This statistic shows the median household income in the United States from 1990 to 2023 in 2023 U.S. dollars. The median household income was 80,610 U.S. dollars in 2023, an increase from the previous year. Household incomeThe median household income depicts the income of households, including the income of the householder and all other individuals aged 15 years or over living in the household. Income includes wages and salaries, unemployment insurance, disability payments, child support payments received, regular rental receipts, as well as any personal business, investment, or other kinds of income received routinely. The median household income in the United States varies from state to state. In 2020, the median household income was 86,725 U.S. dollars in Massachusetts, while the median household income in Mississippi was approximately 44,966 U.S. dollars at that time. Household income is also used to determine the poverty line in the United States. In 2021, about 11.6 percent of the U.S. population was living in poverty. The child poverty rate, which represents people under the age of 18 living in poverty, has been growing steadily over the first decade since the turn of the century, from 16.2 percent of the children living below the poverty line in year 2000 to 22 percent in 2010. In 2021, it had lowered to 15.3 percent. The state with the widest gap between the rich and the poor was New York, with a Gini coefficient score of 0.51 in 2019. The Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality and a score of one indicates a society where one person would have all the money and all other people have nothing.

Of the most populous cities in the U.S., San Jose, California had the highest annual income requirement at ******* U.S. dollars annually for homeowners to have an affordable and comfortable life in 2024. This can be compared to Houston, Texas, where homeowners needed an annual income of ****** U.S. dollars in 2024.

This table contains data on the living wage and the percent of families with incomes below the living wage for California, its counties, regions and cities/towns. Living wage is the wage needed to cover basic family expenses (basic needs budget) plus all relevant taxes; it does not include publicly provided income or housing assistance. The percent of families below the living wage was calculated using data from the Living Wage Calculator and the U.S. Census Bureau, American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. The living wage is the wage or annual income that covers the cost of the bare necessities of life for a worker and his/her family. These necessities include housing, transportation, food, childcare, health care, and payment of taxes. Low income populations and non-white race/ethnic have disproportionately lower wages, poorer housing, and higher levels of food insecurity. More information about the data table and a data dictionary can be found in the About/Attachments section.

In 2024, households in California needed an hourly wage of over 47 U.S. dollars to afford the rent of a two-bedroom apartment. Massachusetts had the second-least affordable two-bedroom apartments, as a household would have to earn at least around 45 U.S. dollars per hour in order to afford rent payments. These figures are considerably higher than the average minimum wage in place in many states. There was no state in which a minimum wage worker could afford rent for the average two-bedroom apartment, if they only worked 40 hours a week. Where are the least affordable counties and metros? The least affordable rents were predominately in Californian counties and metropolitan areas in 2024. District of Columbia has one of the highest minimum wages in the country, which stood at 17 U.S. dollars per hour as of January 2024. Thus, the affordability of two-bedroom apartments highlights how disproportionately high housing costs are in the state.

This statistic depicts the share of household income needed to be saved each year to live comfortably in retirement in the United States as of January 2017. It was found that ** percent of the interviewed workers believed that it was enough to save somewhere between ** and ** percent of the annual household income in order to live comfortably in retirement as of 2017.

This statistic shows the hourly wage needed to earn a living wage in the United States in 2014, by state. In 2014, a living wage in Alabama was ***** U.S. dollars per hour.

Graph and download economic data for Median Household Income in California (MEHOINUSCAA646N) from 1984 to 2023 about CA, households, median, income, and USA.

The graph presents the median monthly salary in the United States from 2000 to 2024. The x-axis represents the years, labeled from '00 to '24*, while the y-axis shows the salary amounts in U.S. dollars per month. Throughout this twenty-four-year period, the median monthly salary consistently increased from $2,500 in 2000 to $5,036 in 2024. The data highlights a steady upward trend, with annual salaries rising each year without any declines. Notably, the salary grew by approximately $200 each year from 2000 to 2019, surged to $4,269 in 2020, and continued to climb each subsequent year, reaching $5,000 by 2024. This consistent growth reflects economic advancements and potential increases in workforce compensation over the decade. The information is depicted in a line graph format, effectively illustrating the continuous rise in median monthly salaries across the specified years.

In 2023, the real median household income in the state of Alabama was 60,660 U.S. dollars. The state with the highest median household income was Massachusetts, which was 106,500 U.S. dollars in 2023. The average median household income in the United States was at 80,610 U.S. dollars.

This layer shows median household income by race and by age of householder. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Median income and income source is based on income in past 12 months of survey. This layer is symbolized to show median household income. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B19013B, B19013C, B19013D, B19013E, B19013F, B19013G, B19013H, B19013I, B19049, B19053Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This feature layer consists of the contiguous United States and District of Columbia, with Alaska and Hawaii. It comprises state minimum wage data for 2018, as well as historical data since 1968, and future data where available. The data was compiled from the U.S. Department of Labor, the National Conference of State Legislatures, and the U.C. Berkeley Labor Center, with living wage data from MIT's Living Wage Calculator. This layer uses the composite geographies layout to position Alaska and Hawaii adjacent to the contiguous United States.Attributes:

Field Name Unit Description

PeakMW Nominal dollar value Highest minimum wage value planned to be reached in future years (2019-2022)

PeakYR Year The year that the highest minimum wage value is planned to be reached (2019-2022)

DiffPeak2018 Nominal dollar value (difference) The difference between the peak minimum wage and the 2018 minimum wage (PeakMW - DiffPeak2018)

MW2018 Nominal dollar value 2018 state minimum wage

Increase2017 Nominal dollar value (difference) The difference between the 2018 minimum wage and the 2017 minimum wage (MW2018 - MW2017)

Increase2000 2017 dollar value (difference) The difference between the 2018 minimum wage and the 2000 minimum wage (MW2018-MW2000)

Effective2018 Nominal dollar value The minimum wage effective in 2018. For states with minimum wages below the federal minimum wage of $7.25, or for states that have no minimum wage requirement, the federal minimum wage applies.

LV2016 Nominal dollar value 2016 living wage for a single adult at the state level

DiffMWLV Nominal dollar value (difference) The difference between the 2018 minimum wage and the 2016 living wage

CurrentMW Category The type of minimum wage policy in place at the state level

PoliciesMW Text When a state has an indexed minimum wage, the type of policy is described here

Update2018 Category Yes = the state implemented an update to its minimum wage in 2018; No = no policy update in 2018

MW2017 Nominal dollar value 2017 minimum wage

MW2016 2017 dollar value 2016 minimum wage, adjusted for inflation to 2017 dollars

MW2015 2017 dollar value 2015 minimum wage, adjusted for inflation to 2017 dollars

MW2014 2017 dollar value 2014 minimum wage, adjusted for inflation to 2017 dollars

MW2013 2017 dollar value 2013 minimum wage, adjusted for inflation to 2017 dollars

MW2012 2017 dollar value 2012 minimum wage, adjusted for inflation to 2017 dollars

MW2011 2017 dollar value 2011 minimum wage, adjusted for inflation to 2017 dollars

MW2010 2017 dollar value 2010 minimum wage, adjusted for inflation to 2017 dollars

MW2009 2017 dollar value 2009 minimum wage, adjusted for inflation to 2017 dollars

MW2008 2017 dollar value 2008 minimum wage, adjusted for inflation to 2017 dollars

MW2007 2017 dollar value 2007 minimum wage, adjusted for inflation to 2017 dollars

MW2006 2017 dollar value 2006 minimum wage, adjusted for inflation to 2017 dollars

MW2005 2017 dollar value 2005 minimum wage, adjusted for inflation to 2017 dollars

MW2004 2017 dollar value 2004 minimum wage, adjusted for inflation to 2017 dollars

MW2003 2017 dollar value 2003 minimum wage, adjusted for inflation to 2017 dollars

MW2002 2017 dollar value 2002 minimum wage, adjusted for inflation to 2017 dollars

MW2001 2017 dollar value 2001 minimum wage, adjusted for inflation to 2017 dollars

MW2000 2017 dollar value 2000 minimum wage, adjusted for inflation to 2017 dollars

MW1998 2017 dollar value 1998 minimum wage, adjusted for inflation to 2017 dollars

MW1997 2017 dollar value 1997 minimum wage, adjusted for inflation to 2017 dollars

MW1996 2017 dollar value 1996 minimum wage, adjusted for inflation to 2017 dollars

MW1994 2017 dollar value 1994 minimum wage, adjusted for inflation to 2017 dollars

MW1992 2017 dollar value 1992 minimum wage, adjusted for inflation to 2017 dollars

MW1991 2017 dollar value 1991 minimum wage, adjusted for inflation to 2017 dollars

MW1988 2017 dollar value 1988 minimum wage, adjusted for inflation to 2017 dollars

MW1981 2017 dollar value 1981 minimum wage, adjusted for inflation to 2017 dollars

MW1980 2017 dollar value 1980 minimum wage, adjusted for inflation to 2017 dollars

MW1979 2017 dollar value 1979 minimum wage, adjusted for inflation to 2017 dollars

MW1976 2017 dollar value 1976 minimum wage, adjusted for inflation to 2017 dollars

MW1972 2017 dollar value 1972 minimum wage, adjusted for inflation to 2017 dollars

MW1970 2017 dollar value 1970 minimum wage, adjusted for inflation to 2017 dollars

MW1968 2017 dollar value 1968 minimum wage, adjusted for inflation to 2017 dollars

Abstract

The purpose of the HIES survey is to obtain information on the income, consumption pattern, incidence of poverty, and saving propensities for different groups of people in FSM. This information will be used to guide policy makers in framing socio-economic developmental policies and in initiating financial measures for improving economic conditions of the people. The 2005 FSM HIES asked income of all persons 15 years and over. It referred to income received during the calendar year 2004, and includes both cash and in-kind income. The survey has five primary objectives, namely to:

1) Rebase the FSM Consumer Price Index (CPI); 2) Provide data on the distribution of income and expenditures throughout the FSM; 3) Provide data for national accounts, particularly regarding income from home production activities and the consumption of goods and services derived form home production activities; 4) Provide nutritional information and food consumption patterns for the FSM families; and 5) Provide data for hardship study.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Expenditure items

Universe

The survey universe covered all persons living in their place of usual residence at the time of the survey. Income data were collected from persons aged 15 years and over while expenditure data were obtained from all household members at a household level. Persons living in institutions, such as school dormitories, hospital wards, hostels, prisons, as well as those whose usual residence were somewhere else were excluded from the survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2005 FSM Household Income and Expenditure Survey (HIES) used a sampling frame based on updated information on Enumeration Districts (ED) and household listing from the 2000 FSM Census. Based on this sampling frame, the four states of FSM were then classified as the domains of the survey. Each of the states was further divided into 3 strata, except for Kosrae which was not divided at all because it doesn't possess any outer islands and it has relatively good access to goods and services. The entire island was therefore classified under stratum 1. Each stratum was defined as follows:

1) State center and immediate surrounding areas:
- High 'living standard' and has immediate access to goods and services.

2) Areas surrounding state center (rest of main island):
- Medium 'living standard' and sometime limited access to goods and services

3) Outer islands:
- Low 'living standard' and rare access to goods and services.

Within each stratum, the HIES used a two-stage stratified sampling approach from which the sample was selected independently. First, enumeration districts (EDs) were drawn from each stratum using Proportion Probability to Size (PPS) sampling. Thus, the larger the ED size, the higher its probability of selection. About 69 EDs out of a total of 373 EDs were selected nationwide for the survey. Generally, one enumerator is assigned to each ED. Second, 20 households were systematically selected from an updated household listing for each of the selected EDs using a random start to come up with a total sample size of 1,380 households, or roughly 8.4 percent of all households in the state. Although it offered a fairly good representation of the total households in the nation, the final sample size showed a reduction of nearly 180 households from the 1,560 households, or 10 percent, initially selected for the survey.

Detailed information on the changes made to the sample size can be found in the next section under "Deviations from Sample Design."

Sampling deviation

The original plan to sample 1,560 households, or about 9.5 percent of all households in the nation was eventually reduced to 1,380 households, or about 8.4 percent of all households. The reduction of the sample size was due to fuel unavailability for transportation and uncertainty of field trip schedules to some of the selected outer islands. Dropping some of these islands from the sample was not expected to impact significantly on the accuracy of the survey results because independent weighting took place within each stratum, where islands were considered to be sufficiently homogenous.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires and forms used for the 2005 FSM HIES consisted of 1) HIES Questionnaire and 2) Weekly Diaries. The HIES Questionnaire were provided to enumerators and should be filled out during the first visit to the household. Its main objective was to collect housing information, basic demographic information about members of the household and general household expenditures over the previous year. On the other hand, the weekly diaries, was an attempt to record household expenditure on a daily basis over the course of a 2 week period. Both the HIES questionnaire and the weekly diary were developed and modeled after similar forms from the 1998 FSM HIES Survey and the 2004 Palau HIES Survey. Dr. Micheal Levin from the US Census Bureau, International Program Center (IPC), Ms. Brihmer Johnson of the FSM Division of Statistics and Mr. Glenn McKinlay, statistics advisor to FSM Division of Statistics, provided crucial inputs to the overall design of these forms. All questionnaires and diaries used during the HIES were printed in English so it was extremely important that field interviewers understand the instuctions and questions contained within. Testing of the questionnaire were carried out by FSM Division of Statistics staffs who conducted "real" interviews with certain households in their neighborhood as well as having their own household be interviewed by a different office staff. Specific sections for both the HIES questionnaire and the weekly diaries are outlined below:

I. HIES Questionnaire

1) General Household characteristics 2 ) Individual Person Characteristics 3) General Expenditure Listings - 12 Months Recall Period

II. 2 Week Daily Diaries

1) Daily Expenditure Diary - Day1 (Mon) thru Day7 (Sun) 2) Home Produced Items 3) Gifts Given Away 4) Gifts Received 5) Unusual Expenses for Special Events

Cleaning operations

Data editing of the 2005 FSM HIES data occurred over several instances during the data processing phase of the project and afterwards prior to putting together the final report. After a two weeks office review and call backs right after the enumeration phase, the initial phase of data editing took place on July 18, 2005 when the data processing phase of the survey commenced. Training for editing and coding took place on the same day along with the signing of contracts for 10 office clerks recruited to carry out this phase of the survey. As part of their contract, these individuals were also hired to key in the data at a later time. One of their primary responsibily was to match geographic ids for questionnaire with corresponding diaries and ensure consistencies and valid entries accordingly. No computer consistency edit checks were run against the data during the keying/verification process since the programs for these processes were not available at the time. All data quality checks and edits were done at the US Bureau of Census. Further edits were applied to the data during the data analysis and report writing process.

There were five types of checks performed: Structural check, Verification check, Consistency check, Macro Editing check, Data Quality assessment. Edit lists were also produced for health module, income and expenditure questionnaire which needed to be checked against the questionnaires. On the edit list, corrections of errors were made by crossing out incorrect or missing values and entering the correct values in red. Missing amounts that were also missing on the questionnaire will need to be estimated using estimates from questionnaires in the same Enumeration District (ED) batch. For the diaries, the batch files were concatenated for each state and exported to tab delimited files. These files were imported into Excel and the unit price for each item was calculated using quantities and weights where possible. Records for each item were then filtered out and check for outlier unit price values (both large and small values as well as missing values). Values for missing amounts were imputed from estimated using average prices from the items within the same ED.

The office operations manual used for editing and coding the questionnaires and diaries is provided under "Technical Documents/Data Processing Documents/Office Editing & Coding."

Response rate

Original Sample Size: 1,560 Households Original Sampling fraction 9.5%

Final Sample Size: 1,380 Households Final Sampling fraction 8.4%

The response rate for the final sample size of 1,380 households is 100 percent. The majority of households originally selected for the survey did respond to the survey. Households which have moved to other unselected areas or elsewhere and those who refused to respond were replaced with nearby households that were willing to participate in the survey.

Sampling error estimates

No sampling error analysis of the survey was calculated.

Data appraisal

The questionnaire design of the 2005 HIES vary from that of the 1998 HIES rendering comparison of the data to the 2005 HIES limited. However, when the data permits, comparisons were made.

Abstract

The main objectives of the 2018/19 NLSS are: i) to provide critical information for production of a wide range of socio-economic and demographic indicators, including for benchmarking and monitoring of SDGs; ii) to monitor progress in population's welfare; iii) to provide statistical evidence and measure the impact on households of current and anticipated government policies. In addition, the 2018/19 NLSS could be utilized to improve other non-survey statistical information, e.g. to determine and calibrate the contribution of final consumption expenditures of households to GDP; to update the weights and determine the basket for the national Consumer Price Index (CPI); to improve the methodology and dissemination of micro-economic and welfare statistics in Nigeria.

The 2018/19 NLSS collected a comprehensive and diverse set of socio-economic and demographic data pertaining to the basic needs and conditions under which households live on a day to day basis. The 2018/19 NLSS questionnaire includes wide-ranging modules, covering demographic indicators, education, health, labour, expenditures on food and non-food goods, non-farm enterprises, household assets and durables, access to safety nets, housing conditions, economic shocks, exposure to crime and farm production indicators.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING PROCEDURE The 2018/19 NLSS sample is designed to provide representative estimates for the 36 states and the Federal Capital Territory (FCT), Abuja. By extension. The sample is also representative at the national and zonal levels. Although the sample is not explicitly stratified by urban and rural areas, it is possible to obtain urban and rural estimates from the NLSS data at the national level. At all stages, the relative proportion of urban and rural EAs as has been maintained. Before designing the sample for the 2018/19 NLSS, the results from the 2009/10 HNLSS were analysed to extract the sampling properties (variance, design effect, etc.) and estimate the required sample size to reach a desired precision for poverty estimates in the 2018/19 NLSS.

EA SELECTION: The sampling frame for the 2018/19 NLSS was based on the national master sample developed by the NBS, referred to as the NISH2 (Nigeria Integrated Survey of Households 2). This master sample was based on the enumeration areas (EAs) defined for the 2006 Nigeria Census Housing and Population conducted by National Population Commission (NPopC). The NISH2 was developed by the NBS to use as a frame for surveys with state-level domains. NISH2 EAs were drawn from another master sample that NBS developed for surveys with LGA-level domains (referred to as the “LGA master sample”). The NISH2 contains 200 EAs per state composed of 20 replicates of 10 sample EAs for each state, selected systematically from the full LGA master sample. Since the 2018/19 NLSS required domains at the state-level, the NISH2 served as the sampling frame for the survey. Since the NISH2 is composed of state-level replicates of 10 sample EAs, a total of 6 replicates were selected from the NISH2 for each state to provide a total sample of 60 EAs per state. The 6 replicates selected for the 2018/19 NLSS in each state were selected using random systematic sampling. This sampling procedure provides a similar distribution of the sample EAs within each state as if one systematic sample of 60 EAs had been selected directly from the census frame of EAs.

A fresh listing of households was conducted in the EAs selected for the 2018/19 NLSS. Throughout the course of the listing, 139 of the selected EAs (or about 6%) were not able to be listed by the field teams. The primary reason the teams were not able to conduct the listing in these EAs was due to security issues in the country. The fieldwork period of the 2018/19 NLSS saw events related to the insurgency in the north east of the country, clashes between farmers and herdsman, and roving groups of bandits. These events made it impossible for the interviewers to visit the EAs in the villages and areas affected by these conflict events. In addition to security issues, some EAs had been demolished or abandoned since the 2006 census was conducted. In order to not compromise the sample size and thus the statistical power of the estimates, it was decided to replace these 139 EAs. Additional EAs from the same state and sector were randomly selected from the remaining NISH2 EAs to replace each EA that could not be listed by the field teams. This necessary exclusion of conflict affected areas implies that the sample is representative of areas of Nigeria that were accessible during the 2018/19 NLSS fieldwork period. The sample will not reflect conditions in areas that were undergoing conflict at that time. This compromise was necessary to ensure the safety of interviewers.

HOUSEHOLD SELECTION: Following the listing, the 10 households to be interviewed were selected from the listed households. These households were selected systemically after sorting by the order in which the households were listed. This systematic sampling helped to ensure that the selected households were well dispersed across the EA and thereby limit the potential for clustering of the selected households within an EA. Occasionally, interviewers would encounter selected households that were not able to be interviewed (e.g. due to migration, refusal, etc.). In order to preserve the sample size and statistical power, households that could not be interviewed were replaced with an additional randomly selected household from the EA. Replacement households had to be requested by the field teams on a case-by-case basis and the replacement household was sent by the CAPI managers from NBS headquarters. Interviewers were required to submit a record for each household that was replaced, and justification given for their replacement. These replaced households are included in the disseminated data. However, replacements were relatively rare with only 2% of sampled households not able to be interviewed and replaced.

Sampling deviation

Although a sample was initially drawn for Borno state, the ongoing insurgency in the state presented severe challenges in conducting the survey there. The situation in the state made it impossible for the field teams to reach large areas of the state without compromising their safety. Given this limitation it was clear that a representative sample for Borno was not possible. However, it was decided to proceed with conducting the survey in areas that the teams could access in order to collect some information on the parts of the state that were accessible.

The limited area that field staff could safely operate in in Borno necessitated an alternative sample selection process from the other states. The EA selection occurred in several stages. Initially, an attempt was made to limit the frame to selected LGAs that were considered accessible. However, after selection of the EAs from the identified LGAs, it was reported by the NBS listing teams that a large share of the selected EAs were not safe for them to visit. Therefore, an alternative approach was adopted that would better ensure the safety of the field team but compromise further the representativeness of the sample. First, the list of 788 EAs in the LGA master sample for Borno were reviewed by NBS staff in Borno and the EAs they deemed accessible were identified. The team identified 359 EAs (46%) that were accessible. These 359 EAs served as the frame for the Borno sample and 60 EAs were randomly selected from this frame. However, throughout the course of the NLSS fieldwork, additional insurgency related events occurred which resulted in 7 of the 60 EAs being inaccessible when they were to be visited. Unlike for the main sample, these EAs were not replaced. Therefore, 53 EAs were ultimately covered from the Borno sample. The listing and household selection process that followed was the same as for the rest of the states.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two sets of questionnaires – household and community – were used to collect information in the NLSS2018/19. The Household Questionnaire was administered to all households in the sample. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

Household Questionnaire: The Household Questionnaire provides information on demographics; education; health; labour; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; agriculture and land tenure; and other sources of household income.

Community Questionnaire: The Community Questionnaire solicits information on access to transported and infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Cleaning operations

CAPI: The 2018/19 NLSS was conducted using the Survey Solutions Computer Assisted Person Interview (CAPI) platform. The Survey Solutions software was developed and maintained by the Development Economics Data Group (DECDG) at the World Bank. Each interviewer and supervisor was given a tablet which they used to

The federally mandated minimum wage in the United States is 7.25 U.S. dollars per hour, although the minimum wage varies from state to state. As of January 1, 2025, the District of Columbia had the highest minimum wage in the U.S., at 17.5 U.S. dollars per hour. This was followed by Washington, which had 16.66 U.S. dollars per hour as the state minimum wage. Minimum wage workers Minimum wage jobs are traditionally seen as “starter jobs” in the U.S., or first jobs for teenagers and young adults, and the number of people working minimum wage jobs has decreased from almost four million in 1979 to about 247,000 in 2020. However, the number of workers earning less than minimum wage in 2020 was significantly higher, at about 865,000. Minimum wage jobs Minimum wage jobs are primarily found in food preparation and serving occupations, as well as sales jobs (primarily in retail). Because the minimum wage has not kept up with inflation, nor has it been increased since 2009, it is becoming harder and harder live off of a minimum wage wage job, and for those workers to afford essential things like rent.

From landing page:FHFA establishes annual single-family and multifamily housing goals for mortgages purchased by Fannie Mae and Freddie Mac. The Enterprise Housing Goals include separate categories for single-family mortgages on housing that is affordable to low-income and very low-income families, as well as refinanced mortgages for low-income borrowers. FHFA also establishes separate annual goals for multifamily housing. Loans that are eligible for housing goals credit are mortgages on owner-occupied housing with one to four units. The mortgages must be conventional, conforming mortgages, defined as mortgages that are not insured or guaranteed by the Federal Housing Administration or another government agency and with principal balances that do not exceed the conforming loan limits for Enterprise mortgages. This page provides data on Enterprise performance and activity related to the single-family housi​​ng goals. A full glossary of terms is provided below. Single-Family Enterprise Mortgage Acquisitions: Race and Ethnicity Data The new housing goals data tables provide insight on the racial and ethnic composition of loans acquired by the Enterprises that are eligible for housing goals credit. FHFA has provided the racial and ethnic distribution of the Enterprises' acquisitions across each of the current single-family housing goals categories. ​ Single-Family Housing Goal Loan Segments: State-Level Data FHFA is publishing state-level data for each single-family goal loan purchase and refinance segment. It is important to note that FHFA does not set state-level targets but only at the national level. These tables provide the Enterprises' share in each state along with the market share, as calculated by FHFA using the 'static' HMDA data for each year to determine Enterprise housing goals performance each year. It is important to note that HMDA state-level data are impacted by the number of HMDA-exempt reporters in each state. For more information on HMDA reporting requirements, visit the CFPB HMDA Reporting Requirements page.Low-Income Census Tracts, Minority Census Tracts and Designated Disaster Areas Data The Federal Housing Enterprises Financial Safety and Soundness Act of 1992 (Safety and Soundness Act) provides for the establishment of single-family and multifamily goals each year, including a single-family purchase money mortgage goal for families residing in low-income areas. The Safety and Soundness Act defines "low-income area" for the single-family low-income areas home purchase goal as: Census tracts or block numbering areas in which the median income does not exceed 80 percent of area median income (AMI). In addition, for the purposes of this goal, "families residing in low-income areas" also include: Families with income not greater than 100 percent of AMI who reside in minority census tracts. Families with income not greater than 100 percent of AMI who reside in designated disaster areas. ​A "minority census tract" is a census tract that has a minority population of at least 30 percent and a median income of less than 100 percent of the AMI. A "low-income census tract" is census tract in which the median income does not exceed 80 percent of the AMI. Designated disaster areas are identified by FHFA based on the three most recent years' declarations by the Federal Emergency Management Agency (FEMA), where individual assistance payments were authorized by FEMA. A map of census tracts identified as minority census tracts in 2024 can be ​found here. A map of census tracts identified as low-income census tracts in 2024 can be found here. ​Learn more about low-income census tracts, minority census tracts, and designated disaster areas.

A wide-ranging representative longitudinal study of private households that permits researchers to track yearly changes in the health and economic well-being of older people relative to younger people in Germany from 1984 to the present. Every year, there were nearly 11,000 households, and more than 20,000 persons sampled by the fieldwork organization TNS Infratest Sozialforschung. The data provide information on all household members, consisting of Germans living in the Old and New German States, Foreigners, and recent Immigrants to Germany. The Panel was started in 1984. Some of the many topics include household composition, occupational biographies, employment, earnings, health and satisfaction indicators. In addition to standard demographic information, the GSOEP questionnaire also contains objective measuresuse of time, use of earnings, income, benefit payments, health, etc. and subjective measures - level of satisfaction with various aspects of life, hopes and fears, political involvement, etc. of the German population. The first wave, collected in 1984 in the western states of Germany, contains 5,921 households in two randomly sampled sub-groups: 1) German Sub-Sample: people in private households where the head of household was not of Turkish, Greek, Yugoslavian, Spanish, or Italian nationality; 2) Foreign Sub-Sample: people in private households where the head of household was of Turkish, Greek, Yugoslavian, Spanish, or Italian nationality. In each year since 1984, the GSOEP has attempted to re-interview original sample members unless they leave the country. A major expansion of the GSOEP was necessitated by German reunification. In June 1990, the GSOEP fielded a first wave of the eastern states of Germany. This sub-sample includes individuals in private households where the head of household was a citizen of the German Democratic Republic. The first wave contains 2,179 households. In 1994 and 1995, the GSOEP added a sample of immigrants to the western states of Germany from 522 households who arrived after 1984, which in 2006 included 360 households and 684 respondents. In 1998 a new refreshment sample of 1,067 households was selected from the population of private households. In 2000 a sample was drawn using essentially similar selection rules as the original German sub-sample and the 1998 refreshment sample with some modifications. The 2000 sample includes 6,052 households covering 10,890 individuals. Finally, in 2002, an overrepresentation of high-income households was added with 2,671 respondents from 1,224 households, of which 1,801 individuals (689 households) were still included in the year 2006. Data Availability: The data are available to researchers in Germany and abroad in SPSS, SAS, TDA, STATA, and ASCII format for immediate use. Extensive documentation in English and German is available online. The SOEP data are available in German and English, alone or in combination with data from other international panel surveys (e.g., the Cross-National Equivalent Files which contain panel data from Canada, Germany, and the United States). The public use file of the SOEP with anonymous microdata is provided free of charge (plus shipping costs) to universities and research centers. The individual SOEP datasets cannot be downloaded from the DIW Web site due to data protection regulations. Use of the data is subject to special regulations, and data privacy laws necessitate the signing of a data transfer contract with the DIW. The English Language Public Use Version of the GSOEP is distributed and administered by the Department of Policy Analysis and Management, Cornell University. The data are available on CD-ROM from Cornell for a fee. Full instructions for accessing GSOEP data may be accessed on the project website, http://www.human.cornell.edu/che/PAM/Research/Centers-Programs/German-Panel/cnef.cfm * Dates of Study: 1984-present * Study Features: Longitudinal, International * Sample Size: ** 1984: 12,290 (GSOEP West) ** 1990: 4,453 (GSOEP East) ** 2000: 20,000+ Links: * Cornell Project Website: http://www.human.cornell.edu/che/PAM/Research/Centers-Programs/German-Panel/cnef.cfm * GSOEP ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00131

The median family income in the United States grew to 100,800 U.S. dollars in 2023, an increase on the previous year. Family income is the total income earned by all family members who have been living in the household for at least one year and are at least 14 years old.

Abstract

The National Survey of Household Income and Expenditure (ENIGH) aims to provide a statistical overview of the behavior of household income and expenditure in terms of its amount, origin and distribution. In addition, it offers information on the occupational and sociodemographic characteristics of the members of the household, as well as the characteristics of the housing infrastructure and household equipment.

The ENIGH is part of the Information System of National Interest (IIN), which means that the results obtained from this project are mandatory for the Federation, the states and the municipalities, in order to contribute to national development.

In 1984, a trend began to broaden the objectives and homogenize the methodology, taking into account international recommendations and the information requirements of the different users, taking care of historical comparability.

Periodicity: Since 1992 it has been carried out biennially (every two years) with the exception of 2005 when an extraordinary survey was carried out.

Target population: It is made up of the households of nationals or foreigners, who usually reside in private homes within the national territory.

Selection Unit: Private home. The dwellings are chosen through a meticulous statistical process that guarantees that the results obtained from only a part of the population (sample) can be generalized to the total.

Sampling Frame: INEGI's multi-purpose framework is made up of demographic and cartographic information obtained from the 2010 Population and Housing Census.

Observation unit: The home.

Unit of analysis: The household, the dwelling and the members of the household.

Thematic coverage:

Characteristics of the house. Residents and identification of households in the dwelling. Sociodemographic characteristics of the residents of the dwelling. Home equipment, services. Activity condition and occupational characteristics of household members aged 12 and over. Total current income (monetary and non-monetary) of households. Financial and capital perceptions of households and their members. Current monetary expenditure of households. Financial and capital expenditures of households.

The different concepts of the ENIGH are governed by recommendations agreed upon in international conventions, for example:

The resolutions and reports of the 18 International Conferences on Labour Statistics, of the International Labour Organization (ILO).

The final report and recommendations of the Canberra Group, an expert group on "Household Income Statistics".

Manual of Household Surveys. Department of International Economic and Social Affairs, Bureau of Statistics. United Nations, New York, 1987.

They are also articulated with the CNational Accounts and with the Household Surveys carried out by the INEGI.

Sample size: At the national level, including the ten-one, there are 93,186 private homes.

Survey period: The collection of information will take place between August 11 and November 18 of this year. Throughout this period, ten cuts are made, each organized in ten days; Therefore, each of these cuts will be known as tens (see calendar in the annex).

Workload: According to the meticulousness in the recording of information in this project, a load of six interviews in private homes per dozen has been defined for each interviewer. The number of interviews may decrease or increase according to several factors: non-response, recovery from non-response, or additional households.

Geographic coverage

National and at the level of the federal entity.

• Urban area: localities with 2,500 or more inhabitants
• Rural area: localities with less than 2,500 inhabitants

Analysis unit

The Home, the Dwelling, and the Members of the Household

Universe

The survey is aimed at households in the national territory.

Kind of data

Probabilistic household survey

Sampling procedure

The design of the exhibition for ENIGH-2016 is characterized by being probabilistic; Consequently, the results obtained from the survey are generalized to the entire population. At the same time, the design is two-stage, stratified and by conglomerates, where the ultimate unit of selection is the dwelling and the unit of observation is the home.

For the selection of the sample, the National Housing Framework 2012 of the INEGI was used, built from the cartographic and demographic information obtained from the 2010 Population and Housing Census.

This sample is a master sample from which the subsamples are selected for all the housing surveys carried out by INEGI; its design is probabilistic, stratified, single-stage and clustered; The latter are also considered primary sampling units, since it is in them that the dwellings that make up the samples of the different surveys are selected, in a second stage. The master sample is constructed as follows:

Formation of the primary sampling units (UPM)

First, the set of UPMs that will cover the national territory is constructed.

The primary sampling units are made up of groups of dwellings with differentiated characteristics depending on the area to which they belong, as specified below:

a) In high urban areas

The minimum size of a UPM is 80 inhabited dwellings and the maximum is 160. They can be made up of:

• A block. • The union of two or more contiguous blocks of the same AGEB. • The union of two or more contiguous blocks of different AGEBs in the same locality. • The union of two or more contiguous blocks from different localities, which belong to the same size of locality.

b) In urban complement:

The minimum size of a UPM is 160 inhabited dwellings and the maximum is 300. They can be made up of:

• A block. • The union of two or more contiguous blocks of the same AGEB. • The union of two or more contiguous blocks of different AGEBs in the same locality. • The union of two or more contiguous blocks from different AGEBs and localities, but from the same municipality.

c) In rural areas:

The minimum size of a UPM is 160 inhabited dwellings and the maximum is 300. They can be made up of:

• An AGEB. • Part of an AGEB. • The union of two or more adjoining AGEBs in the same municipality. • The union of an AGEB with a part of another adjoining AGEB in the same municipality.

The total number of UPMs formed was 245,279.

Stratification

Once the set of PUs has been constructed, those with similar characteristics are grouped, that is, they are stratified.

The political division of the country and the formation of localities differentiated by their size, naturally form a geographical stratification.

In each state, three areas are distinguished, divided in turn into Areas.

High urban, Zone 01 to 09, Cities with 100,000 or more inhabitants.

Urban complement, Zone 25, 35, 45 and 55, From 50,000 to 99,999 inhabitants, 15,000 to 49,999 inhabitants, 5,000 to 14,999 inhabitants, 2,500 to 4,999 inhabitants.

Rural, Zone 60, Localities with less than 2,500 inhabitants.

At the same time, four sociodemographic strata were formed in which all the UPMs in the country were grouped, this stratification considers the sociodemographic characteristics of the inhabitants of the dwellings, as well as the physical characteristics and equipment of the same, expressed through 34 indicators built with information from the 2010 Population and Housing Census*, for which multivariate statistical methods were used.

In this way, each PSU was classified into a single geographical and a sociodemographic stratum.

As a result, there are a total of 683 strata throughout the country.

Selection of the UPMs of the master sample The UPMs of the master sample were selected by means of a sampling with probability proportional to the size.

Sample size

For the calculation of the sample size of the ENIGH-2016, an estimate of the mean for the variable of quarterly current income per household was considered.

Sampling deviation

As a result of the sum of the 81,515 homes selected and 1,203 additional households that were found in those homes, the total amounted to 82,718 households.

Mode of data collection

Face-to-face [f2f]

Research instrument

Six collection instruments will be used for the collection of information in each household, four of which concentrate information on the household as a whole.

These are: - Household and housing questionnaire - Household expenditure questionnaires - Daily expenditure booklet

In the other three, individual information is recorded for people - Questionnaire for people aged 12 and over - Questionnaire for people under 12 years of age - Questionnaire for household businesses

Cleaning operations

Capture activities

The capture consisted of transferring the information from the questionnaires that were fully answered to electronic means through IKTAN, in accordance with the procedures established for the capture process of the ENIGH 2016.

The Person in Charge of Capture and Validation, together with his work team, began the capture of the questionnaires collected by each Interviewer, organized by packages of questionnaires of each page with the result of a complete interview, following the established order:

• Household and housing questionnaire. • Questionnaires for people under 12 years of age. • Questionnaires for people aged 12 and over. • Questionnaires for home businesses. • Household expenditure questionnaire. • Daily expenses booklet.

In addition, the IKTAN made it possible to record and know the progress or conclusion of workloads.

Validation activities

In parallel to the capture, the state coordination began the validation of the sheets

This is a longitudinal survey designed to provide detailed information on the economic situation of households and persons in the United States. These data examine the distribution of income, wealth, and poverty in American society and gauge the effects of federal and state programs on the well-being of families and individuals. There are three basic elements contained in the survey. The first is a control card that records basic social and demographic characteristics for each person in a household, as well as changes in such characteristics over the course of the interviewing period. The second element is the core portion of the questionnaire, with questions repeated at each interview on labor force activity, types and amounts of income, participation in various cash and noncash benefit programs, attendance in postsecondary schools, private health insurance coverage, public or subsidized rental housing, low-income energy assistance, and school breakfast and lunch participation. The third element consists of topical modules, which are a series of supplemental questions asked during selected household visits. Topical modules include some core data to help link individuals to the core files. Topical module data for the 1992 Panel cover the following topics: Topical Module 1 -- welfare and other aid recipiency and employment, Topical Module 2 -- work disability, education and training, marital status, migration, and fertility histories, Topical Module 3 -- extended measures of well-being, including consumer durables, living conditions, and basic needs, Topical Module 4 -- assets and liabilities, retirement expectations and pension plan coverage, real estate, property, and vehicles, Topical Module 5 -- school enrollment and financing, Topical Module 6 -- work schedules, child care, support for nonhousehold members, functional limitations and disabilities, utilization of health care services, and home-based self-employment and size of firm, Topical Module 7 -- selected financial assets, medical expenses and work disability, real estate, shelter costs, dependent care, and vehicles, Topical Module 8 -- school enrollment and financing, Topical Module 9 -- work schedule, child care, child support agreements, child support, support for nonhousehold members, functional limitations and disability, utilization of health care, functional limitations and disability of children, health status and utilization of health care services, and utilization of health care services for children. Parts 26 and 27 are the Wave 5 and Wave 8 Topical Module Microdata Research Files obtained from the Census Bureau. These two topical module files include data on annual income, retirement accounts and taxes, and school enrollment and financing. These topical module files have not been edited nor imputed, although they have been topcoded or bottomcoded and recoded if necessary by the Census Bureau to avoid disclosure of individual respondents' identities. (Source: downloaded from ICPSR 7/13/10)

This is a longitudinal survey designed to provide detailed information on the economic situation of households and persons in the United States. These data examine the distribution of income, wealth, and poverty in American society and gauge the effects of federal and state programs on the well-being of families and individuals. There are three basic elements contained in the survey. The first is a control card that records basic social and demographic characteristics for each person in a household, as well as changes in such characteristics over the course of the interviewing period. The second element is the core portion of the questionnaire, with questions repeated at each interview on labor force activity, types and amounts of income, participation in various cash and noncash benefit programs, attendance in postsecondary schools, private health insurance coverage, public or subsidized rental housing, low-income energy assistance, and school breakfast and lunch participation. The third element consists of topical modules, which are a series of supplemental questions asked during selected household visits. Topical modules include some core data to help link individuals to the core files. Topical module data for the 1992 Panel cover the following topics: Topical Module 1 -- welfare and other aid recipiency and employment, Topical Module 2 -- work disability, education and training, marital status, migration, and fertility histories, Topical Module 3 -- extended measures of well-being, including consumer durables, living conditions, and basic needs, Topical Module 4 -- assets and liabilities, retirement expectations and pension plan coverage, real estate, property, and vehicles, Topical Module 5 -- school enrollment and financing, Topical Module 6 -- work schedules, child care, support for nonhousehold members, functional limitations and disabilities, utilization of health care services, and home-based self-employment and size of firm, Topical Module 7 -- selected financial assets, medical expenses and work disability, real estate, shelter costs, dependent care, and vehicles, Topical Module 8 -- school enrollment and financing, Topical Module 9 -- work schedule, child care, child support agreements, child support, support for nonhousehold members, functional limitations and disability, utilization of health care, functional limitations and disability of children, health status and utilization of health care services, and utilization of health care services for children. Parts 26 and 27 are the Wave 5 and Wave 8 Topical Module Microdata Research Files obtained from the Census Bureau. These two topical module files include data on annual income, retirement accounts and taxes, and school enrollment and financing. These topical module files have not been edited nor imputed, although they have been topcoded or bottomcoded and recoded if necessary by the Census Bureau to avoid disclosure of individual respondents' identities.