When analyzing the ratio of homelessness to state population, New York, Vermont, and Oregon had the highest rates in 2023. However, Washington, D.C. had an estimated ** homeless individuals per 10,000 people, which was significantly higher than any of the 50 states. Homeless people by race The U.S. Department of Housing and Urban Development performs homeless counts at the end of January each year, which includes people in both sheltered and unsheltered locations. The estimated number of homeless people increased to ******* in 2023 – the highest level since 2007. However, the true figure is likely to be much higher, as some individuals prefer to stay with family or friends - making it challenging to count the actual number of homeless people living in the country. In 2023, nearly half of the people experiencing homelessness were white, while the number of Black homeless people exceeded *******. How many veterans are homeless in America? The  number of homeless veterans in the United States has halved since 2010. The state of California, which is currently suffering a homeless crisis, accounted for the highest number of homeless veterans in 2022. There are many causes of homelessness among veterans of the U.S. military, including post-traumatic stress disorder (PTSD), substance abuse problems, and a lack of affordable housing.

The graph displays the top 15 states by an estimated number of homeless people in the United States for the year 2025. The x-axis represents U.S. states, while the y-axis shows the number of homeless individuals in each state. California has the highest homeless population with 187,084 individuals, followed by New York with 158,019, while Hawaii places last in this dataset with 11,637. This bar graph highlights significant differences across states, with some states like California and New York showing notably higher counts compared to others, indicating regional disparities in homelessness levels across the country.

In 2023, there were an estimated ******* white homeless people in the United States, the most out of any ethnicity. In comparison, there were around ******* Black or African American homeless people in the U.S. How homelessness is counted The actual number of homeless individuals in the U.S. is difficult to measure. The Department of Housing and Urban Development uses point-in-time estimates, where employees and volunteers count both sheltered and unsheltered homeless people during the last 10 days of January. However, it is very likely that the actual number of homeless individuals is much higher than the estimates, which makes it difficult to say just how many homeless there are in the United States. Unsheltered homeless in the United States California is well-known in the U.S. for having a high homeless population, and Los Angeles, San Francisco, and San Diego all have high proportions of unsheltered homeless people. While in many states, the Department of Housing and Urban Development says that there are more sheltered homeless people than unsheltered, this estimate is most likely in relation to the method of estimation.

In 2023, there were about ******* homeless people estimated to be living in the United States, the highest number of homeless people recorded within the provided time period. In comparison, the second-highest number of homeless people living in the U.S. within this time period was in 2007, at *******. How is homelessness calculated? Calculating homelessness is complicated for several different reasons. For one, it is challenging to determine how many people are homeless as there is no direct definition for homelessness. Additionally, it is difficult to try and find every single homeless person that exists. Sometimes they cannot be reached, leaving people unaccounted for. In the United States, the Department of Housing and Urban Development calculates the homeless population by counting the number of people on the streets and the number of people in homeless shelters on one night each year. According to this count, Los Angeles City and New York City are the cities with the most homeless people in the United States. Homelessness in the United States Between 2022 and 2023, New Hampshire saw the highest increase in the number of homeless people. However, California was the state with the highest number of homeless people, followed by New York and Florida. The vast amount of homelessness in California is a result of multiple factors, one of them being the extreme high cost of living, as well as opposition to mandatory mental health counseling and drug addiction. However, the District of Columbia had the highest estimated rate of homelessness per 10,000 people in 2023. This was followed by New York, Vermont, and Oregon.

This map shows the percent of population who are veterans. This pattern is shown by states, counties, and tracts. The data is from the most current American Community Survey (ACS) data from the U.S. Census Bureau. Veterans are men and women who have served (even for a short time), but are not currently serving, on active duty in the U.S. Army, Navy, Air Force, Marine Corps, or the Coast Guard, or who served in the U.S. Merchant Marine during World War II. People who served in the National Guard or Reserves are classified as veterans only if they were ever called or ordered to active duty.The pop-up highlights the breakdown of veterans by gender.Zoom to any area in the country to see a local or regional pattern, or use one of the bookmarks to see distinct patterns of poverty through the US. Data is available for the 50 states plus Washington D.C. and Puerto Rico.The data comes from this ArcGIS Living Atlas of the World layer, which is part of a wider collection of layers that contain the most up-to-date ACS data from the Census. The layers are updated annually when the ACS releases their most current 5-year estimates. Visit the layer for more information about the data source, vintage, and download date for the data.

The number of homeless people in Portugal continuously increased from 2018 to 2022. In the latter year, there were ****** homeless individuals in the country. Unsheltered individuals outnumbered the unhoused by more than a thousand homeless persons.

INTRODUCTION: As California’s homeless population continues to grow at an alarming rate, large metropolitan regions like the San Francisco Bay Area face unique challenges in coordinating efforts to track and improve homelessness. As an interconnected region of nine counties with diverse community needs, identifying homeless population trends across San Francisco Bay Area counties can help direct efforts more effectively throughout the region, and inform initiatives to improve homelessness at the city, county, and metropolitan level. OBJECTIVES: The primary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness across San Francisco Bay Area counties between the years 2018-2022. The secondary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness among different age groups in each of the nine San Francisco Bay Area counties between the years 2018-2022. METHODS: Two datasets were used to conduct research. The first dataset (Dataset 1) contains Point-in-Time (PIT) homeless counts published by the U.S. Department of Housing and Urban Development. Dataset 1 was cleaned using Microsoft Excel and uploaded to Tableau Desktop Public Edition 2022.4.1 as a CSV file. The second dataset (Dataset 2) was published by Data SF and contains shapefiles of geographic boundaries of San Francisco Bay Area counties. Both datasets were joined in Tableau Desktop Public Edition 2022.4 and all data analysis was conducted using Tableau visualizations in the form of bar charts, highlight tables, and maps. RESULTS: Alameda, San Francisco, and Santa Clara counties consistently reported the highest annual count of people experiencing homelessness across all 5 years between 2018-2022. Alameda, Napa, and San Mateo counties showed the largest increase in homelessness between 2018 and 2022. Alameda County showed a significant increase in homeless individuals under the age of 18. CONCLUSIONS: Results from this research reveal both stark and fluctuating differences in homeless counts among San Francisco Bay Area Counties over time, suggesting that a regional approach that focuses on collaboration across counties and coordination of services could prove beneficial for improving homelessness throughout the region. Results suggest that more immediate efforts to improve homelessness should focus on the counties of Alameda, San Francisco, Santa Clara, and San Mateo. Changes in homelessness during the COVID-19 pandemic years of 2020-2022 point to an urgent need to support Contra Costa County.

In 2011, about ** percent of the total population in India was homeless. Urban areas witnessed more homelessness in comparison to the rural areas of the country. Homelessness is a growing issue in India that leads to various other problems like violence and drug addiction among others.

This dataset provides information on individuals experiencing sheltered or unsheltered homelessness in the Austin/Travis County Continuum of Care (CoC) on a single night in January when the Point in Time (PIT) Count occurs. "Sheltered" homelessness refers to individuals residing in emergency shelter, safe haven, or transitional housing project types. Unsheltered homelessness refers to individuals with a primary nighttime residence that is a public or private place not designed for or ordinarily used as a regular sleeping accommodation for human beings, including a car, park, abandoned building, bus or train station, airport, or camping ground on the night designated for the count. This measure overlaps, but is different from, the annual count of sheltered homelessness in HMIS (SD23 Measure EOA.E.1b).

Data Source: The data for this measure was reported to the City of Austin by the Ending Community Homelessness Coalition (ECHO). Each year, ECHO, as the homeless Continuum of Care Lead Agency (CoC Lead), aggregates and reports community wide data (including this measure) to the Department of Housing and Urban Development (HUD). This data is referred to as System Performance Measures as they are designed to examine how well a community is responding to homelessness at a system level.

View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/hjiv-t2tm

Last Updated December 2020 with data for 2020 PIT Count.

Homelessness has been a consistent problem for the city of Louisville for decades now. Despite efforts from the city government and local nonprofits, homelessness increased 139% last year alone. The Covid-19 pandemic significantly worsened the crisis, but the risk factors that contribute to homelessness are still endemic across the city: lack of affordable housing, lack of access to physical and mental healthcare, stagnant wages, etc. Homelessness has negative effects on mortality, personal health of the homeless, and public health in general (also see here, no paywall). When I recently attended a strategy meeting for the Louisville Downtown Partnership, one of the top issues voted by attendees was the rise of homelessness downtown. This could come from genuine care or that many Americans associate homeless people with crime. Everyone benefits when the issues that cause homelessness are addressed effectively, and a vital part of that is knowing what areas are most at-risk.The app above was made to map certain risk factors across Jefferson County. The risk factors include percent of households with 50%+ income going to rent, persons without health insurance coverage, percent of households at or below the poverty line, percent of households using public assistance, percent of persons reporting extensive physical and mental distress, unemployment, along with other economic and health-based factors. This doesn’t include every possible factor that could cause homelessness, but many that have strong effects. A dummy census tract was made with all the worst possible outcomes for risk factors, which was then used to rank the similarity of every census tract in Jefferson County; the lower the rank, the more at-risk the tract is. The app allows you to click through every tract in the county and see the ten most at-risk ones.The most at-risk places tend to line up with the west end and areas of the city that were historically redlined. These areas also saw mass amounts of “urban renewal” in the 60s and 70s. They also tend to line up with areas of the city that face the highest eviction rates (thanks to Ryan Massey for pointing this out).

OverviewThese are the Homeless Counts for 2020 as provided by the Los Angeles Homeless Services Authority (LAHSA), and the cities of Glendale, Pasadena, and Long Beach. The majority of this data comes from LAHSA using tract-level counts; the cities of Glendale, Pasadena, and Long Beach did not have tract-level counts available. The purpose of this layer is to depict homeless density at a community scale. Please read the note from LAHSA below regarding the tract level counts. In this layer LAHSA's tract-level population count was rounded to the nearest whole number, and density was determined per square mile of each community. It should be noted that not all of the sub-populations captured from LAHSA (eg. people living in vans, unaccompanied minors, etc.) are not captured here; only sheltered, unsheltered, and total population. Data generated on 12/2/20.Countywide Statistical AreasLos Angeles County's 'Countywide Statistical Areas' layer was used to classify the city / community names. Since this is tract-level data there are several times where a tract is in more than one city/community. Whatever the majority of the coverage of a tract is, that is the community that got coded. The boundaries of these communities follow aggregated tract boundaries and will therefore often deviate from the 'Countywide Statistical Area' boundaries.Note from LAHSALAHSA does not recommend aggregating census tract-level data to calculate numbers for other geographic levels. Due to rounding, the census tract-level data may not add up to the total for Los Angeles City Council District, Supervisorial District, Service Planning Area, or the Los Angeles Continuum of Care.The Los Angeles Continuum of Care does not include the Cities of Long Beach, Glendale, and Pasadena and will not equal the countywide Homeless Count Total.Street Count Data include persons found outside, including persons found living in cars, vans, campers/RVs, tents, and makeshift shelters. A conversion factor list can be found at https://www.lahsa.org/homeless-count/Please visit https://www.lahsa.org/homeless-count/home to view and download data.Last updated 07/16/2020

According to U.S. Department of Housing and Urban Development's definition, homelessness includes individuals and families who lack a fixed, regular, and adequate nighttime residence. A homeless count provides a "snapshot in time" to quantify the size of the homeless population at a specific point during the year. Regardless of how successful outreach efforts are, an undercount of people experiencing homelessness is possible. Counts includes persons experiencing unsheltered and sheltered homelessness. Greater Los Angeles Homeless Count occurred in the nights of February 22, 23 and 24, 2022. Glendale's count occurred in the morning and evening of February 25, 2022. Long Beach's count occurred in the early morning of February 24, 2022. Pasadena's count occurred in the evening of February 22, 2022 and morning of February 23, 2022. Data not available for Los Angeles City neighborhoods and unincorporated Los Angeles County; LAHSA does not recommend aggregating census tract-level data to calculate numbers for other geographic levels.Housing affordability is a major concern for many Los Angeles County residents. Housing burden can increase the risk for homelessness. Individuals experiencing homelessness experience disproportionately higher rates of certain health conditions, such as tuberculosis, HIV infection, alcohol and drug abuse, and mental illness. Barriers to accessing care and limited access to resources contribute greatly to these observed disparities.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

The Directorate-General Employment of the European Commission commissioned a survey that examines public opinion about poverty and exclusion in the European Union. Between the 14th of February and the 18th of March 2007, TNS Opinion & Social, a consortium formed by TNS and EOS Gallup Europe interviewed 26,466 EU citizens aged 15 and over living in the 27 European Union Member States and 1,000 residents of Croatia. This report studies the following issues related to poverty and exclusion covered by the survey. ♦ First of all, we focus on the perceived existence of poverty in the European Union: to what extent are Europeans themselves affected by poverty and to what extent do they see poverty in the area in which they live? In this chapter we furthermore look at attitudes towards poverty: is it an inherited or acquired condition, what causes poverty and why do people live in need? ♦ The second part of the report focuses on one of the most extreme forms of exclusion, homelessness: why do people become homeless, what is the perceived risk of becoming homeless oneself and what do Europeans do to help homeless people? ♦ In the final part we examine what Europeans regard necessary in order to have a decent standard of living with regards to financial means, housing needs, ownership of durable goods, basic necessities and social integration. We also look specifically at people’s views concerning the requirements and the needs of children to live and develop well. We end the report with an examination of how people’s attitudes towards poverty relate to what they consider necessary for a decent standard of living. #####The results by volumes are distributed as follows: * Volume A: Countries * Volume AA: Groups of countries * Volume A' (AP): Trends * Volume AA' (AAP): Trends of groups of countries * Volume B: EU/socio-demographics * Volume C: Country/socio-demographics ---- Researchers may also contact GESIS - Leibniz Institute for the Social Sciences: http://www.gesis.org/en/home/

Created for the 2023-2025 State of Black Los Angeles County (SBLA) interactive report. To learn more about this effort, please visit the report home page at https://ceo.lacounty.gov/ardi/sbla/. For more information about the purpose of this data, please contact CEO-ARDI. For more information about the configuration of this data, please contact ISD-Enterprise GIS. Table Name Indicator Name Universe Timeframe Source Race Notes Source URL

homeownership_pct % Homeownership Occupied Housing Units 2016-2020 American Community Survey - Table B25003B-I Race alone; White is Non-Hispanic White https://data.census.gov/cedsci/table?g=0500000US06037&tid=ACSDT5Y2020.B25003

renters_pct % Renters Occupied Housing Units 2016-2020 American Community Survey - Table B25003B-I Race alone; White is Non-Hispanic White https://data.census.gov/cedsci/table?g=0500000US06037&tid=ACSDT5Y2020.B25003

mean_home_value Mean Home Value Households 2021 Public Use Microdata Sample (PUMS) All races are Non-Hispanic LA County eGIS-Demography

accepted_mortgage_pct Accepted Mortgate Rate Mortgage Applications 2021 Home Mortgage Disclosure Act HMDA categories - https://files.consumerfinance.gov/f/documents/cfpb_reportable-hmda-data_regulatory-and-reporting-overview-reference-chart-2019.pdf https://ffiec.cfpb.gov/data-browser/data/2021

rent_burden_pct Rent Burdened Renter Households 2019 California Housing Partnership All races are Non-Hispanic https://chpc.net/housingneeds/?view=37.405074,-119.26758,5&county=California,Los+Angeles&group=housingneed&chart=shortfall|current,cost-burden|current,cost-burden-re|current,homelessness,historical-rents,vacancy,asking-rents|2022,budgets|2021,funding|current,state-funding,lihtc|2010:2021:historical,rhna-progress,multifamily-production

rent_burden_severe_pct Severely Rent Burdened Renter Households 2019 California Housing Partnership All races are Non-Hispanic https://chpc.net/housingneeds/?view=37.405074,-119.26758,5&county=California,Los+Angeles&group=housingneed&chart=shortfall|current,cost-burden|current,cost-burden-re|current,homelessness,historical-rents,vacancy,asking-rents|2022,budgets|2021,funding|current,state-funding,lihtc|2010:2021:historical,rhna-progress,multifamily-production

eviction_per_100_hh Eviction Rate Renter Households 2014-2017 The Eviction Lab at Princeton University

https://data-downloads.evictionlab.org/#data-for-analysis/

homeless_count Homeless Count Population excluding Long Beach, Glendale, and Pasadena 2022 LAHSA

https://www.lahsa.org/documents?id=6545-2022-greater-los-angeles-homeless-count-deck

homeless_homeless_pct % Homeless Population Population excluding Long Beach, Glendale, and Pasadena 2022 LAHSA

https://www.lahsa.org/documents?id=6545-2022-greater-los-angeles-homeless-count-deck

homeless_county_pct % County Population Population excluding Long Beach, Glendale, and Pasadena 2022 LAHSA

https://www.lahsa.org/documents?id=6545-2022-greater-los-angeles-homeless-count-deck

unable_pay_mortgage_rent% Delayed or Were Unable to Pay Mortgage or Rent in the past 2 Years Households 2018 LAC Health Survey https://www.publichealth.lacounty.gov/ha/HA_DATA_TRENDS.htm

homeless_ever% Who Reported Ever Being Homeless or Not Having Their Own Place to Live or Sleep in the past Five Years Adults 2018 LAC Health Survey https://www.publichealth.lacounty.gov/ha/HA_DATA_TRENDS.htm

This dataset contains locations and information about health care service delivery points for people experiencing homelessness in King County. Service delivery locations may refer to a specific facility address or a more general service area. The dataset was developed by the King County Department of Public Health’s Community Health Services Division to support public access to where services are being delivered. More information about the Health Care for the Homeless Network can be found at www.kingcounty.gov/en/dept/dph/health-safety/health-centers-programs-services/health-services-for-the-homeless.

This dataset provides information on individuals experiencing sheltered homelessness in the Austin/Travis County Continuum of Care (CoC) in a given fiscal year. "Sheltered" homelessness refers to individuals residing in emergency shelter, safe haven, or transitional housing project types. This measure overlaps, but is different from, the Point in Time (PIT) Count (SD23 Measure EOA.E.1a), which is a snapshot of both sheltered and unsheltered homelessness on one night in January.

Data Source: The data for this measure was reported to the City of Austin by the Ending Community Homelessness Coalition (ECHO). Each year, ECHO, as the homeless Continuum of Care Lead Agency (CoC Lead), aggregates and reports community wide data (including this measure) to the Department of Housing and Urban Development (HUD). This data is referred to as System Performance Measures as they are designed to examine how well a community is responding to homelessness at a system level.

View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/2ejn-hrh2

Background: Homeless people are a socially excluded group whose health reflects exposures to intersecting social determinants of health. The aim of this study was to describe and compare the demographic composition, certain social determinants of health, and self-reported health among homeless people in Stockholm, Sweden, in 2006 and 2018.Methods: Analysis of data from face-to-face interviews with homeless people in Stockholm 2006 (n = 155) and 2018 (n = 148), based on a public health survey questionnaire adapted to the group, including the EQ-5D-3L instrument. The chi-squared test was employed to test for statistical significance between groups and the independent t-test for comparison of mean scores and values. Ordinary Least Squares (OLS) regression, with Robust Standard Errors (RSE) was performed on merged 2006 and 2018 data with mean observed EQ VAS score as outcome variable.Results: In 2018 more homeless people originated from countries outside Europe, had temporary social assistance than long-term social insurance, compared to in 2006. In 2018 more respondents reported lack of social support, exposure to violence, and refrained from seeking health care because of economic reasons. Daily smoking, binge drinking, and use of narcotic drugs was lower 2018 than 2006. In 2018 a higher proportion reported problems in the EQ-5D-3L dimensions, the mean TTO index value and the VAS index value was significantly lower than in 2006. In the regression analysis of merged data there was no significant difference between the years.Conclusions: Homeless people are an extremely disadvantaged group, have high rates of illness and disease and report poor health in all EQ-5D-3L dimensions. The EQ VAS score among the homeless people in 2018 is comparable to the score among persons aged 95–104 years in the general Swedish population 2017. The EQ-5D-3L instrument was easily administered to this group, its use allows comparison with larger population groups. Efforts are needed regarding housing, but also intensified collaboration by public authorities with responsibilities for homeless people's health and social welfare. Further studies should evaluate the impact of such efforts by health and social care services on the health and well-being of homeless people.

Note: This Dataset is updated nightly and contains all downloadable Medical Examiner-Coroner records, January 1, 2018 to current, related to deaths that occurred in the County of Santa Clara under the Medical Examiner-Coroner’s jurisdiction and those deaths reportable to the Medical Examiner-Coroner (non-jurisdictional cases/NJA) but in which the office did not assume jurisdiction.

The Santa Clara County Medical Examiner- Coroner’s Office determines cause and manner of death for those deaths that fall under the jurisdiction of the Medical Examiner-Coroner, as defined by California Government code 27491.

The Medical Examiner-Coroner will not be responsible for data verification, interpretation or misinformation once data has been downloaded and manipulated from the dashboard.

Refer to the following document to know more of which deaths are reportable: https://medicalexaminer.sccgov.org/sites/g/files/exjcpb986/files/Reportable%20Death%20Chart%202018.pdf.

This poll, fielded January 6-8, 1992, is part of a continuing series of monthly surveys that solicit public opinion on the presidency and on a range of other political and social issues. Respondents were asked to give their opinions of President George H.W. Bush and his handling of the presidency, foreign policy, and the economy. Respondents were asked to list the most important problem facing the country, which candidate they would vote for if the election for president were being held that day, and whether they were likely to vote in the Democratic or Republican presidential primary or caucus. Several questions asked for respondents' opinions of the Democratic and Republican presidential nominees, which candidates they would like to see win the nominations for president, and what issues they would like to see the candidates emphasize in their campaigns. Opinions were collected on how much George H.W. Bush cared about the general public, whether he distributed his time properly between foreign policy problems and problems at home, and whether his visits to countries in Asia would increase the number of jobs in the United States. A series of questions addressed the causes of homelessness, whether it was something the government could do a lot about, and whether respondents had personally seen a lot of homeless people in their community. Additional questions asked respondents to rate the condition of the national economy, whether they would be better off financially if George H.W. Bush was re-elected president, whether recession was something a president could do a lot about, and whether George H.W. Bush was healthy enough to be an effective president for a second term. Demographic variables include sex, race, age, household income, education level, political party affiliation, political philosophy, and voter registration status.

Abstract

Statistical information on all aspects of the population is vital for the design, implementation, monitoring and evaluation of economic and social development plan and policy issues. Labor force survey is one of the most important sources of data for assessing the role of the population of the country in the economic and social development process. It is useful to indicate the extent of available and unutilized human resources that must be absorbed by the national economy to ensure full employment and economic wellbeing of the population. Statistics on the labor force further present the economic activity status and its relationship to other social and economic characteristics of the population. Seasonal and other variations as well as changes over time in the size, distribution, and characteristics of employed and unemployed population can be monitored using up-to-date information from labor force surveys. It serves as an input for assessing the achievements of the Millennium Development Goals (MDGs). Furthermore, labor force data is also useful as a springboard for monitoring and evaluation of the five years growth and transformation plan of the country.

Geographic coverage

The 2012 Urban Employment and Unemployment Survey (UEUS) covered all urban parts of the country except three zones of Afar, Six zones of Somali, where the residents are pastoralists.

Analysis unit

• Households
• Individuals

Universe

This survey follows household approach and covers households residing in conventional households and thus, population residing in the collective quarters such as universities/colleges, hotel/hostel, monasteries, and homeless population etc., were not covered by this survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

The list of households obtained from the 2007 population and housing census was used to select EAs. A fresh list of households from each EA was prepared at the beginning of the survey period. The list was then used as a frame to select 30 households from sample EAs.

The country was divided into two broad categories - major urban centers and other urban center categories.

Category I: In this category all regional capitals and five other major urban centers that have a high population size as compared to others were included. Each urban center in this category was considered as a reporting level. This category has a total of 16 reporting levels. To select the sample, a stratified two-stage cluster sample design was implemented. The primary sampling units were EAs of each reporting level.

Category II: Urban centers other than those under category I were grouped into this category. 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.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire was organized into seven sections. Section 1 - Area identification of the selected household Section 2 - Particulars of household members Section 3 - Economic activity status during the last seven days Section 4 - Unemployment rate and characteristics of unemployed persons Section 5 - Economic activity status the population during the last six months Section 6 - Employment in the informal sector of Employment Section 7 - Economic activity of children aged 5-17 years

A structured questionnaire was used to solicit the required data in the survey. The draft questionnaire was tested by undertaking a pretest in selected kebeles (lower administrative unit) in Addis Ababa. Based on the pretest, the content, logical flow, layout and presentation of the questionnaire was amended. The questionnaire used in the field for data collection was prepared in Amharic language. Most questions have pre coded answers and column numbers were assigned for each question.

Cleaning operations

The filled-in questionnaires that were retrieved from the field were first subjected to manual editing and coding. During the fieldwork the field supervisors and the heads of branch statistical offices have checked the filled-in questionnaires and carried out some editing. However, the major editing and coding operation was carried out at the head office. All the edited questionnaires were again fully verified and checked for consistency before they were submitted to the data entry by the subject matter experts.

Using the computer edit specifications prepared earlier for this purpose, the entered data were checked for consistencies and then computer editing, or data cleaning was made by referring back to the filled-in questionnaire. This is an important part of data processing operation in attaining the required level of data quality. Consistency checks and re-checks were also made based on frequency and tabulation results. This was done by senior programmers using CSPro software in collaboration with the senior subject experts from Manpower Statistics Team of the CSA.

Response rate

Response rate was 99.68%.