.

Once sample PSUs had been identified, a listing of all households residing in the PSU was made in all those PSUs where such a listing exercise had not been undertaken recently. Using systematic sampling with a random start, a short-list of 24 households was prepared for each PSU. Sixteen households from this list were selected to be interviewed from the PSU; every third household on the list was designated as a replacement household to be interviewed only if it was not possible to interview either of the two households immediately preceding it on the list.

As a result of replacing households that could not be interviewed because of non-responses, temporary absence, and other such reasons, the actual number of households interviewed during the survey - 4,794 - was very close to the planned sample size of 4,800 households. Moreover, following a pre-determined procedure for replacing households had the added advantage of minimizing any biases that may otherwise have arisen had field teams been allowed more discretion in choosing substitute households.

SAMPLE DESIGN EFFECTS:

The three-stage stratified sampling procedure outlined above has several advantages from the point of view of survey organization and implementation. Using this procedure ensures that all regions or strata deemed important are represented in the sample drawn for the survey. Picking clusters of households or PSUs in the various strata rather than directly drawing households randomly from throughout the country greatly reduces travel time and cost. Finally, selecting a fixed number of households in each PSU makes it easier to distribute the workload evenly amongst field teams. However, in using this procedure to select the sample for the survey, two important matters need to be given consideration: (a) sampling weights or raising factors have to be first calculated to get national estimates from the survey data; and (b) the standard errors for estimates obtained from the data need to be adjusted to take account for the use of this procedure.

Mode of data collection

Face-to-face [f2f]

Research instrument

The PIHS used three questionnaires: a household questionnaire, a community questionnaire, and a price questionnaire.

HOUSEHOLD QUESTIONNAIRE:

The PIHS questionnaire comprised 17 sections, each of which covered a separate aspect of household activity. The various sections of the household questionnaire were as follows: 1. HOUSEHOLD INFORMATION 2. HOUSING 3. EDUCATION 4. HEALTH 5. WAGE EMPLOYMENT 6. FAMILY LABOR 7. ENERGY 8. MIGRATION 9. FARMING AND LIVESTOCK 10. NON-FARM ENTERPRISE ACTIVITIES 11. NON-FOOD EXPENDITURES AND INVENTORY OF DURABLE GOODS 12. FOOD EXPENSES AND HOME PRODUCTION 13. MARRIAGE AND MATERNITY HISTORY 14. ANTHROPOMETRICS 15. CREDIT AND SAVINGS 16. TRANSFERS AND REMITTANCES 17. OTHER INCOME

The household questionnaire was designed to be administered in two visits to each sample household. Apart from avoiding the problem of interviewing household members in one long stretch, scheduling two visits also allowed the teams to improve the quality of the data collected.

During the first visit to the household (Round 1), the enumerators covered sections 1 to 8, and fixed a date with the designated respondents of the household for the second visit. During the second visit (Round 2), which was normally held two weeks after the first visit, the enumerators covered the remaining portion of the questionnaire and resolved any omissions or inconsistencies that were detected during data entry of information from the first part of the survey.

Since many of the sections of the questionnaire pertained specifically to female members of the household, female interviewers were included in conducting the survey. The household questionnaire was split into two parts (Male and Female). Sections such as SECTION 3: EDUCATION, which solicited information on all individual members of the household (male as well as female) were included in both parts of the questionnaire. Other sections such as SECTION 2: HOUSING and SECTION 12: FOOD EXPENSES AND HOME PRODUCTION , which collected data at the aggregate household level, were included in either the male questionnaire or the female questionnaire, depending upon which member of the household was more likely

Graph and download economic data for Personal consumption expenditures: Admissions to specified spectator amusements: Live entertainment, excluding sports (DLIGRC1A027NBEA) from 1959 to 2024 about amusements, sport, admissions, entertainment, PCE, consumption expenditures, consumption, personal, GDP, and USA.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Jupiter Inlet Colony, FL, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 3,232, while the mean income for the highest quintile (20% of households with the highest income) is 980,787. This indicates that the top earners earn 303 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 2,094,626, which is 213.57% higher compared to the highest quintile, and 64808.97% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

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 Jupiter Inlet Colony median household income. You can refer the same here

This dataset was collected from 10-year model simulations using a modified version of the agent-based model 'inSTREAM'. The data comparing the abundance, body length and microplastics consumption of a simulated population of rainbow trout was collected annually on the census day (i.e., September 23) from years 3-10. The data was analyzed using mixed models, to see how microplastics pollution (concentrations of 0%, 1% and 3% of drift food) affects this fictional population of rainbow trout, specifically the abundance and body length of fish with all different combinations of two personality traits (dominance, boldness/shyness) of three life stages (fry, juvenile, adult).

Abstract

Susenas is a survey designed to collect socio-demographic data in large area. The data collected were related to the fields of education, health / nutrition, housing / environmental, socio-cultural activities, consumption and household income, trips, and public opinion about the welfare of household. In 1992, Susenas data collection system has been updated, the information used to develop indicators of welfare (Welfare) contained in the module (information collected once every three years) drawn into the core (group information is collected each year).

In 2005 Susenas implement the module consumption / expenditure and household income. The data collected is the basic ingredient for calculating estimates of poverty based on consumption module Susenas three years (the latest data of 2002). However, given the poverty alleviation is a priority program of the current government; the Central bureau of statistic (BPS) attempted to provide data-poor national estimates on an annual basis. With the collecting data consumption / expenditure details every year it will be estimated annual number of poor people.

To meet the data needs of the government about the development of poor people every year, Panel Susenas collected the consumption and expenditure module data with the total sample of 10,000 households in 2003. The number of samples is only able to estimate the national poverty, while the demands of the availability data of poverty rate up to provincial level is increasing.

Geographic coverage

National coverage, representative to the district level

Analysis unit

Household Members (Individual) and Household

Universe

Implementation Susenas 2005 includes 278,352 households spread across. all geografls regions of Indonesian , with details of 68 288 households sample core-module and 210 064 households core sample (without modules), and 10,640 households sample of Susenas panel that is part of households sample core-module.

Kind of data

Sample survey data [ssd]

Sampling procedure

1. Susenas core

The design of sampling Susenas 2005 and Supas 2005 was conducted in an integrated manner in order to estimates some of the same variable can be done in an integrated manner. Sampling procedures Susenas 2005 for a county / city are as follows:

• Phase 1, from sample frame census block are to be selected census block nh (h = 1, for urban; h = 2, for rural) by probability proportional to size (pps) method whereas size is the number of households from P4B census result (April 2004).

• Phase 2, from nh selected nh census block for Susenas 2005, further referred to as census blocks Susenas. Household listing is conducted to all selected census blocks/sub-blocks.

• Phase 3, selecting m = 16 households in each census block selected systematically, for census block payloads of more than 150 households, it is necessary to selection of a sub-block census in PPS systematically with the size of the number of households P4B enumeration (April 2004).

1. Consumption Module / Household Expenditure and Household income with module sample sizes of consumption / expenditure and household income are designed for presentation at the provincial level. The module sample is section of subsample of selected sample for data estimate in district / city level (Census Block NSES), urban and rural areas. The subsample selected by Systematic Linear Sampling from selected census blocks in each district / city for urban and rural areas. Further census blocks selected (subsample) is the census block core-module, due beside enumerated with questionnaire module, also enumerated the core questionnaire. In other words, the census blocks that will be used to estimates at the provincial level (census block core-module) selected by systematic linear sampling from a list of selected census blocks in each district / city (census block core). Core-module census blocks is not selected 2004 Susenas is core census block.

2. Panel Module consumption /expenditure and household income in addition to the design of the sample selection core-module consumption / expenditure and household income above, in Susenas 2005 was also designed to perform the method of survey panel module consumption / expenditure and household income, where sample census block and panel sample of households (repetition) Susenas 2005 (the implementation in February 2005).

For the presentation of the poverty rate at the national level (February 2005), namely the implementation of the survey panel Susenas 2005 (February 2005), the number of census blocks will be selected from a sample of census blocks Susenas core-module (Susenas 2005, June 2005). The sample selection will be conducted in systematic sampling.

Mode of data collection

Face-to-face