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, income, median, real, and USA.

Demobilization following the First World War saw millions of soldiers return to their home countries from the trenches, and in doing so, they brought with them another wave of the deadliest and far-reaching pandemic of all time. As the H1N1 influenza virus, known as the Spanish Flu, spread across the world and infected between one third and a quarter of the global population, it impacted all areas of society. One such impact was on workers' wages, as the labor shortage drove up the demand for skilled workers, which then increased wages. In the United States, wages had already increased due to the shortage of workers caused by the war, however the trend increased further in the two or three years after the war, despite the return of so many personnel from overseas.

In the first fifteen years of the twentieth century, wages across the shown industries had increased gradually and steadily in line with inflation, with the hourly wage in manufacturing increasing from roughly 15 cents per hour to 21 cents per hour in this period. Between 1915 and 1921 or 1921 however, the hourly rate more than doubled across most of these industries, with the hourly wage in manufacturing increasing from 21 cents per hour in 1915 to 56 cents per hour in 1920. Although manufacturing wages were the lowest among those shown here, the trend was similar across even the highest paying trades, with hourly wages in the building trade increasing from 57 cents per hour in 1915 to one dollar and eight cents in 1921. The averages of almost all these trades decreased again in 1922, before plateauing or increasing at a slower rate throughout the late 1920s. Other factors, such as the Wall Street Crash of 1929 and subsequent Great Depression, make comparing this data with wages in later decades more difficult, but it does give some insight into the economic effects of pandemics in history.

Graph and download economic data for Personal income per capita (A792RC0A052NBEA) from 1929 to 2025 about personal income, per capita, personal, income, GDP, and USA.

Compared to Western Europe, the development of average incomes differed between Scandinavia and and East-Central Europe between 1900 and 1950. Over these five decades, income in Scandinavia gradually caught up with the rest of Western Europe, eventually overtaking it by the middle of the century. By contrast, income across East-Central Europe fell further behind the west over this period, falling from 42 percent of the west's rate in 1900 to 37 percent in 1950.

This collection of wage data was published in „Die Geschichte der Lage der Arbeiter in Deutschland von 1789 bis in die Gegenwart“ by Jürgen Kuczynski (volume I and volume II, here quoted after 6th edition, Berlin 1953, 1954). The data contains wage indices of a certain base year and the corresponding wage raw data (hourly wages, weekly wages, annual wages in marks and pfennigs). The wage data is regionally widely spread until the year 1914; it contains single cities as well as bigger regional units. Since 1924 Kuczynski’s surveys rely on the publications of the statistical office. The wage data is ordered by professional groups, industry and agriculture and by certain industrial sectors. Kuczynski’s wage index is mainly based on publications of trade unions and on reports of different chambers of commerce. The weaknesses of the indices are due to the methodological inconsequence and the limited representative status concerning the election of geographical units. Union wages and also actually paid wages are considered in the calculations, like for example daily, weekly and annual wages or layer wages for miners. On the other side important industrial sectors such as the food or the textile sector are not taken into account. Wage data for agriculture relies often on estimations or is calculated with insufficient material. Wages for work at home are not taken into account in the index calculation. There are also problems with the representative status of the index regarding regional units because cities are weighted too important compared with rural regions. Another topic of the survey is the construction of an index of costs of living. For a long time Kuczynski’s index for costs of living was without any concurrence. It was used by different authors without any changes or modifications. The substantial weakness of the index is that for the calculation of the development of the costs of living, it only takes costs of food and rent into account. Prices of food and rent were weighted in the ratio 3 to 1. Kuczynski does not give an explanation for this weighting. Further the certain price indices for food and rent were calculated by the aggregation of incomplete regional price developments.

Data tables in HistatA – Tables for the period from 1800 to1870:A.1 Wage Data (in Mark and Pfennig)A.2 Wage indices, base 1900 = 100A.3 Costs of living and real wages 1900 = 100 B - Tables for the period from 1870 to 1932:B.1 Wage Data (in Mark and Pfennig)B.2 Wage indices, base 1900 = 100B.3 Costs of living and real wages 1900 = 100 C - Tables for the period from 1932 to 1945:C.1 Wage Data (in Mark and Pfennig)C.2 Wage indices, base 1900 = 100C.3 Costs of living and real wages 1932 = 100

Wages in Macedonia increased 8.30 percent in January of 2026 over the same month in the previous year. This dataset provides - Macedonia Real Wage Growth- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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 Calumet Park. The dataset can be utilized to gain insights into gender-based income distribution within the Calumet Park population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Calumet Park, among individuals aged 15 years and older with income, there were 1,900 men and 2,446 women in the workforce. Among them, 810 men were engaged in full-time, year-round employment, while 1,030 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 7.04% fell within the income range of under $24,999, while 0.58% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 15.43% of men in full-time roles earned incomes exceeding $100,000, while 5.83% 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 Calumet Park median household income by race. You can refer the same here

Russia Population: Percent of Total: Household Income per Capita: 14000.1 - 19000 RUB per Month data was reported at 13.800 % in Dec 2018. This records a decrease from the previous number of 14.400 % for Sep 2018. Russia Population: Percent of Total: Household Income per Capita: 14000.1 - 19000 RUB per Month data is updated quarterly, averaging 15.400 % from Dec 2011 (Median) to Dec 2018, with 29 observations. The data reached an all-time high of 16.300 % in Mar 2014 and a record low of 13.800 % in Dec 2018. Russia Population: Percent of Total: Household Income per Capita: 14000.1 - 19000 RUB per Month data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA013: Population by Average Household Income.

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 Plattsburgh town. The dataset can be utilized to gain insights into gender-based income distribution within the Plattsburgh town population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Plattsburgh town, among individuals aged 15 years and older with income, there were 4,521 men and 4,737 women in the workforce. Among them, 2,048 men were engaged in full-time, year-round employment, while 1,900 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 5.08% fell within the income range of under $24,999, while 13.32% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 19.87% of men in full-time roles earned incomes exceeding $100,000, while 13.79% 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 Plattsburgh town median household income by race. You can refer the same here

This collection of wage data was published in „Die Geschichte der Lage der Arbeiter in Deutschland von 1789 bis in die Gegenwart“ by Jürgen Kuczynski (volume I and volume II, here quoted after 6th edition, Berlin 1953, 1954). The data contains wage indices of a certain base year and the corresponding wage raw data (hourly wages, weekly wages, annual wages in marks and pfennigs). The wage data is regionally widely spread until the year 1914; it contains single cities as well as bigger regional units. Since 1924 Kuczynski’s surveys rely on the publications of the statistical office. The wage data is ordered by professional groups, industry and agriculture and by certain industrial sectors. Kuczynski’s wage index is mainly based on publications of trade unions and on reports of different chambers of commerce. The weaknesses of the indices are due to the methodological inconsequence and the limited representative status concerning the election of geographical units. Union wages and also actually paid wages are considered in the calculations, like for example daily, weekly and annual wages or layer wages for miners. On the other side important industrial sectors such as the food or the textile sector are not taken into account. Wage data for agriculture relies often on estimations or is calculated with insufficient material. Wages for work at home are not taken into account in the index calculation. There are also problems with the representative status of the index regarding regional units because cities are weighted too important compared with rural regions. Another topic of the survey is the construction of an index of costs of living. For a long time Kuczynski’s index for costs of living was without any concurrence. It was used by different authors without any changes or modifications. The substantial weakness of the index is that for the calculation of the development of the costs of living, it only takes costs of food and rent into account. Prices of food and rent were weighted in the ratio 3 to 1. Kuczynski does not give an explanation for this weighting. Further the certain price indices for food and rent were calculated by the aggregation of incomplete regional price developments.

Data tables in Histat A – Tables for the period from 1800 to1870: A.1 Wage Data (in Mark and Pfennig) A.2 Wage indices, base 1900 = 100 A.3 Costs of living and real wages 1900 = 100

B - Tables for the period from 1870 to 1932: B.1 Wage Data (in Mark and Pfennig) B.2 Wage indices, base 1900 = 100 B.3 Costs of living and real wages 1900 = 100

C - Tables for the period from 1932 to 1945: C.1 Wage Data (in Mark and Pfennig) C.2 Wage indices, base 1900 = 100 C.3 Costs of living and real wages 1932 = 100

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 Kalkaska County. The dataset can be utilized to gain insights into gender-based income distribution within the Kalkaska County population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Kalkaska County, among individuals aged 15 years and older with income, there were 6,965 men and 6,369 women in the workforce. Among them, 3,127 men were engaged in full-time, year-round employment, while 1,900 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 15.70% fell within the income range of under $24,999, while 18.84% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 11.74% of men in full-time roles earned incomes exceeding $100,000, while 2.79% 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)

https://i.neilsberg.com/ch/kalkaska-county-mi-income-distribution-by-gender-and-employment-type.jpeg" alt="Kalkaska County, MI gender and employment-based income distribution analysis (Ages 15+)">

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:

• $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 Kalkaska County median household income by gender. You can refer the same here

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 Seagoville. The dataset can be utilized to gain insights into gender-based income distribution within the Seagoville population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Seagoville, among individuals aged 15 years and older with income, there were 6,575 men and 4,511 women in the workforce. Among them, 3,588 men were engaged in full-time, year-round employment, while 1,900 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 18.84% fell within the income range of under $24,999, while 16.68% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 5.04% of men in full-time roles earned incomes exceeding $100,000, while 7.21% 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)

https://i.neilsberg.com/ch/seagoville-tx-income-distribution-by-gender-and-employment-type.jpeg" alt="Seagoville, TX gender and employment-based income distribution analysis (Ages 15+)">

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:

• $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 Seagoville median household income by gender. You can refer the same here

This spreadsheet contains our most complete series of income/expenditure data for Royal Society publishing, 1880-2010. It shows the income from sales and from grants; the expenditure on printing, distribution and other costs, for the Transactions and for the Proceedings; it provides calculations of surplus/deficit and expense recovery rate (which, given the nature of RS publishing in this period, is a more useful measure than expressing surplus as % of sales income). A variety of graphs are included, some of which appeared in my 2022 article 'From philanthropy to business' https://royalsocietypublishing.org/doi/full/10.1098/rsnr.2022.0021 Sources of Data: Data for 1880-1899 come from the series of financial ledgers and annual balance sheets in the Royal Society archives. They do not distinguish between costs/income for Transactions or Proceedings

From 1900 onwards, the main run of income/expenditure data comes from the published annual accounts of the Royal Society (in the Year Book until 1999; and thereafter in the separately-published Trustees' Report). For certain years, e.g. in the mid-20thC, it has been possible to supplement this with more detailed breakdowns from the archival series.

The data available become less detailed over time. Cost breakdowns for paper/printing/illustrations etc are only available up to 1966. Income/expenditure breakdowns by journal (i.e. Proceedings/Transactions/other) are only availble until 2005. Salary and overhead costs are only sometimes available.
Inconsistencies There are various inconsistencies to be aware of:

1. The Society changed its accounting year occasionally. This spreadsheet reports the results for whichever accounting year the Society was using at the time, and so users should be aware of moments of transition. Traditionally, the Society's accounting year had ended on its anniversary day (30 November). In 1939, it moved to a year-end of 30 Sept (so, 1939 figures are for an 11-month 'year'). In 1968, it moved to a year-end of 31 Aug (so, 1968 figures are for a 11-month 'year'). In 1991, it adopted a year-end of 31 March (so, 1991 figures are for a 7-month 'year'). And, by c.2004, the Publishing Team was reporting internally by Calendar Year, even though RS officially still kept a March financial year...

2. Decimalisation in 1971

   1. Staff/overhead costs were sometimes included in the publication account, and sometimes not. Staff costs WERE included from 1936-55 inclusive; and again from 1980 (though staff costs from mid-1970s can be identified from the archives). After 2000, publishing staff costs were often included in a bigger category of 'trading costs' and can't be separated easily via the 'annual accounts' (but can be identified in JT analysis and ST spreadsheet)
      Grants Income From 1910 to 1957, the figure reported publicly for publications income included income from grants/donations to support publications, as well as sales income. There had been grants income supporting publications from at least 1895, but there is no consistent source showing this. In this spreadsheet, we have retrospectively created a 'publications income (excluding grants)' figure for 1910-57 to allow a more consistent longue duree comparison (though the 1895-1910 period should still be treated with caution)