In economics, the inflation rate is a measure of the change in price of a basket of goods. The most common measure being the consumer price index. It is the percentage rate of change in price level over time, and also indicates the rate of decrease in the purchasing power of money. The annual rate of inflation for 2023, was 4.1 percent higher in the United States when compared to the previous year. More information on inflation and the consumer price index can be found on our dedicated topic page. Additionally, the monthly rate of inflation in the United States can be accessed here. Inflation and purchasing power Inflation is a key economic indicator, and gives economists and consumers alike a look at changes in prices in the wider economy. For example, if an average pair of socks costs 100 dollars one year and 105 dollars the following year, the inflation rate is five percent. This means the amount of goods an individual can purchase with a unit of currency has decreased. This concept is often referred to as purchasing power. The data presents the average rate of inflation in a year, whereas the monthly measure of inflation measures the change in prices compared with prices one year ago. For example, monthly inflation in the U.S. reached a peak in June 2022 at 9.1 percent. This means that prices were 9.1 percent higher than they were in June of 2021. The purchasing power is the extent to which a person has available funds to make purchases. The Big Mac Index has been published by The Economist since 1986 and exemplifies purchasing power on a global scale, allowing us to see note the differences between different countries currencies. Switzerland for example, has the most expensive Big Mac in the world, costing consumers 6.71 U.S. dollars as of July 2022, whereas a Big Mac cost 5.15 dollars in the United States, and 4.77 dollars in the Euro area. One of the most important tools in influencing the rate of inflation is interest rates. The Federal Reserve of the United States has the capacity to make changes to the federal interest rate . Changes to the rate of inflation are thought to be an imbalance between supply and demand. After COVID-19 related lockdowns came to an end there was a sudden increase in demand for goods and services with consumers having more funds than usual thanks to reduced spending during lockdown and government funded economic support. Additionally, supply-chain related bottlenecks also due to lockdowns around the world and the Russian invasion of Ukraine meant that there was a decrease in the supply of goods and services. By increasing the interest rate, the Federal Reserve aims to reduce spending, and thus bring demand back into balance with supply.

Inflation is generally defined as the continued increase in the average prices of goods and services in a given region. Following the extremely high global inflation experienced in the 1980s and 1990s, global inflation has been relatively stable since the turn of the millennium, usually hovering between three and five percent per year. There was a sharp increase in 2008 due to the global financial crisis now known as the Great Recession, but inflation was fairly stable throughout the 2010s, before the current inflation crisis began in 2021. Recent years Despite the economic impact of the coronavirus pandemic, the global inflation rate fell to 3.26 percent in the pandemic's first year, before rising to 4.66 percent in 2021. This increase came as the impact of supply chain delays began to take more of an effect on consumer prices, before the Russia-Ukraine war exacerbated this further. A series of compounding issues such as rising energy and food prices, fiscal instability in the wake of the pandemic, and consumer insecurity have created a new global recession, and global inflation in 2024 is estimated to have reached 5.76 percent. This is the highest annual increase in inflation since 1996. Venezuela Venezuela is the country with the highest individual inflation rate in the world, forecast at around 200 percent in 2022. While this is figure is over 100 times larger than the global average in most years, it actually marks a decrease in Venezuela's inflation rate, which had peaked at over 65,000 percent in 2018. Between 2016 and 2021, Venezuela experienced hyperinflation due to the government's excessive spending and printing of money in an attempt to curve its already-high inflation rate, and the wave of migrants that left the country resulted in one of the largest refugee crises in recent years. In addition to its economic problems, political instability and foreign sanctions pose further long-term problems for Venezuela. While hyperinflation may be coming to an end, it remains to be seen how much of an impact this will have on the economy, how living standards will change, and how many refugees may return in the coming years.

Inflation in Zimbabwe rose to 10.61 percent in 2018, and is projected to jump dramatically to 736.11 percent in 2024. After that, estimates predict a slow decline for now - however, given Zimbabwe’s history of poor monetary policy, including one of the worst instances of hyperinflation, this seems unrealistic. Inflation history Inflation depends significantly on economic expectations of it, making it hard to reduce inflation once it has hit higher levels. This happened in Zimbabwe in the years approaching 2008, at the end of which a single U.S. dollar was worth over 2.6 trillion Zimbabwe dollars, up from 10,000 Zimbabwe dollars at the start of 2005. This all but destroyed Zimbabwe’s economy, leading to very low gross domestic product (GDP) per capita and a government struggling to finance itself. The way ahead In 2009, the Zimbabwean dollar had twelve zeros slashed from the banknotes. This was not enough, and after three decades of rule, former Zimbabwean president Robert Mugabe was removed from power at the end of 2017. Citizens of the country are trying to hold foreign banknotes; they prefer U.S. dollars or euros, but the South African rand is more common. However, the rand’s performance against other currencies has been lackluster in recent years. This underscores the struggle that the Zimbabwean people have to find a stable currency at the moment.

In 2023, the usual median hourly rate of a worker's wage in the United States was 19.24 U.S. dollars, a decrease from the previous year. Dollar value is based on 2023 U.S. dollars. In 1979, the median hourly earnings in the U.S. was 17.48 dollars.

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

About this dataset

Context

The dataset illustrates the median household income in Keytesville, spanning the years from 2010 to 2021, with all figures adjusted to 2022 inflation-adjusted dollars. Based on the latest 2017-2021 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2021, the median household income for Keytesville decreased by $1,996 (5.03%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $4,559 (6.51%) between 2010 and 2021.

Analyzing the trend in median household income between the years 2010 and 2021, spanning 11 annual cycles, we observed that median household income, when adjusted for 2022 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 6 years and declined for 5 years.

https://i.neilsberg.com/ch/keytesville-mo-median-household-income-trend.jpeg" alt="Keytesville, MO median household income trend (2010-2021, in 2022 inflation-adjusted dollars)">

Content

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

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2021
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2022 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

Graph and download economic data for Gross Domestic Product: Implicit Price Deflator (GDPDEF) from Q1 1947 to Q1 2025 about implicit price deflator, headline figure, inflation, GDP, and USA.

Inflation Rate in Taiwan decreased to 1.37 percent in June from 1.55 percent in May of 2025. This dataset provides the latest reported value for - Taiwan Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

*Ascending rank, from most underfunded disease condition (indicated by negative numbers) to most overfunded (positive numbers) as predicted by each model.† Univariate linear regression of the association between 2004 disease-specific Disability-Adjusted Life-Years (DALYs) and the outcome NIH dollars. Differences between expected and actual funding levels in 1996 are adjusted for inflation to 2006 dollar equivalents, but are otherwise unchanged from those reported by Gross et al.‡ Standard univariate linear regression of the outcome NIH dollars as predicted by disease-specific DALYs. A stepwise forward multivariable model retained only DALYs as a predictor.§ Standard multivariable linear regression of the outcome NIH dollars as predicted by disease-specific DALYs and charity revenue.¶ The constrained model is the multivariable linear regression model where the predicted NIH dollars are obligated to be proportional to disease-specific DALYs after adjustment for total number of hospital discharges and average hospital charges.

Inflation Rate in Bangladesh decreased to 8.48 percent in June from 9.05 percent in May of 2025. This dataset provides the latest reported value for - Bangladesh Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The table displays the source data and describes all steps that were taken to estimate the cost of a single MDA round per 100,000 eligibles.n.a. = not applicable.aThe term base year refers to the year in which cost were originally measured (1996 for India, 2002 for West Africa).bCalculated from 1), 3) and 4), assuming that drugs (50 mg DEC tablets) were purchased for all eligible persons.cFor India: cost of DEC (50-mg tablets; 5.2 tablets p.p. on average; 0.026 US$ p.p. on average) were subtracted.dCorrection for inflation, using the annual deflators as published by the World Bank [24], i.e. the rate of price change in the economy as a whole. The amount under 6) was first converted back to local currency using the base year conversion rate. Then we applied the correction for inflation between the base year and 2009. The new amount was reconverted into US dollars using the 2009 conversion rate. Average annual inflation in India was about 5% between 1996 and 2009. The average annual inflation between 2002 and 2009 in Burkina Faso was 9%.eWe assume that sensitization efforts in India are intensified to achieve higher coverage, as studied elsewhere [25], [26]. Associated extra costs (for personnel and supplies) would be 0.009 US $ per person in 2002, or 0.015 US$ per eligible if adjusted to 2009 values.fVolunteer remuneration has changed. In 2002, volunteers were paid for 2 days of training only, not distribution. By 2010 Burkina volunteers were remunerated for about 2.5 days training and 7 days distribution; the daily rate remained the same. [sources: [11] and personal communications from program directors in Burkina Faso in 2011].gIn India, DEC has to be purchased by the government, at 0.00924 US% p.p. on average (for 100 mg tablets, 2.75 tablets p.p. on average).hDonated drug: albendazole (0.022 US$ p.p.).iDonated drugs: albendazole (0.022 US$ p.p.) and ivermectin (4.2 US$ p.p. on average).

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Robeson County. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2013 and 2023, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Robeson County, the median household income for the households where the householder is White increased by $6,829(13.94%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $48,979 in 2013 and $55,808 in 2023.
• Black or African American: In Robeson County, the median household income for the households where the householder is Black or African American decreased by $1,996(6.01%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $33,188 in 2013 and $31,192 in 2023.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Robeson County.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• 2023: 2023 median household income
• Please note: All incomes have been adjusted for inflation and are presented in 2023-inflation-adjusted dollars.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

Inflation Rate in Hong Kong decreased to 1.90 percent in May from 2 percent in April of 2025. This dataset provides the latest reported value for - Hong Kong Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Mitchell County. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2011 and 2021, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Mitchell County, the median household income for the households where the householder is White increased by $1,996(3.10%), between 2011 and 2021. The median household income, in 2022 inflation-adjusted dollars, was $64,453 in 2011 and $66,449 in 2021.
• Black or African American: In Mitchell County, there was no reported median household income for Black or African American households in 2011. However, in 2021, the median household income increased to $20,562(2022 inflation-adjusted dollars), marking a significant change.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

https://i.neilsberg.com/ch/mitchell-county-ia-median-household-income-by-race-trends.jpeg" alt="Mitchell County, IA median household income trends across races (2011-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Mitchell County.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• Please note: 2020 1-Year ACS estimates data was not reported by Census Bureau due to impact on survey collection and analysis during COVID-19, thus for large cities (population 65,000 and above) median household income data is not available.
• Please note: All incomes have been adjusted for inflation and are presented in 2022-inflation-adjusted dollars.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

The statistic shows the average inflation rate in Canada from 1987 to 2024, with projections up until 2030. The inflation rate is calculated using the price increase of a defined product basket. This product basket contains products and services, on which the average consumer spends money throughout the year. They include expenses for groceries, clothes, rent, power, telecommunications, recreational activities and raw materials (e.g. gas, oil), as well as federal fees and taxes. In 2022, the average inflation rate in Canada was approximately 6.8 percent compared to the previous year. For comparison, inflation in India amounted to 5.56 percent that same year. Inflation in Canada In general, the inflation rate in Canada follows a global trend of decreasing inflation rates since 2011, with the lowest slump expected to occur during 2015, but forecasts show an increase over the following few years. Additionally, Canada's inflation rate is in quite good shape compared to the rest of the world. While oil and gas prices have dropped in Canada much like they have around the world, food and housing prices in Canada have been increasing. This has helped to offset some of the impact of dropping oil and gas prices and the effect this has had on Canada´s inflation rate. The annual consumer price index of food and non-alcoholic beverages in Canada has been steadily increasing over the last decade. The same is true for housing and other price indexes for the country. In general there is some confidence that the inflation rate will not stay this low for long, it is expected to return to a comfortable 2 percent by 2017 if estimates are correct.

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Lake Edward township. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2011 and 2021, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Lake Edward township, the median household income for the households where the householder is White decreased by $1,996(2.49%), between 2011 and 2021. The median household income, in 2022 inflation-adjusted dollars, was $80,198 in 2011 and $78,202 in 2021.
• Black or African American: As per the U.S. Census Bureau population data, in Lake Edward township, there are no households where the householder is Black or African American; hence, the median household income for the Black or African American population is not applicable.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

https://i.neilsberg.com/ch/lake-edward-township-mn-median-household-income-by-race-trends.jpeg" alt="Lake Edward Township, Minnesota median household income trends across races (2011-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Lake Edward township.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• Please note: 2020 1-Year ACS estimates data was not reported by Census Bureau due to impact on survey collection and analysis during COVID-19, thus for large cities (population 65,000 and above) median household income data is not available.
• Please note: All incomes have been adjusted for inflation and are presented in 2022-inflation-adjusted dollars.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

About this dataset

Context

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

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Bergen County, the median income for all workers aged 15 years and older, regardless of work hours, was $69,820 for males and $45,063 for females.

These income figures highlight a substantial gender-based income gap in Bergen County. Women, regardless of work hours, earn 65 cents for each dollar earned by men. This significant gender pay gap, approximately 35%, underscores concerning gender-based income inequality in the county of Bergen County.

- Full-time workers, aged 15 years and older: In Bergen County, among full-time, year-round workers aged 15 years and older, males earned a median income of $97,608, while females earned $78,327, leading to a 20% gender pay gap among full-time workers. This illustrates that women earn 80 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Bergen County.

Content

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

Gender classifications include:

• Male
• Female

Employment type classifications include:

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

Graph and download economic data for Real gross domestic product per capita (A939RX0Q048SBEA) from Q1 1947 to Q1 2025 about per capita, real, GDP, and USA.

When converted to the value of one US dollar in 2020, goods and services that cost one dollar in 1700 would cost just over 63 dollars in 2020, this means that one dollar in 1700 was worth approximately 63 times more than it is today. This data can be used to calculate how much goods and services from the years shown would cost today, by multiplying the price from then by the number shown in the graph. For example, an item that cost 50 dollars in 1970 would theoretically cost 335.5 US dollars in 2020 (50 x 6.71 = 335.5), although it is important to remember that the prices of individual goods and services inflate at different rates than currency, therefore this graph must only be used as a guide.

About this dataset

Context

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

Key observations: Insights from 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Watertown Town, the median income for all workers aged 15 years and older, regardless of work hours, was $66,528 for males and $58,555 for females.

Based on these incomes, we observe a gender gap percentage of approximately 12%, indicating a significant disparity between the median incomes of males and females in Watertown Town. Women, regardless of work hours, still earn 88 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

- Full-time workers, aged 15 years and older: In Watertown Town, among full-time, year-round workers aged 15 years and older, males earned a median income of $93,464, while females earned $90,606, resulting in a 3% gender pay gap among full-time workers. This illustrates that women earn 97 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Watertown Town.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Watertown Town, showcasing a consistent income pattern irrespective of employment status.

https://i.neilsberg.com/ch/watertown-town-ma-income-by-gender.jpeg" alt="Watertown Town, MA gender based income disparity">

Content

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

Gender classifications include:

• Male
• Female

Employment type classifications include:

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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