About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Middle Inlet, Wisconsin, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 20,755, while the mean income for the highest quintile (20% of households with the highest income) is 186,714. This indicates that the top earners earn 9 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 350,363, which is 187.65% higher compared to the highest quintile, and 1688.09% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 Middle Inlet town median household income. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Sands Point, NY, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 62,342, while the mean income for the highest quintile (20% of households with the highest income) is 1,206,232. This indicates that the top earners earn 19 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 1,779,703, which is 147.54% higher compared to the highest quintile, and 2854.74% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 Sands Point median household income. You can refer the same here

Disposable Income per Capita: Urban: Middle Income data was reported at 48,508.000 RMB in 2024. This records an increase from the previous number of 46,276.000 RMB for 2023. Disposable Income per Capita: Urban: Middle Income data is updated yearly, averaging 8,678.295 RMB from Dec 1985 (Median) to 2024, with 40 observations. The data reached an all-time high of 48,508.000 RMB in 2024 and a record low of 737.280 RMB in 1985. Disposable Income per Capita: Urban: Middle Income data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Income by Income Level. Since 2013, All households in the sample are grouped, by per capita disposable income of the household, into groups of low income, lower middle income, middle income, upper middle income, and high income, each group consisting of 20%, 20%, 20%, 20%, and 20% of all households respectively.

VITAL SIGNS INDICATOR Jobs by Wage Level (EQ1)

FULL MEASURE NAME Distribution of jobs by low-, middle-, and high-wage occupations

LAST UPDATED January 2019

DESCRIPTION Jobs by wage level refers to the distribution of jobs by low-, middle- and high-wage occupations. In the San Francisco Bay Area, low-wage occupations have a median hourly wage of less than 80% of the regional median wage; median wages for middle-wage occupations range from 80% to 120% of the regional median wage, and high-wage occupations have a median hourly wage above 120% of the regional median wage.

DATA SOURCE California Employment Development Department OES (2001-2017) http://www.labormarketinfo.edd.ca.gov/data/oes-employment-and-wages.html

American Community Survey (2001-2017) http://api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Jobs are determined to be low-, middle-, or high-wage based on the median hourly wage of their occupational classification in the most recent year. Low-wage jobs are those that pay below 80% of the regional median wage. Middle-wage jobs are those that pay between 80% and 120% of the regional median wage. High-wage jobs are those that pay above 120% of the regional median wage. Regional median hourly wages are estimated from the American Community Survey and are published on the Vital Signs Income indicator page. For the national context analysis, occupation wage classifications are unique to each metro area. A low-wage job in New York, for instance, may be a middle-wage job in Miami. For the Bay Area in 2017, the median hourly wage for low-wage occupations was less than $20.86 per hour. For middle-wage jobs, the median ranged from $20.86 to $31.30 per hour; and for high-wage jobs, the median wage was above $31.30 per hour.

Occupational employment and wage information comes from the Occupational Employment Statistics (OES) program. Regional and subregional data is published by the California Employment Development Department. Metro data is published by the Bureau of Labor Statistics. The OES program collects data on wage and salary workers in nonfarm establishments to produce employment and wage estimates for some 800 occupations. Data from non-incorporated self-employed persons are not collected, and are not included in these estimates. Wage estimates represent a three-year rolling average.

Due to changes in reporting during the analysis period, subregion data from the EDD OES have been aggregated to produce geographies that can be compared over time. West Bay is San Mateo, San Francisco, and Marin counties. North Bay is Sonoma, Solano and Napa counties. East Bay is Alameda and Contra Costa counties. South Bay is Santa Clara County from 2001-2004 and Santa Clara and San Benito counties from 2005-2017.

Due to changes in occupation classifications during the analysis period, all occupations have been reassigned to 2010 SOC codes. For pre-2009 reporting years, all employment in occupations that were split into two or more 2010 SOC occupations are assigned to the first 2010 SOC occupation listed in the crosswalk table provided by the Census Bureau. This method assumes these occupations always fall in the same wage category, and sensitivity analysis of this reassignment method shows this is true in most cases.

In order to use OES data for time series analysis, several steps were taken to handle missing wage or employment data. For some occupations, such as airline pilots and flight attendants, no wage information was provided and these were removed from the analysis. Other occupations did not record a median hourly wage (mostly due to irregular work hours) but did record an annual average wage. Nearly all these occupations were in education (i.e. teachers). In this case, a 2080 hour-work year was assumed and [annual average wage/2080] was used as a proxy for median income. Most of these occupations were classified as high-wage, thus dispelling concern of underestimating a median wage for a teaching occupation that requires less than 2080 hours of work a year (equivalent to 12 months fulltime). Finally, the OES has missing employment data for occupations across the time series. To make the employment data comparable between years, gaps in employment data for occupations are ‘filled-in’ using linear interpolation if there are at least two years of employment data found in OES. Occupations with less than two years of employment data were dropped from the analysis. Over 80% of interpolated cells represent missing employment data for just one year in the time series. While this interpolating technique may impact year-over-year comparisons, the long-term trends represented in the analysis generally are accurate.

China Disposable Income per Capita: Upper Middle Income data was reported at 53,359.000 RMB in 2024. This records an increase from the previous number of 50,220.000 RMB for 2023. China Disposable Income per Capita: Upper Middle Income data is updated yearly, averaging 37,850.925 RMB from Dec 2013 (Median) to 2024, with 12 observations. The data reached an all-time high of 53,359.000 RMB in 2024 and a record low of 24,361.249 RMB in 2013. China Disposable Income per Capita: Upper Middle Income data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Income by Income Level.

China Net Income per Capita: Rural Household: Wage: Upper Middle Income data was reported at 4,789.214 RMB in 2012. This records an increase from the previous number of 4,083.704 RMB for 2011. China Net Income per Capita: Rural Household: Wage: Upper Middle Income data is updated yearly, averaging 2,141.959 RMB from Dec 2002 (Median) to 2012, with 11 observations. The data reached an all-time high of 4,789.214 RMB in 2012 and a record low of 1,098.310 RMB in 2002. China Net Income per Capita: Rural Household: Wage: Upper Middle Income data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Income and Expenditure by Income Level: Rural.

China Disposable Income per Capita: Urban: Upper Middle Income data was reported at 68,151.000 RMB in 2024. This records an increase from the previous number of 65,430.000 RMB for 2023. China Disposable Income per Capita: Urban: Upper Middle Income data is updated yearly, averaging 11,827.130 RMB from Dec 1985 (Median) to 2024, with 40 observations. The data reached an all-time high of 68,151.000 RMB in 2024 and a record low of 861.960 RMB in 1985. China Disposable Income per Capita: Urban: Upper Middle Income data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Income by Income Level. Since 2013, All households in the sample are grouped, by per capita disposable income of the household, into groups of low income, lower middle income, middle income, upper middle income, and high income, each group consisting of 20%, 20%, 20%, 20%, and 20% of all households respectively.

China Net Income per Capita: Rural Household: Upper Middle Income data was reported at 11,373.030 RMB in 2013. This records an increase from the previous number of 10,142.080 RMB for 2012. China Net Income per Capita: Rural Household: Upper Middle Income data is updated yearly, averaging 4,788.186 RMB from Dec 2000 (Median) to 2013, with 14 observations. The data reached an all-time high of 11,373.030 RMB in 2013 and a record low of 2,767.000 RMB in 2000. China Net Income per Capita: Rural Household: Upper Middle Income data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Income and Expenditure by Income Level: Rural.

China Consumption Exp per Capita: Urban: Upper Middle Income: Miscellaneous data was reported at 800.350 RMB in 2012. This records an increase from the previous number of 672.430 RMB for 2011. China Consumption Exp per Capita: Urban: Upper Middle Income: Miscellaneous data is updated yearly, averaging 278.590 RMB from Dec 1985 (Median) to 2012, with 23 observations. The data reached an all-time high of 800.350 RMB in 2012 and a record low of 36.590 RMB in 1985. China Consumption Exp per Capita: Urban: Upper Middle Income: Miscellaneous data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Consumption Structure by Income Level: Urban.

Graph and download economic data for Real Median Personal Income in the United States (MEPAINUSA672N) from 1974 to 2023 about personal income, personal, median, income, real, and USA.

China Consumption Exp per Capita: Urban: Upper Middle Income: Household Facility, Article & Service data was reported at 1,346.210 RMB in 2012. This records an increase from the previous number of 1,277.090 RMB for 2011. China Consumption Exp per Capita: Urban: Upper Middle Income: Household Facility, Article & Service data is updated yearly, averaging 472.550 RMB from Dec 1985 (Median) to 2012, with 23 observations. The data reached an all-time high of 1,346.210 RMB in 2012 and a record low of 96.360 RMB in 1985. China Consumption Exp per Capita: Urban: Upper Middle Income: Household Facility, Article & Service data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Consumption Structure by Income Level: Urban.

This dataset contains replication files for "The Fading American Dream: Trends in Absolute Income Mobility Since 1940" by Raj Chetty, David Grusky, Maximilian Hell, Nathaniel Hendren, Robert Manduca, and Jimmy Narang. For more information, see https://opportunityinsights.org/paper/the-fading-american-dream/. A summary of the related publication follows. One of the defining features of the “American Dream” is the ideal that children have a higher standard of living than their parents. We assess whether the U.S. is living up to this ideal by estimating rates of “absolute income mobility” – the fraction of children who earn more than their parents – since 1940. We measure absolute mobility by comparing children’s household incomes at age 30 (adjusted for inflation using the Consumer Price Index) with their parents’ household incomes at age 30. We find that rates of absolute mobility have fallen from approximately 90% for children born in 1940 to 50% for children born in the 1980s. Absolute income mobility has fallen across the entire income distribution, with the largest declines for families in the middle class. These findings are unaffected by using alternative price indices to adjust for inflation, accounting for taxes and transfers, measuring income at later ages, and adjusting for changes in household size. Absolute mobility fell in all 50 states, although the rate of decline varied, with the largest declines concentrated in states in the industrial Midwest, such as Michigan and Illinois. The decline in absolute mobility is especially steep – from 95% for children born in 1940 to 41% for children born in 1984 – when we compare the sons’ earnings to their fathers’ earnings. Why have rates of upward income mobility fallen so sharply over the past half-century? There have been two important trends that have affected the incomes of children born in the 1980s relative to those born in the 1940s and 1950s: lower Gross Domestic Product (GDP) growth rates and greater inequality in the distribution of growth. We find that most of the decline in absolute mobility is driven by the more unequal distribution of economic growth rather than the slowdown in aggregate growth rates. When we simulate an economy that restores GDP growth to the levels experienced in the 1940s and 1950s but distributes that growth across income groups as it is distributed today, absolute mobility only increases to 62%. In contrast, maintaining GDP at its current level but distributing it more broadly across income groups – at it was distributed for children born in the 1940s – would increase absolute mobility to 80%, thereby reversing more than two-thirds of the decline in absolute mobility. These findings show that higher growth rates alone are insufficient to restore absolute mobility to the levels experienced in mid-century America. Under the current distribution of GDP, we would need real GDP growth rates above 6% per year to return to rates of absolute mobility in the 1940s. Intuitively, because a large fraction of GDP goes to a small fraction of high-income households today, higher GDP growth does not substantially increase the number of children who earn more than their parents. Of course, this does not mean that GDP growth does not matter: changing the distribution of growth naturally has smaller effects on absolute mobility when there is very little growth to be distributed. The key point is that increasing absolute mobility substantially would require more broad-based economic growth. We conclude that absolute mobility has declined sharply in America over the past half-century primarily because of the growth in inequality. If one wants to revive the “American Dream” of high rates of absolute mobility, one must have an interest in growth that is shared more broadly across the income distribution.

In the financial year 2021, a majority of Indian households fell under the aspirers category, earning between ******* and ******* Indian rupees a year. On the other hand, about ***** percent of households that same year, accounted for the rich, earning over * million rupees annually. The middle class more than doubled that year compared to ** percent in financial year 2005. Middle-class income group and the COVID-19 pandemic During the COVID-19 pandemic specifically during the lockdown in March 2020, loss of incomes hit the entire household income spectrum. However, research showed the severest affected groups were the upper middle- and middle-class income brackets. In addition, unemployment rates were rampant nationwide that further lead to a dismally low GDP. Despite job recoveries over the last few months, improvement in incomes were insignificant. Economic inequality While India maybe one of the fastest growing economies in the world, it is also one of the most vulnerable and severely afflicted economies in terms of economic inequality. The vast discrepancy between the rich and poor has been prominent since the last ***** decades. The rich continue to grow richer at a faster pace while the impoverished struggle more than ever before to earn a minimum wage. The widening gaps in the economic structure affect women and children the most. This is a call for reinforcement in in the country’s social structure that emphasizes access to quality education and universal healthcare services.

China Cash Expenditure per Capita: Rural Household: Upper Middle Income data was reported at 10,042.948 RMB in 2012. This records an increase from the previous number of 8,826.032 RMB for 2011. China Cash Expenditure per Capita: Rural Household: Upper Middle Income data is updated yearly, averaging 5,018.381 RMB from Dec 2002 (Median) to 2012, with 11 observations. The data reached an all-time high of 10,042.948 RMB in 2012 and a record low of 2,758.310 RMB in 2002. China Cash Expenditure per Capita: Rural Household: Upper Middle Income data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Income and Expenditure by Income Level: Rural.

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Middle Taylor Township, Pennsylvania, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 16,456, while the mean income for the highest quintile (20% of households with the highest income) is 200,443. This indicates that the top earners earn 12 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 301,208, which is 150.27% higher compared to the highest quintile, and 1830.38% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 Middle Taylor township median household income. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Middle Point, OH, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 22,763, while the mean income for the highest quintile (20% of households with the highest income) is 136,393. This indicates that the top earners earn 6 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 173,134, which is 126.94% higher compared to the highest quintile, and 760.59% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 Middle Point median household income. You can refer the same here

For detailed information, visit the Tucson Equity Priority Index StoryMap.Download the layer's data dictionaryNote: This layer is symbolized to display the percentile distribution of the Limited Resources Sub-Index. However, it includes all data for each indicator and sub-index within the citywide census tracts TEPI.What is the Tucson Equity Priority Index (TEPI)?The Tucson Equity Priority Index (TEPI) is a tool that describes the distribution of socially vulnerable demographics. It categorizes the dataset into 5 classes that represent the differing prioritization needs based on the presence of social vulnerability: Low (0-20), Low-Moderate (20-40), Moderate (40-60), Moderate-High (60-80) High (80-100). Each class represents 20% of the dataset’s features in order of their values. The features within the Low (0-20) classification represent the areas that, when compared to all other locations in the study area, have the lowest need for prioritization, as they tend to have less socially vulnerable demographics. The features that fall into the High (80-100) classification represent the 20% of locations in the dataset that have the greatest need for prioritization, as they tend to have the highest proportions of socially vulnerable demographics. How is social vulnerability measured?The Tucson Equity Priority Index (TEPI) examines the proportion of vulnerability per feature using 11 demographic indicators:Income Below Poverty: Households with income at or below the federal poverty level (FPL), which in 2023 was $14,500 for an individual and $30,000 for a family of fourUnemployment: Measured as the percentage of unemployed persons in the civilian labor forceHousing Cost Burdened: Homeowners who spend more than 30% of their income on housing expenses, including mortgage, maintenance, and taxesRenter Cost Burdened: Renters who spend more than 30% of their income on rentNo Health Insurance: Those without private health insurance, Medicare, Medicaid, or any other plan or programNo Vehicle Access: Households without automobile, van, or truck accessHigh School Education or Less: Those highest level of educational attainment is a High School diploma, equivalency, or lessLimited English Ability: Those whose ability to speak English is "Less Than Well."People of Color: Those who identify as anything other than Non-Hispanic White Disability: Households with one or more physical or cognitive disabilities Age: Groups that tend to have higher levels of vulnerability, including children (those below 18), and seniors (those 65 and older)An overall percentile value is calculated for each feature based on the total proportion of the above indicators in each area. How are the variables combined?These indicators are divided into two main categories that we call Thematic Indices: Economic and Personal Characteristics. The two thematic indices are further divided into five sub-indices called Tier-2 Sub-Indices. Each Tier-2 Sub-Index contains 2-3 indicators. Indicators are the datasets used to measure vulnerability within each sub-index. The variables for each feature are re-scaled using the percentile normalization method, which converts them to the same scale using values between 0 to 100. The variables are then combined first into each of the five Tier-2 Sub-Indices, then the Thematic Indices, then the overall TEPI using the mean aggregation method and equal weighting. The resulting dataset is then divided into the five classes, where:High Vulnerability (80-100%): Representing the top classification, this category includes the highest 20% of regions that are the most socially vulnerable. These areas require the most focused attention. Moderate-High Vulnerability (60-80%): This upper-middle classification includes areas with higher levels of vulnerability compared to the median. While not the highest, these areas are more vulnerable than a majority of the dataset and should be considered for targeted interventions. Moderate Vulnerability (40-60%): Representing the middle or median quintile, this category includes areas of average vulnerability. These areas may show a balanced mix of high and low vulnerability. Detailed examination of specific indicators is recommended to understand the nuanced needs of these areas. Low-Moderate Vulnerability (20-40%): Falling into the lower-middle classification, this range includes areas that are less vulnerable than most but may still exhibit certain vulnerable characteristics. These areas typically have a mix of lower and higher indicators, with the lower values predominating. Low Vulnerability (0-20%): This category represents the bottom classification, encompassing the lowest 20% of data points. Areas in this range are the least vulnerable, making them the most resilient compared to all other features in the dataset.

China Consumption Exp per Capita: Urban: Upper Middle Income data was reported at 19,830.170 RMB in 2012. This records an increase from the previous number of 18,160.910 RMB for 2011. China Consumption Exp per Capita: Urban: Upper Middle Income data is updated yearly, averaging 5,163.985 RMB from Dec 1985 (Median) to 2012, with 28 observations. The data reached an all-time high of 19,830.170 RMB in 2012 and a record low of 830.280 RMB in 1985. China Consumption Exp per Capita: Urban: Upper Middle Income data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Income and Expenditure by Income Level: Urban.

China Consumption Exp per Capita: Urban: Upper Middle Income: Food data was reported at 7,102.410 RMB in 2012. This records an increase from the previous number of 6,515.040 RMB for 2011. China Consumption Exp per Capita: Urban: Upper Middle Income: Food data is updated yearly, averaging 2,272.110 RMB from Dec 1985 (Median) to 2012, with 23 observations. The data reached an all-time high of 7,102.410 RMB in 2012 and a record low of 430.800 RMB in 1985. China Consumption Exp per Capita: Urban: Upper Middle Income: Food data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Consumption Structure by Income Level: Urban.