About this dataset

Context

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

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Drummond, while the Census reported a median income of $30,063 for all male workers aged 15 years and older, data for females in the same category was unavailable due to an insufficient number of sample observations.

Given the absence of income data for females from the Census Bureau, conducting a thorough analysis of gender-based pay disparity in the town of Drummond was not possible.

- Full-time workers, aged 15 years and older: In Drummond, for full-time, year-round workers aged 15 years and older, while the Census reported a median income of $57,917 for males, while data for females was unavailable due to an insufficient number of sample observations.

As there was no available median income data for females, conducting a comprehensive assessment of gender-based pay disparity in Drummond was not feasible.

Content

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

Gender classifications include:

• Male
• Female

Employment type classifications include:

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

About this dataset

Context

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

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Bainville, while the Census reported a median income of $30,824 for all female workers aged 15 years and older, data for males in the same category was unavailable due to an insufficient number of sample observations.

Because income data for males was not available from the Census Bureau, conducting a comprehensive analysis of gender-based pay disparity in the town of Bainville was not possible.

- Full-time workers, aged 15 years and older: In Bainville, for full-time, year-round workers aged 15 years and older, the Census reported a median income of $31,534 for females, while data for males was unavailable due to an insufficient number of sample observations.

As there was no available median income data for males, conducting a comprehensive assessment of gender-based pay disparity in Bainville was not feasible.

Content

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

Gender classifications include:

• Male
• Female

Employment type classifications include:

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

About this dataset

Context

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

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Melstone, while the Census reported a median income of $24,750 for all female workers aged 15 years and older, data for males in the same category was unavailable due to an insufficient number of sample observations.

Because income data for males was not available from the Census Bureau, conducting a comprehensive analysis of gender-based pay disparity in the town of Melstone was not possible.

- Full-time workers, aged 15 years and older: In Melstone, for full-time, year-round workers aged 15 years and older, the Census reported a median income of $33,750 for females, while data for males was unavailable due to an insufficient number of sample observations.

As there was no available median income data for males, conducting a comprehensive assessment of gender-based pay disparity in Melstone was not feasible.

Content

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

Gender classifications include:

• Male
• Female

Employment type classifications include:

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

About this dataset

Context

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

Key observations: Insights from 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Mount Moriah, while the Census reported a median income of $25,478 for all male workers aged 15 years and older, data for females in the same category was unavailable due to an insufficient number of sample observations.

Given the absence of income data for females from the Census Bureau, conducting a thorough analysis of gender-based pay disparity in the town of Mount Moriah was not possible.

- Full-time workers, aged 15 years and older: In Mount Moriah, for full-time, year-round workers aged 15 years and older, while the Census reported a median income of $41,548 for males, while data for females was unavailable due to an insufficient number of sample observations.

As there was no available median income data for females, conducting a comprehensive assessment of gender-based pay disparity in Mount Moriah was not feasible.

https://i.neilsberg.com/ch/mount-moriah-mo-income-by-gender.jpeg" alt="Mount Moriah, MO gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

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

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2022
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Mount Moriah median household income by gender. You can refer the same here

About this dataset

Context

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

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Mountain Lake township, while the Census reported a median income of $39,375 for all male workers aged 15 years and older, data for females in the same category was unavailable due to an insufficient number of sample observations.

Given the absence of income data for females from the Census Bureau, conducting a thorough analysis of gender-based pay disparity in the township of Mountain Lake township was not possible.

- Full-time workers, aged 15 years and older: In Mountain Lake township, for full-time, year-round workers aged 15 years and older, the Census reported a median income of $40,000 for females, while data for males was unavailable due to an insufficient number of sample observations.

As there was no available median income data for males, conducting a comprehensive assessment of gender-based pay disparity in Mountain Lake township was not feasible.

Content

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

Gender classifications include:

• Male
• Female

Employment type classifications include:

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

Abstract

The objectives of the 1991 Population Census were:

a. to develop a set of benchmark data for different purposes, b. to provide data for small administrative areas of the country on population, housing and household facilities, c. to provide reliable frames for different types of sample surveys, d. to provide sex disaggregated data of the population and other variables related to households, demographic, social and economic conditions of the country, and e. to provide detailed information on women, children, and the aged

Geographic coverage

National coverage Regional Urban and rural areas District Village Development Committee/Municipality

Analysis unit

private household, individual

Universe

The census covered all household members (usual residents) in private households.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The 1991 census collected data based on short form for the complete enumeration of the benchmark information and the long form for the sample enumeration of other socio-economic and demographic information. The long form was administered for population dwellings in about 20 percent of the total housing units. Based on these, estimates were generated at the district level with reliable degree of precision.

The sampling scheme of the 1991 Population Census for the long form is summarized below.

1. The sampling covered the private households only. For the institutional population, Schedule-1 only was administered.

2. For the sampling, 75 administrative districts formed the main strata and VDC's and municipalities within the district formed the domains.

3. There were around 36,000 wards in the country at the time of the census. For the purpose of the census enumeration some of the large wards were further divided into sub-wards. These wards and sub-wards formed the EA's for sampling. The total number of EAs thus formed was around 40,000.

4. Sampling was carried out in each EA; housing unit being the sampling unit.

5. The list of housing units and households served as the sampling frame for the EA. The housing units were selected by systematic sampling method. The sampling interval taken was 8.

6. The list of selected housing units was made available to the enumerator for the enumeration. All households and persons found in the selected units were enumerated.

7. The ratio method was used in making estimates for the sample.

8. Tabulation groups were created separately for tabulation of persons and those for households. The main control variables for the majority of tabulations for persons were two variables: age sex. Tabulation groups for household tabulations were formed in a different manner: taking households as a tabulation group in the domain.

9. To implement the ratio estimation, first weights were calculated. The weights for sample data were computed by dividing the 100 percent counts for the same tabulation groups in the domain by sample counts for the same tabulation groups in the domain. To avoid inconsistency due to rounding, the figures were converted to whole numbers.

This detailed sampling procedure is documented in the Technical Documents.

Mode of data collection

Face-to-face [f2f]

Research instrument

In 1991, both household listing and actual census field operation took place simultaneously, unlike in 1981 where household listing preceded total enumeration. Nevertheless, the number of items included in the household list was as comprehensive as before.

Two types of schedules were prepared. Form 1 for complete enumeration and Form 2 for sample enumeration. Both schedules contained questions on household as well as individuals.

FORM 1: (COMPLETE Enumeration) A. Household Information (Question relating to Household), House type & ownership, Agriculture land holding, Livestock/poultry raised for agriculture purpose, Female ownership on house, land and Livestock/Cattle, Small scale economic activities other than agriculture, Absentees from Household (HH), reasons & country of destination. B. Individual Information (Question relating to Individuals), Name, sex, age, relationship to the Household head, Caste/ethnicity, religion, Language spoken and citizen.

FORM 2: (SAMPLE Enumeration) A. Household Information (Question relating to Household), Household facilities (Radio, TV, etc.), Deaths in last 12 months in the Household. B. Individual Information (Question relating to Individuals), Place of birth, Migration, Literacy, Educational attainment, Marital status, Age at first Marriage, Children born, Economic/Non-economic activities, occupation/Industry, employment status, Reasons for not being active, Living arrangement of children below 16 years of age.

Questionnaire contents and the pre-testing of census enumeration procedure were conducted in four districts, Bajura from mountain, Mahottari from terai and Dhankuta as well as Kathmandu from the hilly region.

Cleaning operations

The completed schedules returned to the Headquarters from the field, were carefully edited which was followed by coding operations.

Data editing took place at a number of stages throughout the processing, including:

a) Office editing and coding b) During data entry c) Structure checking and completeness d) Secondary editing e) Structural checking of SPSS data files

Response rate

At the time of census there were 3,914 VDC's and 58 municipalities. VDC's contained a total of 35,226 wards while urban areas contained 806 wards. Thus total numbers of wards in the country were 36,032.

For form 2, there is no available data for response rate.

Data appraisal

The quality of the 1991 census data is evaluated in two steps. The first step deals with the evaluation of the quality of the census data on selected aspects such as age reporting, fertility, mortality, etc. The second step examines the extent of coverage in the census enumeration.

A Post Enumeration Survey (PES) was designed to evaluate the 1991 census data quality with respect to completeness of coverage. It was scheduled to be conducted in August 1991 but the actual revisiting of sampled areas for reconciliation of the differences was completed by April 1992.

Abstract

Over the past decade, Albania has been undergoing a transition toward a market economy and a more open society. It has faced severe internal and external challenges, such as lack of basic infrastructure, rapid collapse of output and inflation rise after the collapse of the communist regime, turmoil during the 1997 pyramid crisis, and social and economic instability because of the 1999 Kosovo crisis. Despite these shocks, Albanian economy has recovered from a very low income level through a sustained growth during the past few years, even though it remains one of the poorest countries in Europe, with GDP per capita at around 1,300$. Based on the Living Standard Measurement Study (LSMS) 2002 survey data (wave 1, henceforth), for the first time in Albania INSTAT has computed an absolute poverty line on a nationally representative poverty survey at household level. Based on this welfare measure, one quarter (25.4 percent) of the Albanian population, or close to 790,000 individuals, were defined as poor in 2002. The distribution of poverty is also disproportionately rural, as 68 percent of the poor are in rural areas, against 32 percent in urban areas (as compared to a total urban population well over 40 percent). These estimates are quite sensitive to the choice of the poverty line, as there are a large number of households clustered around the poverty line. Income related poverty is compounded by the severe lack of access to basic infrastructure, education and health services, clean water, etc., and the ability of the Government to address these issues is complicated by high levels of internal and external migration that are not well understood. The availability of a nationally representative survey is crucial as the paucity of household-level information has been a constraining factor in the design, implementation and evaluation of economic and social programs in Albania. Two recent surveys carried out by the Albanian Institute of Statistics (INSTAT) -the 1998 Living Conditions Survey (LCS) and the 2000 Household Budget Survey (HBS) - drew attention, once again, to the need for accurately measuring household welfare according to well-accepted standards, and for monitoring these trends on a regular basis. This target is well-achieved by drawing information over time on a panel component of LSMS 2002 households, namely the Albanian Panel Survey (APS), conducted in 2003 and 2004. An increasing attention to the policies aimed at achieving the Millennium Development Goals (MDGs) is paid by the National Parliament of Albania, recently witnessed by the resolution approved in July 2003, where it pushes “… the total commitment of both state structures and civil society to achieve the MDGs in Albania by 2015”. The path towards a sustained growth is constantly monitored through the National Reports on Progress toward Achieving the MDGs, which involves a close collaboration of the UN with the national institutions, led by the National Strategy for Social and Economic Development (NSSED) Department of the Ministry of Finance. Also, in the process leading to the Poverty Reduction Strategy Paper (PRSP; also known in Albania as Growth and Poverty Reduction Strategy, GPRS), the Government of Albania reinforced its commitment to strengthening its own capacity to collect and analyse on a regular basis information it needs to inform policy-makers. In its first phase (2001-2006), this monitoring system will include the following data collection instruments:

(i) Population and Housing Census (ii) Living Standards Measurement Surveys every 3 years (iii) Annual panel surveys.

The focus during this first phase of the monitoring system is on a periodic LSMS (in 2002 and 2005), followed by panel surveys on a sub-sample of LSMS households (APS 2003, 2004 and 2006), drawing heavily on the 2001 census information. Here our target is to illustrate the main characteristics of the APS 2004 data with reference to the LSMS. The survey work was undertaken by the Living Standards Unit of INSTAT, with the technical assistance of the World Bank.

Geographic coverage

National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLE DESIGN

Panel sample, with LSMS 2002 and 2004 The APS 2004 collects information on 1,797 valid observations at household level and 7,476 at individual level. The sample of the second and third waves of the panel (APS) has been selected from the LSMS 2002 in order to be representative of Albanian households and individuals at national level. The LSMS 2002 differs from the APS 2003 and 2004 in that the former is designed to be representative at regional level (Mountain, Central, Coastal and Tirana) as well as for urban and rural domains, while the latter are for last domains only (urban and rural) LSMS 2002 sample design The LSMS is based on a probability sample of housing units (HUs) within the 16 strata of the sampling frame. It is divided in three regions: Coastal, Central, and Mountain Area. In addition, urban areas of Tirana are also considered as a separate region/stratum. The three regions are further stratified in major cities (the most important cities in the region), other urban (other cities in the region), and rural. The city of Tirana and its suburbs have been implicitly stratified to improve the efficiency of the sample design. Each stratum has been divided in Enumeration Area (EA), in accordance with the 2001 Census data, and each Primary Sampling Unit (PSU) selected with probabilities proportional to the number of occupied HUs in the EA. Every EA includes occupied and unoccupied HUs. Occupied rather than total units have been used because of the large number of empty dwellings registered in the Census data. The Housing Unit, defined as the space occupied by one household, is taken as sampling unit because is more permanent and easier to identify compared to the household. 10 EAs for each major city (75 for Tirana) and 65 EAs for each rural region -with the exception of the mountain area which is over-represented (75 EAs)- are selected. 8 households, plus 4 eventual substitutes, have been systematically selected in each EAs. As the LSMS consists of 450 EAs, total sample size is 3,600 households.

(b) STRATIFICATION

The panel component selected from the LSMS is designed to provide a nationally representative sample of households and individuals within Albania. It consists of roughly half of the households in the 2002 LSMS, interviewed both in 2003 and 2004. Contrarily to what done for the LSMS, no over-sampling in the Mountain Area has been performed for the panel survey. The sample is designed to minimize the variability in households' selection probabilities. It ensures national representativeness by matching the sample distribution across strata with the population distribution drawn from 2001 Census data. In Table 3 the ex-ante sampling scheme of the 2003-2004 APS is shown. Compared to the LSMS design, statistical precision has improved. Under equal stratum population variances hypothesis, sample design effects are expected to be around 1.02, compared to the 1.28 of the LSMS sample. Moreover, further precision is obtained by keeping all 450 EAs of LSMS in the panel sample, thus reducing the eventual bias due to clustering because of new design. Finally, the panel survey has a number of peculiar features that should be considered when using the data. The sample is designed to focus on individuals, who have been also traced when moving from the original household to a new one. This possibility represents the only way a household can enter the panel sample if it has not been already interviewed in the wave 1 (or in wave 2 for the APS 2004). If an original survey member (OSM) moves to a new household, his/her old and new household -and their members- are both included in the panel sample. Though a moved OSM will be present in the roster of both sampled households, he/she is a valid member only in the new one. In the old household he/she is considered as "moved away", hence not a valid member. This might generate some confusion. Three modalities exist to classify an individual in the third wave. First, when he/she is an OSM, that is a respondent interviewed both in wave 1 and 2. Second, when he is a re-joiner from 2002, that is an OSM not interviewed in 2003 (i.e. because temporarily absent) who returns in 2004. Third, when he/she is a new member, whenever he/she is a newborn of an original household, a member joined by an OSM or a person who co-resides with an original survey household. So, the APS is an indefinite life panel study, without replacement by drawing new sample units. From wave 2, only individuals aged 15 years and over are considered valid members, hence eligible for the interview. Individuals moved out of Albania are not accounted as valid for this survey year, though they are still eligible for future waves.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

A first data cleaning took place in Albania and implemented by INSTAT in collaboration with ISER and Government of Albania consultants. The cleaning process has involved following activities: 1. defining data checking routines and writing the syntax code of the cleaning programs; 2. generating lists of outliers and inconsistencies for each module to be checked against paper questionnaires; During the first few days, data cleaning operators have been working on the Export Procedure of the Data Entry Program to check if data export succeeded and to finalize the English version of the dictionaries and error messages. Some changes were made to the Export Procedure due to a problem on the “Agriculturea2” file conversion and to the

Overview

The Associated Press is sharing data from the COVID Impact Survey, which provides statistics about physical health, mental health, economic security and social dynamics related to the coronavirus pandemic in the United States.

Conducted by NORC at the University of Chicago for the Data Foundation, the probability-based survey provides estimates for the United States as a whole, as well as in 10 states (California, Colorado, Florida, Louisiana, Minnesota, Missouri, Montana, New York, Oregon and Texas) and eight metropolitan areas (Atlanta, Baltimore, Birmingham, Chicago, Cleveland, Columbus, Phoenix and Pittsburgh).

The survey is designed to allow for an ongoing gauge of public perception, health and economic status to see what is shifting during the pandemic. When multiple sets of data are available, it will allow for the tracking of how issues ranging from COVID-19 symptoms to economic status change over time.

The survey is focused on three core areas of research:

• Physical Health: Symptoms related to COVID-19, relevant existing conditions and health insurance coverage.
• Economic and Financial Health: Employment, food security, and government cash assistance.
• Social and Mental Health: Communication with friends and family, anxiety and volunteerism. (Questions based on those used on the U.S. Census Bureau’s Current Population Survey.) ## Using this Data - IMPORTANT This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use our queries linked below or statistical software such as R or SPSS to weight the data.

Queries

If you'd like to create a table to see how people nationally or in your state or city feel about a topic in the survey, use the survey questionnaire and codebook to match a question (the variable label) to a variable name. For instance, "How often have you felt lonely in the past 7 days?" is variable "soc5c".

Nationally: Go to this query and enter soc5c as the variable. Hit the blue Run Query button in the upper right hand corner.

Local or State: To find figures for that response in a specific state, go to this query and type in a state name and soc5c as the variable, and then hit the blue Run Query button in the upper right hand corner.

The resulting sentence you could write out of these queries is: "People in some states are less likely to report loneliness than others. For example, 66% of Louisianans report feeling lonely on none of the last seven days, compared with 52% of Californians. Nationally, 60% of people said they hadn't felt lonely."

Margin of Error

The margin of error for the national and regional surveys is found in the attached methods statement. You will need the margin of error to determine if the comparisons are statistically significant. If the difference is:

• At least twice the margin of error, you can report there is a clear difference.
• At least as large as the margin of error, you can report there is a slight or apparent difference.
• Less than or equal to the margin of error, you can report that the respondents are divided or there is no difference. ## A Note on Timing Survey results will generally be posted under embargo on Tuesday evenings. The data is available for release at 1 p.m. ET Thursdays.

About the Data

The survey data will be provided under embargo in both comma-delimited and statistical formats.

Each set of survey data will be numbered and have the date the embargo lifts in front of it in the format of: 01_April_30_covid_impact_survey. The survey has been organized by the Data Foundation, a non-profit non-partisan think tank, and is sponsored by the Federal Reserve Bank of Minneapolis and the Packard Foundation. It is conducted by NORC at the University of Chicago, a non-partisan research organization. (NORC is not an abbreviation, it part of the organization's formal name.)

Data for the national estimates are collected using the AmeriSpeak Panel, NORC’s probability-based panel designed to be representative of the U.S. household population. Interviews are conducted with adults age 18 and over representing the 50 states and the District of Columbia. Panel members are randomly drawn from AmeriSpeak with a target of achieving 2,000 interviews in each survey. Invited panel members may complete the survey online or by telephone with an NORC telephone interviewer.

Once all the study data have been made final, an iterative raking process is used to adjust for any survey nonresponse as well as any noncoverage or under and oversampling resulting from the study specific sample design. Raking variables include age, gender, census division, race/ethnicity, education, and county groupings based on county level counts of the number of COVID-19 deaths. Demographic weighting variables were obtained from the 2020 Current Population Survey. The count of COVID-19 deaths by county was obtained from USA Facts. The weighted data reflect the U.S. population of adults age 18 and over.

Data for the regional estimates are collected using a multi-mode address-based (ABS) approach that allows residents of each area to complete the interview via web or with an NORC telephone interviewer. All sampled households are mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Interviews are conducted with adults age 18 and over with a target of achieving 400 interviews in each region in each survey.Additional details on the survey methodology and the survey questionnaire are attached below or can be found at https://www.covid-impact.org.

Attribution

Results should be credited to the COVID Impact Survey, conducted by NORC at the University of Chicago for the Data Foundation.

AP Data Distributions

​To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

As of 2024, South Africa's population increased, counting approximately 63 million inhabitants. Of these, roughly 27.5 million were aged 0-24, while 654,000 people were 80 years or older. Gauteng and Cape Town are the most populated South Africa’s yearly population growth has been fluctuating since 2013, with the growth rate dropping below the world average in 2024. The majority of people lived in the borders of Gauteng, the smallest of the nine provinces in terms of land area. The number of people residing there amounted to 16.6 million in 2023. Although the Western Cape was the third-largest province, the city of Cape Town had the highest number of inhabitants in the country, at 3.4 million. An underemployed younger population South Africa has a large population under 14, who will be looking for job opportunities in the future. However, the country's labor market has had difficulty integrating these youngsters. Specifically, as of the fourth quarter of 2024, the unemployment rate reached close to 60 percent and 384 percent among people aged 15-24 and 25–34 years, respectively. In the same period, some 27 percent of the individuals between 15 and 24 years were economically active, while the labor force participation rate was higher among people aged 25 to 34, at 74.3 percent.

In 2022, there were 67.85 million married men and 68.45 million married women living in the United States. This is compared to 3.7 million widowed men and 11.48 million widowed women.

Marriage in the United States

Nevada had the highest marriage rate in the United States in 2021, followed by Hawaii and Montana. This can be attributed to marriage accessibility in the state. Las Vegas weddings are known for being quick, easy, and inexpensive chapel weddings. In comparison to the cheap weddings available in Las Vegas, the average expenditure for a wedding in the United States was the highest in New Jersey, clocking in at 51,000 U.S. dollars.

Same-sex marriage

The number of Americans who think that same-sex marriage should be recognized by law has more than doubled since 1996, while the number of Americans who think it should not be valid has decreased. It was not until June 26, 2015 that the United States Supreme Court legalized same-sex marriage in all 50 states. Before then, it was up to the states to decide if they allowed same-sex marriage. States in the Southeast are the most opposed to same-sex marriage, whereas the strongest support comes from Northern coastal states.

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.