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 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.

Data is collected because of public interest in how the City’s budget is being spent on salary and overtime pay for all municipal employees. Data is input into the City's Personnel Management System (“PMS”) by the respective user Agencies. Each record represents the following statistics for every city employee: Agency, Last Name, First Name, Middle Initial, Agency Start Date, Work Location Borough, Job Title Description, Leave Status as of the close of the FY (June 30th), Base Salary, Pay Basis, Regular Hours Paid, Regular Gross Paid, Overtime Hours worked, Total Overtime Paid, and Total Other Compensation (i.e. lump sum and/or retro payments). This data can be used to analyze how the City's financial resources are allocated and how much of the City's budget is being devoted to overtime. The reader of this data should be aware that increments of salary increases received over the course of any one fiscal year will not be reflected. All that is captured, is the employee's final base and gross salary at the end of the fiscal year. In very limited cases, a check replacement and subsequent refund may reflect both the original check as well as the re-issued check in employee pay totals. NOTE 1: To further improve the visibility into the number of employee OT hours worked, beginning with the FY 2023 report, an updated methodology will be used which will eliminate redundant reporting of OT hours in some specific instances. In the previous calculation, hours associated with both overtime pay as well as an accompanying overtime “companion code” pay were included in the employee total even though they represented pay for the same period of time. With the updated methodology, the dollars shown on the Open Data site will continue to be inclusive of both types of overtime, but the OT hours will now reflect a singular block of time, which will result in a more representative total of employee OT hours worked. The updated methodology will primarily impact the OT hours associated with City employees in uniformed civil service titles. The updated methodology will be applied to the Open Data posting for Fiscal Year 2023 and cannot be applied to prior postings and, as a result, the reader of this data should not compare OT hours prior to the 2023 report against OT hours published starting Fiscal Year 2023. The reader of this data may continue to compare OT dollars across all published Fiscal Years on Open Data. NOTE 2: As a part of FISA-OPA’s routine process for reviewing and releasing Citywide Payroll Data, data for some agencies (specifically NYC Police Department (NYPD) and the District Attorneys’ Offices (Manhattan, Kings, Queens, Richmond, Bronx, and Special Narcotics)) have been redacted since they are exempt from disclosure pursuant to the Freedom of Information Law, POL § 87(2)(f), on the ground that disclosure of the information could endanger the life and safety of the public servants listed thereon. They are further exempt from disclosure pursuant to POL § 87(2)(e)(iii), on the ground that any release of the information would identify confidential sources or disclose confidential information relating to a criminal investigation, and POL § 87(2)(e)(iv), on the ground that disclosure would reveal non-routine criminal investigative techniques or procedures. Some of these redactions will appear as XXX in the name columns.

This dataset contains replication files for "The Association Between Income and Life Expectancy in the United States, 2001-2014" by Augustin Bergeron, Raj Chetty, David Cutler, Benjamin Scuderi, Michael Stepner, and Nicholas Turner. For more information, see https://opportunityinsights.org/paper/lifeexpectancy/. A summary of the related publication follows. How can we reduce socioeconomic disparities in health outcomes? Although it is well known that there are significant differences in health and longevity between income groups, debate remains about the magnitudes and determinants of these differences. We use new data from 1.4 billion anonymous earnings and mortality records to construct more precise estimates of the relationship between income and life expectancy at the national level than was feasible in prior work. We then construct new local area (county and metro area) estimates of life expectancy by income group and identify factors that are associated with higher levels of life expectancy for low-income individuals. Our findings show that disparities in life expectancy are not inevitable. There are cities throughout America — from New York to San Francisco to Birmingham, AL — where gaps in life expectancy are relatively small or are narrowing over time. Replicating these successes more broadly will require targeted local efforts, focusing on improving health behaviors among the poor in cities such as Las Vegas and Detroit. Our findings also imply that federal programs such as Social Security and Medicare are less redistributive than they might appear because low-income individuals obtain these benefits for significantly fewer years than high-income individuals, especially in cities like Detroit. Going forward, the challenge is to understand the mechanisms that lead to better health and longevity for low-income individuals in some parts of the U.S. To facilitate future research and monitor local progress, we have posted annual statistics on life expectancy by income group and geographic area (state, CZ, and county) at The Health Inequality Project website. Using these data, researchers will be able to study why certain places have high or improving levels of life expectancy and ultimately apply these lessons to reduce health disparities in other parts of the country.

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 data, maintained by the Mayor’s Office of Housing (MOH), is an inventory of all income-restricted units in the city. This data includes public housing owned by the Boston Housing Authority (BHA), privately- owned housing built with funding from DND and/or on land that was formerly City-owned, and privately-owned housing built without any City subsidy, e.g., created using Low-Income Housing Tax Credits (LIHTC) or as part of the Inclusionary Development Policy (IDP). Information is gathered from a variety of sources, including the City's IDP list, permitting and completion data from the Inspectional Services Department (ISD), newspaper advertisements for affordable units, Community Economic Development Assistance Corporation’s (CEDAC) Expiring Use list, and project lists from the BHA, the Massachusetts Department of Housing and Community Development (DHCD), MassHousing, and the U.S. Department of Housing and Urban Development (HUD), among others. The data is meant to be as exhaustive and up-to-date as possible, but since many units are not required to report data to the City of Boston, MOH is constantly working to verify and update it. See the data dictionary for more information on the structure of the data and important notes. The database only includes units that have a deed-restriction. It does not include tenant-based (also known as mobile) vouchers, which subsidize rent, but move with the tenant and are not attached to a particular unit. There are over 22,000 tenant-based vouchers in the city of Boston which provide additional affordability to low- and moderate-income households not accounted for here. The Income-Restricted Housing report can be directly accessed here:
https://www.boston.gov/sites/default/files/file/2023/04/Income%20Restricted%20Housing%202022_0.pdf

Learn more about income-restricted housing (as well as other types of affordable housing) here: https://www.boston.gov/affordable-housing-boston#income-restricted

According to a recent study, Colombia had the lowest monthly cost of living in Latin America with 546 U.S. dollars needed for basic living. In contrast, four countries had a cost of living above one thousand dollars, Costa Rica, Chile, Panama and Uruguay. In 2022, the highest minimum wage in the region was recorded by Ecuador with 425 dollars per month.

Can Latin Americans survive on a minimum wage? Even if most countries in Latin America have instated laws to guarantee citizens a basic income, these minimum standards are often not enough to meet household needs. For instance, it was estimated that almost 22 million people in Mexico lacked basic housing services. Salary levels also vary greatly among Latin American economies. In 2022, the average net monthly salary in Brazil was lower than Ecuador's minimum wage.

What can a minimum wage afford in Latin America? Latin American real wages have generally risen in the past decade. However, consumers in this region still struggle to afford non-basic goods, such as tech products. Recent estimates reveal that, in order to buy an iPhone, Brazilian residents would have to work more than two months to be able to pay for it. A gaming console, on the other hand, could easily cost a Latin American worker several minimum wages.

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.

In 2025, the minimum hourly wage in Beijing was the highest in China at 26.4 yuan per hour. In the past decade, China has been shifting from a cheap labor driven economy to more matured, service-oriented markets and industries. While the economy continues to grow, prices and wages keep on increasing as well. How do wages differ across the country? China’s provinces and municipalities are divided into districts of different levels. Most provinces set different minimum wages for different districts depending on the cost of living and level of development. Usually, provincial capitals and major cities enjoy higher hourly wages than smaller towns and rural areas of the same province. In 2025, the highest minimum hourly wages in China were to be found in Beijing and Tianjin municipalities with 26.4 and 24.4 yuan respectively, whereas employees in Hainan province who received a minimum wage were paid the least – between 16.3 and 17.9 yuan per hour. Minimum monthly wages that year were the highest in Shanghai and the lowest in Qinghai province. The average annual salary in urban China was around 120,700 yuan in 2023. What are the prospects? Regional governments in China are required to update their minimum wages at least every few years. Hebei, Fujian, and Guangdong – provinces that have not adjusted minimum wages in the past two years – are likely to do so in 2025. Along with economic development, increasing living standards, increasing prices and a shrinking labor force, overall minimum wages will likely continue growing in China.

A dataset listing the richest zip codes in New York per the most current US Census data, including information on rank and average income.

Nearly 9 million Americans live in extreme-poverty neighborhoods, places that also tend to be racially segregated and dangerous. Yet, the effects on the well-being of residents of moving out of such communities into less distressed areas remain uncertain. Moving to Opportunity (MTO) is a randomized housing experiment administered by the United States Department of Housing and Urban Development that gave low-income families living in high-poverty areas in five cities the chance to move to lower-poverty areas. Families were randomly assigned to one of three groups: (1) the low-poverty voucher (LPV) group (also called the experimental group) received Section 8 rental assistance certificates or vouchers that they could use only in census tracts with 1990 poverty rates below 10 percent. The families received mobility counseling and help in leasing a new unit. One year after relocating, families could use their voucher to move again if they wished, without any special constraints on location; (2) the traditional voucher (TRV) group (also called the Section 8 group) received regular Section 8 certificates or vouchers that they could use anywhere; these families received no special mobility counseling; (3) the control group received no certificates or vouchers through MTO, but continued to be eligible for project-based housing assistance and whatever other social programs and services to which they would otherwise be entitled. Families were tracked from baseline (1994-1998) through the long-term evaluation survey fielding period (2008-2010) with the purpose of determining the effects of "neighborhood" on participating families. This data collection includes data from the 3,273 adult interviews completed as part of the MTO long-term evaluation. Using data from the long-term evaluation, the associated article reports that moving from a high-poverty to lower-poverty neighborhood was associated in the long-term (10 to 15 years) with modest, but potentially important, reductions in the prevalence of extreme obesity and diabetes. The data contain all outcomes and mediators analyzed for the associated article (with the exception of a few mediator variables from the interim MTO evaluation) as well as a variety of demographic and other baseline measures that were controlled for in the analysis.

China is the largest labor force market in the world. China’s economic prosperity wouldn’t exist without the large number of people working in this country. With increasing living standards and growing inflation, the wages of employees in China are increasing as well. As of 2022, average wages in China increased to ******* yuan from ****** yuan in 2012. Wage gap between regions The wages vary in China depending on sector, position, gender and region like in any other country. Since China’s different regions have developed unequally, the wage gaps between people working in different regions can also be very large. This is a reason for no single minimum wage being set for the entire nation. The local governments set minimum wages based on local living standards. Considering the city tier, the wage standards are higher in cities with higher rankings. ******** and ******* have the highest minimum wage standards in China. Although the minimum wages in China have been increasing, the standards are still lower than in developed countries. Challenges of increasing labor costs Increasing wages also make the labor force market less attractive. Affected by increasing labor costs and the China-United States trade war, many companies are transferring their investment destinations, especially in the manufacturing sector. Local governments are also taking measures to ensure the living costs remain at a reasonable level to retain companies and employees. These measures include regulating the residential housing market more strictly.

Guyana was the South American country 20360the highest gross national income per capita, with 20,360 U.S. dollars per person in 2023. Uruguay ranked second, registering a GNI of 19,530 U.S. dollars per person, based on current prices. Gross national income (GNI) is the aggregated sum of the value added by residents in an economy, plus net taxes (minus subsidies) and net receipts of primary income from abroad. Which are the largest Latin American economies? Based on annual gross domestic product, which is the total amount of goods and services produced in a country per year, Brazil leads the regional ranking, followed by Mexico, Argentina, and Chile. Many Caribbean countries and territories hold the highest GDP per capita in this region, measurement that reflects how GDP would be divided if it was perfectly equally distributed among the population. GNI per capita is, however, a more exact calculation of wealth than GDP per capita, as it takes into consideration taxes paid and income receipts from abroad. How much inequality is there in Latin America? In many Latin American countries, more than half the total wealth created in their economies is held by the richest 20 percent of the population. When a small share of the population concentrates most of the wealth, millions of people don't have enough to make ends meet. For instance, in Brazil, about 5.32 percent of the population lives on less than 3.2 U.S. dollars per day.