The analysis of real wages has a long tradition in Germany. The focus of the acquisition is on company wages, on wages of certain branches or for categories of workers as well as on the investigation of long term aggregated nominal and real wages. The study of Ashok V. Desai on the development of real wages in the German Reich between 1871 and 1913 is an important contribution to historical research on wages. The study is innovative and methodically on an exemplary level. But mainly responsible for the upswing in the historical research on wages in the 50s and 60s is an extraordinary publication by Jürgen Kuczynski. “The new historical research on wages in Germany is insolubly connected with Jürgen Kuczynski. In his broad researches the history of wages is only one section among many other themes but it is a very important one can be seen as the core piece of his work.” (Kaufhold, K.H., 1987: Forschungen zur deutschen Preis- und Lohngeschichte (seit 1930). In: Historia Socialis et Oeconomica. Festschrift für Wolfgang Zorn zum 65. Geburtstag. Stuttgart: Franz Steiner Verlag, S, 83). In his first study on long series on nominal and real wages in Germany he used a broad empirical basis and encouraged more research in this area. His weaknesses are methodological inconsistencies and a restricted representativeness. For example he includes tariff wages but also actually paid wages. Some important industries like food or textile industry are not taken into account. Wages in agriculture were often estimated but without enough material necessary for a good estimation. Wages for work at home are not regraded in the calculation of the index. The weight of cities in the calculation of the index is relatively too high compared to rural regions and therefor it leaks regional representativeness.In his study Desai uses the reports of trade associations for the Reich´s insurance office on the persons who are insured in the accident insurance and their wages as a basis for the calculation of annual nominal average wages. Desais focusses on industrial wages because only for them long term series are available. As the insurance premiums are calculated according to the income level the documents of the trade associations can be used for the calculation of an index for wages development. Desais study is also very useful regarding the calculation of a new index for costs of living based the model of a typical worker family. „F. Grumbach and H. König have used the same sources to derive indices of industrial earnings. The main differences between their series and ours are: (a) we have adopted the industrial classification followed by the Reichsversicherungsamt, while Grumbach and König have made larger industrial groups, (b) we have calculated average annual earnings, while they claim to have calculated average daily earnings (i.e. to have adjusted the annual figures for the average number of days worked per year per worker), and (c) they have failed to correct distortions in the original data” (Desai, A.V., 1968: Real Wages in Germany 1871–1913. Oxford. Clarendon Press, S. 4). Register of tables in HISTAT:A. OverviewsA.1 Overview: Different estimations of the real and nominal gross wages in the German Reich, index 1913 = 100 (1871-1913)A.2 Overview: Development of costs of living, index 1913 = 100 (1871-1913)A.3 Overview: Development of nominal and real wages, index 1913=100 (1844-1937) D. Study by Ashok V. DesaiD.01 Different estimations of real wages in the German Reich, index 1895 = 100 (1871-1913)D.02 Annual average wage (1871-1886)D.03 Annual gross wages in chosen production segments (1887-1913)D.04 Annual average wage in industry, transportation and trade (1871-1913)D.05 Construction of an index for costs of living, 1895 = 100 (1871-1913)D.06 Real wages, in constant prices from 1895 (1871-1913)D.07 Wheat prices and prices for wheat bread (1872-1913)D.08 Rye prices and prices for rye bread (1872-1913)D.09 Average export prices by product groups, index 1895 = 100 (1872-1913)D.10 Average import prices by product groups, index 1895 = 100 (1872-1913)D.11 Average export prices, import prices and terms of trade, index 1895 = 100 (1872-1913) O. Study by Thomas J. OrsaghO. Adjusted indices for costs of living and real wages after Orsgah, index 1913 = 100 (1871-1913)

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Iowa County: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 345(5.10%) households where the householder is under 25 years old, 1,930(28.53%) households with a householder aged between 25 and 44 years, 2,560(37.84%) households with a householder aged between 45 and 64 years, and 1,930(28.53%) households where the householder is over 65 years old.
• In Iowa County, the age group of 45 to 64 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

Content

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

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 Iowa County median household income by age. You can refer the same here

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 Wakeman. 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 Wakeman, the median household income for the households where the householder is White increased by $1,930(2.81%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $68,606 in 2013 and $70,536 in 2023.
• Black or African American: As per the U.S. Census Bureau population data, in Wakeman, 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

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 Wakeman.
• 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 Wakeman median household income by race. You can refer the same here

Graph and download economic data for Real average of GDP and GDI (PB0000091A225NBEA) from 1930 to 2024 about GDI, average, income, real, GDP, rate, and USA.

PERIOD: Based on the household survey for September 1926 to August 1927. NOTE: (Value in yen). SOURCE: [Survey by the Statistics Bureau, Imperial Cabinet].

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 Martin. 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 Martin, the median household income for the households where the householder is White decreased by $1,930(4.38%), between 2011 and 2021. The median household income, in 2022 inflation-adjusted dollars, was $44,023 in 2011 and $42,093 in 2021.
• Black or African American: In Martin, the median household income for the households where the householder is Black or African American increased by $17,324(93.58%), between 2011 and 2021. The median household income, in 2022 inflation-adjusted dollars, was $18,513 in 2011 and $35,837 in 2021.
• 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/martin-tn-median-household-income-by-race-trends.jpeg" alt="Martin, TN 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 Martin.
• 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 Martin median household income by race. You can refer the same here

at Current Prices: Gross National Product, at Current Prices: Net National Product, at Current Prices: Net National Product, at Current Prices: Personal Disposable Income, at Current Prices: Personal Consumption Expenditure, at Constant Prices: Gross National Expenditure, at Constant Prices: Private Consumption Expenditure

PERIOD: FY 1921-1930. NOTE: (In yen). SOURCE: [Annual Report of the Ministry of Education].

About this dataset

Context

The dataset illustrates the median household income in Renville County, 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 Renville County increased by $1,930 (3%), 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 7 years and declined for 4 years.

https://i.neilsberg.com/ch/renville-county-mn-median-household-income-trend.jpeg" alt="Renville County, MN 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 Renville County median household income. You can refer the same here

Graph and download economic data for Gross domestic product per capita (A939RC0A052NBEA) from 1929 to 2024 about per capita, GDP, and USA.

Over the first half of the 20th century, the Soviet Union's GDP per capita rose from 1,218 U.S. dollars to 2,8334 U.S. dollars. There was a slight decrease between 1913 and 1929 due to the devastation caused by the First World War and Russian Revolution and the transition to a communist government and socialist economic structure. However, GDP per capita grew over the following three intervals, and the Soviet Union's relative isolation in the 1920s and 1930s meant that it was relatively untouched by the Great Depression in the 1930s. At the end of the recovery period after the Second World War, in 1950, GDP per capita had already exceeded pre-war levels by a significant margin, and the Soviet Union emerged as one of the two global superpowers, alongside the United States.

In the build up to the Second World War, the United States was the major power with the highest gross domestic product (GDP) per capita in the world. In 1938, the United States also had the highest overall GDP in the world, and by a significant margin, however differences in GDP per person were much smaller. Switzerland In terms of countries that played a notable economic role in the war, the neutral country of Switzerland had the highest GDP per capita in the world. A large part of this was due to the strength of Switzerland's financial system. Most major currencies abandoned the gold standard early in the Great Depression, however the Swiss Franc remained tied to it until late 1936. This meant that it was the most stable, freely convertible currency available as the world recovered from the Depression, and other major powers of the time sold large amounts of gold to Swiss banks in order to trade internationally. Switzerland was eventually surrounded on all sides by Axis territories and lived under the constant threat of invasion in the war's early years, however Swiss strategic military planning and economic leverage made an invasion potentially more expensive than it was worth. Switzerland maintained its neutrality throughout the war, trading with both sides, although its financial involvement in the Holocaust remains a point of controversy. Why look at GDP per capita? While overall GDP is a stronger indicator of a state's ability to fund its war effort, GDP per capita is more useful in giving context to a country's economic power in relation to its size and providing an insight into living standards and wealth distribution across societies. For example, Germany and the USSR had fairly similar GDPs in 1938, whereas Germany's per capita GDP was more than double that of the Soviet Union. Germany was much more industrialized and technologically advanced than the USSR, and its citizens generally had a greater quality of life. However these factors did not guarantee victory - the fact that the Soviet Union could better withstand the war of attrition and call upon its larger population to replenish its forces greatly contributed to its eventual victory over Germany in 1945.

Throughout the interwar period, Nazi leaders and propaganda repeatedly put forward the bogus claim that Jews owned up to 20 percent of all wealth in Germany, despite making up fewer than one percent of the population. At this time, Jews were used as a scapegoat for Germany's economic difficulties after the First World War and during the Great Depression, and the Nazis claimed that the Jews were lining their pockets at the expense of "Aryan" Germans. Unfortunately, there are no official figures for Jewish wealth in the 1930s, and emigration tax data only gives an insight into the finances of wealthier Jews. There are, however, a range of estimates from contemporary and more recent sources, which have been used to estimate the real share of German capital that was owned by Jews. Contemporary estimates At various points in the 1930s, the media, statistical office, and central bank all claimed that the combined wealth of German Jews was somewhere between two and 20 billion Reichsmarks (RM). While these three institutions were all state run under the Nazi regime, and despite their uncertainty, some of these estimates are still treated with consideration due to the credentials of the journalists, economists, and statisticians involved. Additionally, these figures were used with the purpose of identifying just how much money the state could take from the Jewish population, therefore it was of interest for the Nazi authorities to ascertain accurate figures, and not inflate estimates for propaganda purposes. Interestingly, the estimates from the Statistical office actually increased from 1933 to 1936, despite the fact that the state had already been seizing Jewish wealth and restricting Jewish business on a large scale since 1933; this has been attributed to the economic impact of the Great Depression. Modern estimates The estimates from Junz and Ritschl were published in 2002 and 2019 respectively, and used some of the contemporary estimates in their investigation, while taking many additional factors into account. These are now some of the most widely-cited estimates on this subject, with estimates of around 8-16 billion RM in 1933, five billion RM in 1936, and 4.4 billion RM in 1938. In Ritschl's 2019 paper, he then goes on to estimate the share of total German wealth owned by Jews; his results show that the Jewish share of private capital was slightly higher than the average, but was still very much in line with their population size.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Big Stone County. The dataset can be utilized to gain insights into gender-based income distribution within the Big Stone County population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Big Stone County, among individuals aged 15 years and older with income, there were 2,007 men and 1,930 women in the workforce. Among them, 892 men were engaged in full-time, year-round employment, while 542 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 5.94% fell within the income range of under $24,999, while 10.52% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 19.39% of men in full-time roles earned incomes exceeding $100,000, while 9.59% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

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.

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 Big Stone County median household income by race. You can refer the same here

Abstract

China Living Standards Survey (CLSS) consists of one household survey and one community (village) survey, conducted in Hebei and Liaoning Provinces (northern and northeast China) in July 1995 and July 1997 respectively. Five villages from each three sample counties of each province were selected (six were selected in Liaoyang County of Liaoning Province because of administrative area change). About 880 farm households were selected from total thirty-one sample villages for the household survey. The same thirty-one villages formed the samples of community survey. This document provides information on the content of different questionnaires, the survey design and implementation, data processing activities, and the different available data sets.

Geographic coverage

The China Living Standards Survey (CLSS) was conducted only in Hebei and Liaoning Provinces (northern and northeast China).

Kind of data

Sample survey data [ssd]

Sampling procedure

The CLSS sample is not a rigorous random sample drawn from a well-defined population. Instead it is only a rough approximation of the rural population in Hebei and Liaoning provinces in Northeastern China. The reason for this is that part of the motivation for the survey was to compare the current conditions with conditions that existed in Hebei and Liaoning in the 1930’s. Because of this, three counties in Hebei and three counties in Liaoning were selected as "primary sampling units" because data had been collected from those six counties by the Japanese occupation government in the 1930’s. Within each of these six counties (xian) five villages (cun) were selected, for an overall total of 30 villages (in fact, an administrative change in one village led to 31 villages being selected). In each county a "main village" was selected that was in fact a village that had been surveyed in the 1930s. Because of the interest in these villages 50 households were selected from each of these six villages (one for each of the six counties). In addition, four other villages were selected in each county. These other villages were not drawn randomly but were selected so as to "represent" variation within the county. Within each of these villages 20 households were selected for interviews. Thus the intended sample size was 780 households, 130 from each county.

Unlike county and village selection, the selection of households within each village was done according to standard sample selection procedures. In each village, a list of all households in the village was obtained from village leaders. An "interval" was calculated as the number of the households in the village divided by the number of households desired for the sample (50 for main villages and 20 for other villages). For the list of households, a random number was drawn between 1 and the interval number. This was used as a starting point. The interval was then added to this number to get a second number, then the interval was added to this second number to get a third number, and so on. The set of numbers produced were the numbers used to select the households, in terms of their order on the list.

In fact, the number of households in the sample is 785, as opposed to 780. Most of this difference is due to a village in which 24 households were interviewed, as opposed to the goal of 20 households

Mode of data collection

Face-to-face [f2f]

Research instrument

Household Questionnaire

The household questionnaire contains sections that collect data on household demographic structure, education, housing conditions, land, agricultural management, household non-agricultural business, household expenditures, gifts, remittances and other income sources, and saving and loans. For some sections (general household information, schooling, housing, gift-exchange, remittance, other income, and credit and savings) the individual designated by the household members as the household head provided responses. For some other sections (farm land, agricultural management, family-run non-farm business, and household consumption expenditure) a member identified as the most knowledgeable provided responses. Identification codes for respondents of different sections indicate who provided the information. In sections where the information collected pertains to individuals (employment), whenever possible, each member of the household was asked to respond for himself or herself, except that parents were allowed to respond for younger children. Therefore, in the case of the employment section it is possible that the information was not provided by the relevant person; variables in this section indicate when this is true.

The household questionnaire was completed in a one-time interview in the summer of 1995. The survey was designed so that more sensitive issues such as credit and savings were discussed near the end. The content of each section is briefly described below.

Section 0 SURVEY INFORMATION

This section mainly summarizes the results of the survey visits. The following information was entered into the computer: whether the survey and the data entry were completed, codes of supervisor’s brief comments on interviewer, data entry operator, and related revising suggestion (e.g., 1. good, 2. revise at office, and 3. re-interview needed). Information about the date of interview, the names of interviewer, supervisor, data enterer, and detail notes of interviewer and supervisor were not entered into the computer.

Section 1 GENERAL HOUSEHOLD INFORMATION

1A HOUSEHOLD STRUCTURE 1B INFORMATION ABOUT THE HOUSEHOLD MEMBERS’ PARENTS 1C INFORMATION ABOUT THE CHILDREN WHO ARE NOT LIVING IN HOME

Section 1A lists the personal id code, sex, relationship to the household head, ethnic group, type of resident permit (agricultural [nongye], non-agricultural [fei nongye], or no resident permit), date of birth, marital status of all people who spent the previous night in that household and for household members who are temporarily away from home. The household head is listed first and receives the personal id code 1. Household members were defined to include “all the people who normally live and eat their meals together in this dwelling.” Those who were absent more than nine of the last twelve months were excluded, except for the head of household. For individuals who are married and whose spouse resides in the household, the personal id number of the spouse is noted. By doing so, information on the spouse can be collected by appropriately merging information from the section 1A and other parts of the survey.

Section 1B collects information on the parents of all household members. For individuals whose parents reside in the household, parents’ personal id numbers are noted, and information can be obtained by appropriately merging information from other parts of the survey. For individuals whose parents do not reside in the household, information is recorded on whether each parent is alive, as well as their schooling and occupation.

Section 1C collects information for children of household members who are not living in home. Children who have died are not included. The information on the name, sex, types of resident permit, age, education level, education cost, reasons not living in home, current living place, and type of job of each such child is recorded.

Section 2 SCHOOLING

In Section 2, information about literacy and numeracy, school attendance, completion, and current enrollment for all household members of preschool age and older. The interpretation of pre-school age appears to have varied, with the result that while education information is available for some children of pre-school age, not all pre-school children were included in this section. But for ages 6 and above information is available for nearly all individuals, so in essence the data on schooling can be said to apply all persons 6 age and above. For those who were enrolled in school at the time of the survey, information was also collected on school attendance, expenses, and scholarships. If applicable, information on serving as an apprentice, technical or professional training was also collected.

Section 3 EMPLOYMENT

3A GENERAL INFORMATION 3B MAJOR NON-FARM JOB IN 1994 3C THE SECOND NON-FARM JOB IN 1994 3D OTHER EMPLOYMENT ACTIVITIES IN 1994 3E SEARCHING FOR NON-FARM JOB 3F PROCESS FOR GETTING MAJOR NON-FARM JOB 3G CORVEE LABOR

All individuals age thirteen and above were asked to respond to the employment activity questions in Section 3. Section 3A collects general information on farm and non-farm employment, such as whether or not the household member worked on household own farm in 1994, when was the last year the member worked on own farm if he/she did not work in 1994, work days and hours during busy season, occupation and sector codes of the major, second, and third non-farm jobs, work days and total income of these non-farm jobs. There is a variable which indicates whether or not the individual responded for himself or herself.

Sections 3B and 3C collect detailed information on the major and the second non-farm job. Information includes number of months worked and which month in 1994 the member worked on these jobs, average works days (or hours) per month (per day), total number of years worked for these jobs by the end of 1994, different components of income, type of employment contracts. Information on employer’s ownership type and location was also collected.

Section 3D collects information on average hours spent doing chores and housework at home every day during non-busy and busy season. The chores refer to cooking, laundry, cleaning, shopping, cutting woods, as well as small-scale farm yard animals raising, for example, pigs or chickens. Large-scale animal

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 Highlanding 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 Highlanding township, the median household income for the households where the householder is White decreased by $1,930(2.67%), between 2011 and 2021. The median household income, in 2022 inflation-adjusted dollars, was $72,189 in 2011 and $70,259 in 2021.
• Black or African American: As per the U.S. Census Bureau population data, in Highlanding 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/highlanding-township-mn-median-household-income-by-race-trends.jpeg" alt="Highlanding 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 Highlanding 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 Highlanding township median household income by race. You can refer the same here

About this dataset

Context

The dataset illustrates the median household income in Dearborn County, 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 Dearborn County increased by $1,930 (2.52%), 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 7 years and declined for 4 years.

https://i.neilsberg.com/ch/dearborn-county-in-median-household-income-trend.jpeg" alt="Dearborn County, IN 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 Dearborn County median household income. You can refer the same here

German Empire; regions of the German Empire; economic regions

In der Studie werden aus verschiedenen Quellen die Wachstumsraten des Durchschnittslohns seit 1850 dargestellt, und zwar- die Wachstumsrate des jährlichen durchschnittlichen Arbeitseinkommens von 1850 bis 1951 (nach Walther G. Hoffmann), - die Wachstumsrate der durchschnittlichen Bruttoarbeitsentgelte nach der Sozialversicherung, - die Wachstumsrate der Brutto-Stundenverdienste der Industriearbeiter und - die Wachstumsrate des durchschnittlichen Lohneinkommens (nach D. Schewe/K. Nordhorn, H.J. Müller und R. Skiba). Die auf der Hoffmannschen Untersuchung fußende Berechnung der Wachstumsrate des durchschnittlichen Lohneinkommens lässt für die Zeit vor 1870 keinen durchgehenden langfristigen Trend erkennen. In den fünfziger Jahren zeigt sich ein deutlicher Anstieg auf fast 4%. Besonders starke Schwankungen nach oben und nach unten ergeben sich in den siebziger Jahren. Mit Beginn der achtziger Jahre setzt dann ein langfristig ansteigender Trend ein. Zwischen 1930 und 1935 weist die Lohnwachstumsrate negative Werte auf. In der Zeit von 1920 bis 1967 kann ein langfristig steigender Trend der Lohnwachstumsrate von der Mitte der dreißiger Jahre bis 1967 festgestellt werden (sieht man einmal von der Krisenzeit um 1930 und die ersten 6 Jahre nach dem Zweiten Weltkrieg ab). In dieser Zeit ist die Lohnwachstumsrate von rund 5% auf rund 8% gestiegen. Die durchschnittliche Wachstumsrate ab 1951 liegt bei rund 7,5%. Themen Daten im Recherche- und Downloadsystem HISTAT: A.1 Die Wachstumsrate der Lohneinkommen im Deutschen Reich und in der Bundesrepublik Deutschland (1850-1959)B.1 Die Entwicklung der Wachstumsrate des durchschnittlichen Lohneinkommens im Deutschen Reich und in der Bundesrepublik Deutschland (1917-1967)B.2 Wachstumsrate der Lohneinkommen in der Bundesrepublik Deutschland (1951-1968) In this studiy a compilation of the average earnings growth rates since 1850 on the basis of different sources is given:the yearly growth-rates of average work-income from 1850 to 1951 (according to Walther G. Hoffmann), the growth rate of average gross compensation, growth rate of gross hourly earnings of industrial workers,growth rate of gross earnings ( according to D. Schewe/K. Nordhorn, H.J. Müller und R. Skiba). Topics Timeseries available via the downloadsystem HISTAT: A.1 Die Wachstumsrate der Lohneinkommen im Deutschen Reich und in der Bundesrepublik Deutschland (1850-1959)B.1 Die Entwicklung der Wachstumsrate des durchschnittlichen Lohneinkommens im Deutschen Reich und in der Bundesrepublik Deutschland (1917-1967)B.2 Wachstumsrate der Lohneinkommen in der Bundesrepublik Deutschland (1951-1968) Sources:

About this dataset

Context

The dataset illustrates the median household income in Citrus Heights, spanning the years from 2010 to 2023, with all figures adjusted to 2023 inflation-adjusted dollars. Based on the latest 2019-2023 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 2023, the median household income for Citrus Heights increased by $1,930 (2.57%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $5,602 (7.68%) between 2010 and 2023.

Analyzing the trend in median household income between the years 2010 and 2023, spanning 13 annual cycles, we observed that median household income, when adjusted for 2023 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 6 years and declined for 7 years.

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 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 0223

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2023
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2023 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 Citrus Heights median household income. You can refer the same here