Between 1914 and 1969, weekly wages in manufacturing industries in the United States grew by a factor of 12. In the first half of the century, the most significant periods of increase came during the World Wars, as manufacturing industries were at the core of the war effort. However, wages then fell sharply after both World Wars, due to post-war recessions and oversaturation of the job market as soldiers returned home. Interwar period Wage growth during the interwar period was often stagnant, despite the significant economic growth during the Roarin' 20s, and manufacturing wages remained steady at around 24 dollars from 1923 to 1929. This was, again, due to oversaturation of the job market, as employment in the agricultural sector declined due to mechanization and many rural workers flocked to industrial cities in search of employment. The Great Depression then saw the largest and most prolonged period of decline in manufacturing wages. From September 1929 to March 1933, weekly wages fell from 24 dollars to below 15 dollars, and it would take another four years for them to return to pre-Depression levels. Postwar prosperity After the 1945 Recession, the decades that followed the Second World War then saw consistent growth in manufacturing wages in almost every year, as the U.S. cemented itself as the foremost economic power in the world. This period is sometimes referred to as the Golden Age of Capitalism, and the U.S. strengthened its economic presence in Western Europe and other OECD countries, while expanding its political and military presence across Asia. Manufacturing and exports played a major role in the U.S.' economic growth in this period, and wages grew from roughly 40 dollars per week in 1945 to more than 120 dollars by the late 1960s.

Graph and download economic data for Real Median Family Income in the United States (MEFAINUSA672N) from 1953 to 2024 about family, median, income, real, and USA.

Following the inauguration of Franklin D. Roosevelt, government relief spending increased drastically. In his first year in office, workers in major cities were receiving benefits equal to just over one-fifth of average manufacturing wages. By 1936, relief benefits had risen to over two-fifths of the value of manufacturing wages - this also coincided with a wage increase from around 17 U.S. dollars per week in 1933 to 23 U.S. dollars in 1936, which means that the total value of relief benefits more than doubled in these years.

Annual: at Current Prices: Gross National Product, Annual: at Current Prices: Net National Product, Annual: at Current Prices: Personal Disposable Income, Annual: at Current Prices: Personal Consumption Expenditure, Annual: at 1934～1936 Prices: Gross National Product, Annual: at 1934～1936 Prices: Net National Product, Annual: at 1934～1936 Prices: Personal Consumption Expenditure, Seven-year Moving Average: at 1934～1936 Prices: Gross National Product, Seven-year Moving Average: at 1934～1936 Prices: Net National Product, Seven-year Moving Average: at 1934～1936 Prices: Personal Consumption Expenditure

The Data-compilation is a selection of time-series on wage- and salary development as well as on the development of the national income in Germany from 1850 to 1985. The following studies has been included: - Walther G. Hoffmann (1965): Das Wachstum der deutschen Wirtschaft seit der Mitte des 19. Jahrhunderts.- Rüdiger Hohls (1991): Arbeit und Verdienst. Entwicklung und Struktur der Arbeitseinkommen im Deutschen Reich und in der Bundesrepublik.- Pierenkemper, Toni (1987): Arbeitsmarkt und Angestellte im deutschen Kaiserreich 1880-1913. Interessen und Strategien als Elemente der Integration eines segmentierten Arbeitsmarktes.- Wiegand, Erich/Zapf, Wolfgang (1982): Wandel der Lebensbedingungen in Deutschland. Wohlfahrtsentwicklung seit der Industrialisierung. Tables in ZA-Online-Database HISTAT: A. Hoffmann, Walther G.: The Growth of the German Economy since the mid of the 19th centuryA.1 The average earned income per annum by industrial sector (1850-1959)A.2 The average earned income per annum in mining and saline (1850-1959)A.3 The average earned income per annum in industry and craft (1850-1959)A.4 The average earned income per annum in transport (1850-1959)A.5 The average earned income per annum in other services (1850-1959)A.6 Net national product (NNP) in factor costs in current prices and national income per capita according to Hoffmann (1850-1959)A.7 Gross value added and real national income per capita in prices of 1913 according to Hoffmann (1850-1959)A.8 The development of average earned income of employees in industry and craft, Index 1913 = 100 (1850-1959) B. Hohls, Rüdiger: The Sectoral Structure of Earnings in GermanyB.1 Nominal annual earnings of employees by industrial sector in Germany in Mark, 1885-1985B.2 Nominal earnings of white collar workers and blue collar workers in Germany, 1890-1940 C. Living costs, prices and earnings, consumer price indexC.1 Development of living costs (index) of medium employees’ households (1924-1978)C.2 Preices and earnings, index 1962 = 100 (1820-2001)C.3 Living costs, consumer price index (1820-2001) D. Pierenkemper, Toni: Employment market and employees in the German ‘Reich’ 1880-1913.D.1 Income of selected white collar categories in Mark (1880-1913)D.2 Real income of selected white collar categories (1880-1913) E. Wiegand, E.: Historical Development of Wages and Living Costs in Germany.E.1 Development of real gross income of blue collar workers in industry, index 1970 = 100 (1925-1978)E.2 Development of real gross income of blue collar workers in industry (1925-1978)E.3 Development of nominal and real national income per capita (1950-1978) E.4 Development of nominal and real national income per capita (1925-1939)E.5 National income: monthly income from dependent personal services per employee (1925-1971)E.6 Overlook: Development of wages, employed workers and gross income from dependent personal services in Germany (1810-1989)

The Bureau of Health Professions Area Resource File is a county-based data file summarizing secondary data from a wide variety of sources into a single file to facilitate health analysis. The file contains over 6,000 data elements for all counties in the United States with the exception of Alaska, for which there is a state total, and certain independent cities that have been combined into their appropriate counties. The data elements include: (1) County descriptor codes (name, FIPS, HSA, PSRO, SMSA, SEA, BEA, city size, P/MSA, Census Contiguous County, shortage area designation, etc.), (2) Health professions data (number of professionals registered as M.D., D.O., DDS, R.N., L.P.N., veterinarian, pharmacist, optometrist, podiatrist, and dental hygienist), (3) Health facility data (hospital size, type, utilization, staffing and services, and nursing home data), (4) Population data (size, composition, employment, housing, morbidity, natality, mortality by cause, by sex and race, and by age, and crime data), (5) Health Professions Training data (training programs, enrollments, and graduates by type), (6) Expenditure data (hospital expenditures, Medicare enrollments and reimbursements, and Medicare prevailing charge data), (7) Economic data (total, per capita, and median income, income distribution, and AFDC recipients), and (8) Environment data (land area, large animal population, elevation, latitude and longitude of population centroid, water hardness index, and climate data).

This table contains 11 series, with data for years 1926 - 1960 (not all combinations necessarily have data for all years), and was last released on 2009-01-21. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Income-based estimates (11 items: Gross domestic product (GDP) at market prices; Net domestic income at factor cost; Wages; salaries and supplementary labour income; Corporation profits before taxes ...).

In an impressive increase from years past, 40.1 percent of women in the United States had completed four years or more of college in 2024. This figure is up from 3.8 percent of women in 1940. A significant increase can also be seen in males, with 37.1 percent of the U.S. male population having completed four years or more of college in 2024, up from 5.5 percent in 1940. 4- and 2-year colleges In the United States, college students are able to choose between attending a 2-year postsecondary program and a 4-year postsecondary program. Generally, attending a 2-year program results in an Associate’s Degree, and 4-year programs result in a Bachelor’s Degree. Many 2-year programs are designed so that attendees can transfer to a college or university offering a 4-year program upon completing their Associate’s. Completion of a 4-year program is the generally accepted standard for entry-level positions when looking for a job. Earnings after college Factors such as gender, degree achieved, and the level of postsecondary education can have an impact on employment and earnings later in life. Some Bachelor’s degrees continue to attract more male students than female, particularly in STEM fields, while liberal arts degrees such as education, languages and literatures, and communication tend to see higher female attendance. All of these factors have an impact on earnings after college, and despite nearly the same rate of attendance within the American population between males and females, men with a Bachelor’s Degree continue to have higher weekly earnings on average than their female counterparts.

Graph and download economic data for Average Hourly Earnings, Cotton Manufacturing for United States (M08143USM052NNBR) from Jun 1920 to Oct 1940 about cotton, earnings, hours, manufacturing, and USA.

The researcher made in this study an attempt to give a recapitulatory description of the financing of both World Wars. Therefore according to a given definition of war expenditures only those expenditures were taken into account which arose during the war and which were accounted for in the national budget of the German Empire (Reichshaushalt).The so called post-war burdens, which arose after the respective ceasefire, were not taken into account.By comparing the financing methods of both wars, an insight into the background and the respective characteristics (caused by the different economic systems and economic conditions) shall be enabled. List of data tables (time series data) in the search and download system HISTAT (www.histat.gesis.org): A. The development of military spending in Germany A.1 Total and military expenditure of the German Reich in million marks (1910-1919)A.2 The monthly war expenditure in million marks (1914-1919)A.3 Total expenditure and military expenditure of the German Empire (1933-1945)A.4 Expenditure in the national budget in billions of RM (ordinary and special accounts) (1937/38-1944/45)A.5 total expenditure and expenditure on debt servicing (1914-1919, 1938-45) B. The funding in both wars through tax policies B.1 The most important tax revenue in million marks (1913-1918)B.2 The development of the customs revenue in Germany, England and France in millions of currency units of each country (1913-1918)B.3 The main groups of ordinary income (1913-1918)B.4 The total expenditure, the ordinary revenues, the need for extraordinary covering funds and the borrowing in million marks (1914-1918)B.5 actual revenue of the empire from taxes and customs duties in million RM (1933/34-1943/44)B.6 total expenditure, total revenue and revenue from taxes and customs duties (1938-1945)B.7 national budget in billions of RM (ordinary and extraordinary income) (1938/39-1944/45)B.8 Floating debt of the German Empire from discounted treasury bills, compared with the proceeds of war bonds (1914-1919) C. The funding in both wars by credit policy C.1 Results of the nine German war bonds in million marks (1914-1918)C.2 National debt in million marks (1900-1920)C.3 The bank deposits in billion RM in Germany (1940-1944)C.4 Public and private indebtedness in Germany (1932-1944)C.5A Total outstanding volume of debt securities in million RM (1933-1940)C.5B Total outstanding volume of debt securities in million RM (1933-1940)C.6 National debt in billions of RM (1933/34, 1937/38-1944/45)C.7 The internal debt of various countries (1939-1944)C.8 Development of public debt in various countries in millions of currency units of the country (1913/1920, 1939/45) D. Financing in both wars by monetary policy D.1 The Reichsbank, billion marks (1913-1918)D.2 Coverage of circulation of the Reichsbank and of the foreign funds as annual average in percent (1914-1919)D.3 Circulation in million RM (1928-1945)D.4 The sight liabilities of the Reichsbank in million RM (1940-1945) E. Financing in the two wars by the pricing policies and economic measures E.1 International wholesale index numbers as annual averages (1913-1922)E.2 General index of prices as an annual average (1928-1944)E.3 The wholesale price index as an annual average (1928-1944)E.4 Index numbers of commodity prices as an annual average (1928-1944)E.5 Import and export values in billions of German marks (1913-1917) F. The financing of both was from different point of view F.1 consumer goods production (1939-1944)F.2 The monthly Germany´s armaments production in the First and Second World War (1918, 1944/45)F.3 Empire index numbers for the cost of living as an annual average (1928-1944)F.4 Index number of earnings over the year (1928-1944) G. G. Further comparison tables other authors G.1 Defense spending and national income by Blaich (1932-1938)G.2 Share of defense production as a percentage of industrial production in the German Empire (Deutsches Reich) after Rolf Wagenfuhr (1939-1944). Timeseries are downloadable via the online system HISTAT (www.histat.gesis.org).

Comprehensive demographic dataset for Newton, MA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.