According to an April 2023 survey by We Are Social and Statista Q, 40 percent of U.S. consumers feel highly affected by the ongoing cost of living crisis, whereas only 6 percent don't feel affected at all.

Cost of living indices are relative to New York City (NYC) which means that for New York City, each index should be 100. If another city has, for example, rent index of 120, it means that on an average in that city rents are 20% more expensive than in New York City. If a city has rent index of 70, that means on an average in that city rents are 30% less expensive than in New York City.

Cost of Living Index (Excl. Rent) is a relative indicator of consumer goods prices, including groceries, restaurants, transportation and utilities. Cost of Living Index doesn't include accommodation expenses such as rent or mortgage. If a city has a Cost of Living Index of 120, it means Numbeo estimates it is 20% more expensive than New York (excluding rent).

Rent Index is an estimation of prices of renting apartments in the city compared to New York City. If Rent index is 80, Numbeo estimates that price of rents in that city is on an average 20% less than the price in New York.

Groceries Index is an estimation of grocery prices in the city compared to New York City. To calculate this section, Numbeo uses weights of items in the "Markets" section for each city.

Restaurants Index is a comparison of prices of meals and drinks in restaurants and bars compared to NYC.

Cost of Living Plus Rent Index is an estimation of consumer goods prices including rent comparing to New York City.

Local Purchasing Power shows relative purchasing power in buying goods and services in a given city for the average wage in that city. If domestic purchasing power is 40, this means that the inhabitants of that city with the average salary can afford to buy on an average 60% less goods and services than New York City residents with an average salary.

U.S. Software Developer Salaries

Analyzing Regional Variations

By [source]

About this dataset

This dataset provides an extensive look into the financial health of software developers in major cities and metropolitan areas around the United States. We explore disparities between states and cities in terms of mean software developer salaries, median home prices, cost of living avgs, rent avgs, cost of living plus rent avgs and local purchasing power averages. Through this data set we can gain insights on how to better understand which areas are more financially viable than others when seeking employment within the software development field. Our data allow us to uncover patterns among certain geographic locations in order to identify other compelling financial opportunities that software developers may benefit from

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How to use the dataset

This dataset contains valuable information about software developer salaries across states and cities in the United States. It is important for recruiters and professionals alike to understand what kind of compensation software developers are likely to receive, as it may be beneficial when considering job opportunities or applying for a promotion. This guide will provide an overview of what you can learn from this dataset.

The data is organized by metropolitan areas, which encompass multiple cities within the same geographical region (e.g., “New York-Northern New Jersey” covers both New York City and Newark). From there, each metro can be broken down further into a number of different factors that may affect software developer salaries in the area:

• Mean Software Developer Salary (adjusted): The average salary of software developers in that particular metro area after accounting for cost of living differences within the region.
• Mean Software Developer Salary (unadjusted): The average salary of software developers in that particular metro area before adjusting for cost-of-living discrepancies between locales.
• Number of Software Developer Jobs: This column lists how many total jobs are available to software developers in this particular metropolitan area.
• Median Home Price: A metric which shows median value of all homes currently on the market within this partcular city or state. It helps gauge how expensive housing costs might be to potential residents who already have an idea about their income/salary range expectations when considering a move/relocation into another location or potentially looking at mortgage/rental options etc.. 5) Cost Of Living Avg: A metric designed to measure affordability using local prices paid on common consumer goods like food , transportation , health care , housing & other services etc.. Also prominent here along with rent avg ,cost od living plus rent avg helping compare relative cost structures between different locations while assessing potential remunerations & risk associated with them . 6)Local Purchasing Power Avg : A measure reflecting expected difference in discretionary spending ability among households regardless their income level upon relocation due to price discrepancies across locations allows individual assessment critical during job search particularly regarding relocation as well as comparison based decision making across prospective candidates during any hiring process . 7 ) Rent Avg : Average rental costs for homes / apartments dealbreakers even among prime job prospects particularly medium income earners.(basis family size & other constraints ) 8 ) Cost Of Living Plus Rent Avg : Used here as one sized fits perspective towards measuring overall cost structure including items

Research Ideas

• Comparing salaries of software developers in different cities to determine which city provides the best compensation package.
• Estimating the cost of relocating to a new city by looking at average costs such as rent and cost of living.
• Predicting job growth for software developers by analyzing factors like local purchasing power, median home price and number of jobs available

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking perm...

Average Household Income: Denpasar Municipality: Wage/Salary data was reported at 6,416,337.000 IDR in 2018. Average Household Income: Denpasar Municipality: Wage/Salary data is updated yearly, averaging 6,416,337.000 IDR from Dec 2018 (Median) to 2018, with 1 observations. The data reached an all-time high of 6,416,337.000 IDR in 2018 and a record low of 6,416,337.000 IDR in 2018. Average Household Income: Denpasar Municipality: Wage/Salary data remains active status in CEIC and is reported by Statistics Indonesia. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HB003: Cost of Living Survey (SBH-2018): Average Monthly Household Income: by Cities.

Residential rents in urban areas in the United States have grown faster than the general basket of products and services of the urban population. In 2024, the consumer price index (CPI) for rent of primary residences reached 420 index points, more than 100 index points more than the CPI for all items. The CPI measures the development of prices, with 1984 chosen as a base year. An index value of 400 indicates that rents have quadrupled since 1984.

The City of Toronto monitors food affordability every year using the Ontario Nutritious Food Basket (ONFB) costing tool. Food prices, among other essential needs, have increased considerably in the last several years. People receiving social assistance and earning low wages often do not have enough money to cover the cost of basic expenses, including food. As such, ONFB data is best used to assess the cost of living in Toronto by analyzing food affordability in relation to income, alongside other local basic expenses. The dataset describes the affordability of food and other basic expenses relative to income for 13 household scenarios. Scenarios were selected to reflect household characteristics that increase the risk of being food insecure, including reliance on social assistance as the main source of income, single-parent households, and rental housing. A median income scenario has also been included as a comparator. Income, including federal and provincial tax benefits, and the cost of four basic living expenses - rent food, childcare, and transportation - are estimated for each scenario. Results show the estimated amount of money remaining at the end of the month for each household. Three versions of the scenarios were created to describe: Income scenarios with subsidies: Subsidies can substantially reduce a households’ monthly expenses. Local subsidies for rent (Rent-Geared-to-Income), childcare (Childcare Fee Subsidy), and transit (Fair Pass) are accounted for in this file. Income scenarios without subsidies + average market rent: In this file, rental costs are based on average market rent, as measured by the Canadian Mortgage and Housing Corporation (CMHC). Income scenarios without subsidies + current market rent: Rental costs are based on current market rent (as of October 2023), as measured by the Toronto Regional Real Estate Board (TRREB). All values are rounded to the nearest dollar.

Title: Top Cities Worldwide: Quality of Life Index 2024 Subtitle: Ranking the World's Best Cities for Living Based on Key Metrics

Source of Data: The dataset was collected from Numbeo.com, a publicly accessible database that provides data on various quality-of-life indicators across cities worldwide. Numbeo aggregates user-contributed data validated through statistical methods to ensure reliability.

Data Collection Method: Data was acquired through web scraping. Care was taken to follow ethical web scraping practices, adhering to Numbeo’s terms of service and respecting their robots.txt file.

Columns Description:

The dataset includes the following columns:

• Rank: City ranking based on the Quality of Life Index.
• City: Name of the city.
• Country: Country where the city is located.
• Quality of Life Index: Overall index measuring quality of life, calculated based on various sub-indices.
• Purchasing Power Index: Measures relative purchasing power in the city.
• Safety Index: Indicates how safe the city is based on crime rates.
• Health Care Index: Reflects the quality and accessibility of healthcare services.
• Cost of Living Index: Represents the cost of living, including housing, food, and transportation.
• Property Price to Income Ratio: A measure of housing affordability, calculated as the ratio of property prices to average incomes.
• Traffic Commute Time Index: Average time spent commuting within the city.

Limitations and Considerations:

• User-Generated Data: Since data on Numbeo is user-contributed, it may be subject to biases.
• Data Update Frequency: As Numbeo updates its data regularly, the dataset represents a snapshot in time and may require periodic updates.

Usage Note: The dataset is intended for research and analytical purposes. Users should verify the data's applicability for their specific use cases, considering the limitations mentioned above.

The American Community Survey (ACS) 5 Year 2016-2020 socioeconomic estimate data is a subset of information derived from the following census tables:B08013 - Aggregate Travel Time To Work Of Workers By Sex;B08303 - Travel Time To Work;B17019 - Poverty Status In The Past 12 Months Of Families By Household Type By Tenure;B17021 - Poverty Status Of Individuals In The Past 12 Months By Living Arrangement;B19001 - Household Income In The Past 12 Months;B19013 - Median Household Income In The Past 12 Months;B19025 - Aggregate Household Income In The Past 12 Months;B19113 - Median Family Income In The Past 12 Months;B19202 - Median Non-family Household Income In The Past 12 Months;B23001 - Sex By Age By Employment Status For The Population 16 Years And Over;B25014 - Tenure By Occupants Per Room;B25026 - Total Population in Occupied Housing Units by Tenure by year Householder Moved into Unit;B25106 - Tenure By Housing Costs As A Percentage Of Household Income In The Past 12 Months;C24010 - Sex By Occupation For The Civilian Employed Population 16 Years And Over;B20004 - Median Earnings In the Past 12 Months (In 2015 Inflation-Adjusted Dollars) by Sex by Educational Attainment for the Population 25 Years and Over;B23006 - Educational Attainment by Employment Status for the Population 25 to 64 Years, and;B24021 - Occupation By Median Earnings In The Past 12 Months (In 2015 Inflation-Adjusted Dollars) For The Full-Time, Year-Round Civilian Employed Population 16 Years And Over.

To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_ACS 5-Year Socioeconomic Estimate Data by CountyDate of Coverage: 2016-2020

Around 64 percent of U.S. consumers spend less on non-essentials amidst the ongoing cost of living crisis in 2023. This is according to a survey conducted by We are Social and Statista Q, which shows that rising inflation rates have caused around a similar percentage of customers to pay more attention to bargains, good deals, or offers (when going shopping). Furthermore, around 39 percent of U.S. consumers do not go out for dinner/lunch anymore to deal with the situation.

Statistics on household income. These statistics were produced from the results of Living Costs and Household Income Surveys. Average earnings - Changes to average wages and earnings statistics by sex, industry and nationality.

Average Household Income: Semarang Municipality: Wage/Salary data was reported at 5,637,331.000 IDR in 2018. Average Household Income: Semarang Municipality: Wage/Salary data is updated yearly, averaging 5,637,331.000 IDR from Dec 2018 (Median) to 2018, with 1 observations. The data reached an all-time high of 5,637,331.000 IDR in 2018 and a record low of 5,637,331.000 IDR in 2018. Average Household Income: Semarang Municipality: Wage/Salary data remains active status in CEIC and is reported by Statistics Indonesia. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HB003: Cost of Living Survey (SBH-2018): Average Monthly Household Income: by Cities.

Average Household Income: Balikpapan Municipality: Wage/Salary data was reported at 7,964,745.000 IDR in 2018. Average Household Income: Balikpapan Municipality: Wage/Salary data is updated yearly, averaging 7,964,745.000 IDR from Dec 2018 (Median) to 2018, with 1 observations. The data reached an all-time high of 7,964,745.000 IDR in 2018 and a record low of 7,964,745.000 IDR in 2018. Average Household Income: Balikpapan Municipality: Wage/Salary data remains active status in CEIC and is reported by Statistics Indonesia. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HB003: Cost of Living Survey (SBH-2018): Average Monthly Household Income: by Cities.

Average Household Income: Pontianak Municipality: Wage/Salary data was reported at 5,517,431.000 IDR in 2018. Average Household Income: Pontianak Municipality: Wage/Salary data is updated yearly, averaging 5,517,431.000 IDR from Dec 2018 (Median) to 2018, with 1 observations. The data reached an all-time high of 5,517,431.000 IDR in 2018 and a record low of 5,517,431.000 IDR in 2018. Average Household Income: Pontianak Municipality: Wage/Salary data remains active status in CEIC and is reported by Statistics Indonesia. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HB003: Cost of Living Survey (SBH-2018): Average Monthly Household Income: by Cities.

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.

The City of Toronto monitors the affordability of food annually using the Nutritious Food Basket (NFB) costing tool. Food prices increased considerably in 2022. People with low incomes do not have enough money to cover the cost of basic expenses, including food. As such, NFB data is best viewed in relation to income, alongside other local basic expenses. The dataset describes the affordability of food and other basic expenses relative to income for nine household scenarios. Scenarios were selected to reflect household characteristics that increase the risk of being food insecure, including reliance on social assistance as the main source of income, single-parent households, and rental housing. A median income scenario has also been included as a comparator. Income, including federal and provincial tax benefits, and the cost of four basic living expenses - shelter, food, childcare, and transportation - are estimated for each scenario. Results show the amount of money remaining at the end of the month for each household. Three versions of the scenarios were created to describe: Income scenarios with subsidies: Subsidies can substantially reduce a households’ monthly expenses. Local subsidies for rent (Rent-Geared-to-Income), childcare (Childcare Fee Subsidy), and transit (Fair Pass) are accounted for in this file. Income scenarios without subsidies + average rent: In this file, rental costs are based on average rent, as measured by the Canadian Mortgage and Housing Corporation (CMHC). Income scenarios without subsidies + market rent: Rental costs are based on average market rent (as of June 2022), as measured by the Toronto Regional Real Estate Board (TRREB). Limitations Scenarios describe estimated values only, rounded to the nearest dollar. Income is estimated using a May/June 2022 reference period to align with Nutritious Food Basket data collection. Thus, tax year 2020 has been utilized in calculations. Income amounts include all entitlements available to Ontario residents; therefore, they are maximum amounts. Actual income amounts may be lower if residents do not file their income tax and/or do not apply for all available tax credits.

The Housing Affordability Data System (HADS) is a set of housing unit level datasets that measures the affordability of housing units and the housing cost burdens of households, relative to area median incomes, poverty level incomes, and Fair Market Rents. The purpose of these datasets is to provide housing analysts with consistent measures of affordability and burdens over a long period. The datasets are based on the American Housing Survey (AHS) national files from 1985 through 2005 and the metropolitan files for 2002 and 2004. Users can link records in HADS files to AHS records, allowing access to all of the AHS variables. Housing-level variables include information on the number of rooms in the housing unit, the year the unit was built, whether it was occupied or vacant, whether the unit was rented or owned, whether it was a single family or multiunit structure, the number of units in the building, the current market value of the unit, and measures of relative housing costs. The dataset also includes variables describing the number of people living in the household, household income, and the type of residential area (e.g., urban or suburban).

About the dataset This dataset uses information from the DWP benefit system to provide estimates of children living in poverty for wards in London. In order to be counted in this dataset, a family must have claimed Child Benefit and at least one other household benefit (Universal Credit, tax credits or Housing Benefit) during the year. The numbers are calibrated to the Households Below Average Income (HBAI) dataset used to provide the government's headline poverty statistics. The definition of relative low income is living in a household with equivalised* income before housing costs (BHC) below 60% of contemporary national median income. The income measure includes contributions from earnings, state support and pensions. Further detail on the estimates of dependent children living in relative low income, including alternative geographical breakdowns and additional variables, such as age of children, family type and work status are available from DWP's statistical tabulation tool Stat-Xplore. Minor adjustments to the data have been applied to guard against the identification of individual claimants. This dataset replaced the DWP children in out-of-work benefit households and HMRC children in low income families local measure releases. This dataset includes estimates for all wards in London of numbers of dependent children living in relative low income families for each financial year from 2014/15 to the latest available (2022/23). The figures for the latest year are provisional and are subject to minor revision when the next dataset is released by DWP. Headlines Number of children The number of dependent children living in relative low income across London, rose from below 310,000 in the financial year ending 2015 to over 420,000 in the financial year ending 2020, but has decreased since then to below 350,000, which is well below the number for financial year ending 2018. While many wards in London have followed a similar pattern, the numbers of children in low income families in some wards have fallen more sharply, while the numbers in other wards have continued to grow. Proportion of children in each London ward Ward population sizes vary across London, the age profile of that population also varies and both the size and make-up of the population can change over time, so in order to make more meaningful comparisons between wards or over time, DWP have also published rates, though see note below regarding caution when using these figures. A dependent child is anyone aged under 16; or aged 16 to 19 in full-time non-advanced education or in unwaged government training. Ward level estimates for the total number of dependent children are not available, so percentages cannot be derived. Ward level estimates for the percentage of children under 16 living in low income families are usually published by DWP but, in its latest release, ward-level population estimates were not available at the time, so no rates were published. To derive the rates in this dataset, the GLA has used the ONS's latest ward-level population estimates (official statistics in development). Percentages for 2021/22 are calculated using the 2021 mid year estimates, while percentages for 2022/23 are calculated using the 2022 mid year estimates. As these are official statistics in development, rates therefore need to be treated with some caution. Notes *equivalised income is adjusted for household size and composition in order to compare living standards between households of different types.

Average weekly earnings for the whole economy, for total and regular pay, in real terms (adjusted for consumer price inflation), UK, monthly, seasonally adjusted.

Average Household Income: Yogyakarta Municipality: Other data was reported at 737,627.000 IDR in 2018. Average Household Income: Yogyakarta Municipality: Other data is updated yearly, averaging 737,627.000 IDR from Dec 2018 (Median) to 2018, with 1 observations. The data reached an all-time high of 737,627.000 IDR in 2018 and a record low of 737,627.000 IDR in 2018. Average Household Income: Yogyakarta Municipality: Other data remains active status in CEIC and is reported by Statistics Indonesia. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HB003: Cost of Living Survey (SBH-2018): Average Monthly Household Income: by Cities.