Research increasingly stresses the role of human capital in modern economic development. Existing historical evidence -- mostly from British textile industries -- however, rejects that formal education was important for the Industrial Revolution. Our new evidence from technological follower Prussia uses a unique school enrollment and factory employment database linking 334 counties from pre-industrial 1816 to two industrial phases in 1849 and 1882. Using pre-industrial education as instrument for later education and controlling extensively for pre-industrial development, we find that basic education is significantly associated with nontextile industrialization in both phases of the Industrial Revolution. Panel data models with county fixed effects confirm the results. (JEL I20, J24, N13, N33, N63)

Abstract

UNIDO maintains a variety of databases comprising statistics of overall industrial growth, detailed data on business structure and statistics on major indicators of industrial performance by country in the historical time series. Among which is the UNIDO Industrial Statistics Database at the 2-digit level of ISIC (INDSTAT2).

INDSTAT2 is the largest industrial statistics database of its kind. Unlike others presented by different classification standards for different time periods, INDSTAT2 provides data by a single classification standard for more than 40 years, which makes it particularly valuable for long-term structural analysis. It contains time series data on the manufacturing sector for the period 1963 onwards. As with INDSTAT4, INDSTAT2 presents data for seven principle indicators (number of establishments, employment, wages and salaries, output, value added, gross fixed capital formation and number of female employees). The value figures are presented in current prices, in both National Currency and US Dollars. The database includes the index numbers of industrial production, which show the real growth of the volume of production by 2-digit of ISIC Rev. 3. The data are arranged at the 2-digit level of the International Standard Industrial Classification of All Economic Activities (ISIC) Revision 3 pertaining to the manufacturing sector, which comprises 23 industries. The time series can either be used to compare a certain branch or sector of countries or – if present in the data set – some sectors of one country.

For more information, please visit: http://www.unido.org/resources/statistics/statistical-databases.html

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This statistic displays the influence of the Fourth Industrial Revolution on employment in the Netherlands in 2016. Approximately a ** percent of the respondents answered they believe the Fourth Industrial Revolution will lead to fewer jobs in their sector.

This study explores the transformation of women's wage work in New England between 1820 and 1900 with distinct studies of rural outwork, cotton textile manufacturing, boot and shoemaking, domestic service, needle trades, and teaching. Appendices in TRANSFORMING WOMEN'S WORK (Cornell University Press, 1994) discuss in detail how the datasets were constructed. Beginning with either employment or census records, Thomas Dublin employed nominal record linkage to assemble life course data on groups of women workers in Mew England.

Compared with the other major European powers of the 19th and 20th century, France's industrialization was comparatively slow. In global history, the period between 1815 and 1914 is often referred to as the Pax Britannica; a time where Britain emerged as the leading superpower and relations between the Great Powers was very peaceful, allowing many Western European countries to undergo industrial revolutions. In France, however, the term "industrial revolution" was less applicable, as it remained a fairly agricultural society; transportation infrastructure was poor, and large urban centers made their money through banking, shipping, and artisanal production. It was only during the 1840s, where France's railway network developed, that urbanization and industrialization began to take off, but it still lagged behind Britain, Germany, and Belgium for the remainder of the century. The loss of Alsace-Lorraine to Germany in 1871 significantly hindered France's industrialization, as it was one of the country's richest mining regions. Interwar period France then reclaimed Alsace-Lorraine after the First World War, and occupied two of Germany's most industrious areas during the 1920s; the Rhineland was occupied through the Treaty of Versailles until 1930, and the Ruhr region from 1923 to 1925, when Germany defaulted on its reparation payments. Because of this, industrial output was higher in the 1920s than in the 1930s, where it declined due to the Great Depression, withdrawal from Germany, and then the Second World War. Figures are missing from some years due to German occupation, but in 1944, the year of France's liberation, output was almost a third of its pre-war level. Post-war recovery Within three to four years, French industrial output had returned to a similar level as the 1930s, and this marked the beginning of Les Trente Glorieuses (the glorious thirty). These three decades saw uninterrupted economic growth, and industrial output soared. Using 1963 as a benchmark, industrial output doubled from 1951 to 1963, and tripled from 1951 to 1971. France, along with West Germany, played a key role in European integration in this period, which laid the groundwork for the European Union's formation. Industrial growth then came to a halt in the mid-1970s, due to the 1973-75 Recession, at which point the government put safeguards in place to prevent uncontrolled growth in the future.

The first decades of the 21st century are characterized by waves of innovation and by promises of technological breakthroughs. It is a commonplace to claim that we are on the brink of the Fourth Industrial Revolution. It is far from clear, however, what this claim entails: what is the “Fourth Industrial Revolution”? In this article we investigate the content of this label and its changes over time. We employ a co-word analysis of 3455 scientific publications as a method to unravel the changing content of the Fourth Industrial Revolution. The paper shows that the Fourth Industrial Revolution as a label has changed over time, has a high volatility, is inarticulate about society, and absorbs everything that is somewhat promising.

Abstract

UNIDO maintains a variety of databases comprising statistics of overall industrial growth, detailed data on business structure and statistics on major indicators of industrial performance by country in the historical time series. Among which is the UNIDO Industrial Statistics Database at the 3 & 4-digit levels of ISIC Revision 4 (INDSTAT4-Rev.4).

INDSTAT4 contains highly disaggregated data on the manufacturing sector for the period 2005 onwards. Comparability of data over time and across the countries has been the main priority of developing and updating this database. INDSTAT4 offers a unique possibility of in-depth analysis of the structural transformation of economies over time. The database contains seven principle indicators of industrial statistics. The data are arranged at the 3- and 4-digit levels of the International Standard Industrial Classification of All Economic Activities (ISIC) Revision 4 pertaining to the manufacturing, which comprises more than 160 manufacturing sectors and sub-sectors. The time series can either be used to compare a certain branch or sector of countries or – if present in the data set – some sectors of one country.

For more information, please visit: http://www.unido.org/resources/statistics/statistical-databases.html

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How were unskilled workers selected and hired in preindustrial labour markets? We exploit records from the rebuilding of St. Paul’s Cathedral, London (1672–1748) to analyze the hiring and employment history of over one thousand general building labourers, the benchmark category of ‘unskilled’ workers in long-run wage series. Despite volatile demand, St. Paul’s created a stable workforce by rewarding the tenure of long-standing workers. More senior workers received more days of work each month, preference when jobs were scarce, and the opportunity to earn additional income. We find the cathedral’s strategy consistent with reducing hiring frictions and turnover costs.

This statistic presents the market capitalization growth of industries related to the ****** industrial revolution in South Korea from 2006 to 2015, by category. During the period in consideration, the total market capitalization growth of the ****** industrial revolution related areas in South Korea stood at **** percent. The fastest growing sector was the software and service, which increased **** percent for the examined decade.

This includes Tables S1-S6 supporting Jonell, T.N., Jones, P., Lucas, A., Naylor, S., 2024, Limited waterpower contributed to rise of steam power in British ‘Cottonopolis’: Proceedings of the National Academy of Sciences Nexus, doi: 10.1093/pnasnexus/pgae251

Sources: Census of production’s material was conducted from very different sources, such as from Statistical Abstracts for the United Kingdom, and scientific publications.

As appropriate (for example: production censuses were not available), estimates were made.

This is the replication package for the following paper: Railways, Growth, and Industrialization in a Developing German Economy, 1829-1910. The paper studies the average and heterogeneous effects of railway access on parish-level population, income, and industrialization in Württemberg during the Industrial Revolution. The package contains data and code replicating the paper's tables and figures.

Using 600 genotyped DNA samples from Belgium, Denmark, England, Germany and the Netherlands from the eighth to nineteenth century we find a statistically significant positive time trend in educational attainment polygenic index scores. The upward shift in average scores on this genotypic index is large enough to serve as a contributory factor to the Industrial Revolution, consistent with Clark’s hypothesis. The positive trend becomes significant only in the period after 1350 CE.

View Industrial revolution c/o tho import data USA including customs records, shipments, HS codes, suppliers, buyer details & company profile at Seair Exim.

Replication Data for: "People and Machines: A Look at the Evolving Relationship between Capital and Skill in Manufacturing, 1860-1930, Using Immigration Shocks"

Blockchain data query: Colonization missions - Industrial Revolution

The data and programs replicate tables and figures from "Enlightenment Ideals and Belief in Progress in the Run-up to the Industrial Revolution: A Textual Analysis," by Almelhem, Iyigun, Kennedy, and Rubin. Please see the README file for additional details.

In 2023, global carbon dioxide emissions from fossil fuel combustion and industrial processes reached a record high of 37.8 billion metric tons (GtCO₂). Global CO₂ emissions are projected to have reached record levels in 2024. The world has pumped more than 1,800 GtCO₂ into the atmosphere since the industrial revolution began, though almost 45 percent has been produced since 2000. What is carbon dioxide? CO₂ is a colorless, naturally occurring gas that is released after people and animals inhale oxygen. It is a greenhouse gas, meaning it absorbs and releases thermal radiation which in turn creates the “greenhouse effect”. In addition to other greenhouse gases, CO₂ is also a major contributor to the ability of the Earth to maintain a habitable temperature. Without CO₂ and other greenhouse gases, Earth would be too cold to live on. However, while CO₂ alone is not a harmful gas, the abundance of it is what causes climate change. The increased use of electricity, transportation, and deforestation in human society have resulted in the increased emissions of CO₂, which in turn has seen a rise in earth’s temperature. In fact, around 70 percent of global warming since 1851 is attributable to CO₂ emissions from human activities. Who are the largest emitters worldwide? China is the biggest carbon polluter worldwide, having released almost 12 GtCO₂ in 2023. This was more than the combined emissions of the United States and India, the second and third-largest emitters that year, respectively.

Although it was not a united country until 1871, industrialization across Germany began in the early 1800s, and it quickly saw Germany emerge as a Great Power in Europe. German industrialization was largely driven by coal and steel production, of which Germany had rich deposits, and these were used in construction and infrastructure to modernize the country. The mechanization of agriculture also fed into this, as many people from rural regions flocked to cities in search of work. Many of the coal and iron deposits were located in Germany's west, particularly around the Rhine and Ruhr regions, and industry here benefitted from strong rail and water transport networks. Today, with over five million inhabitants, the Ruhr region is the most populous metropolitan area in the country, largely due to these developments. While Germany was among the most advanced nations in the world by the end of the 19th century, industrial output grew higher still in the 20th; between 1896 and 1913, industrial output in Germany doubled. Interwar turmoil After the First World War, Germany lost its resource rich territories of Alsace-Lorraine and the Saarland, while the Rhine and Ruhr regions were also occupied by France, and much of its industrial output was sent to other countries as war reparations. Hyperinflation in 1923 also saw the collapse of the German economy, and it was not until the late-1920s that economic recovery from the war truly began, although this was also short-lived. As Germany had been dependent on financial aid from the U.S. in order to recover and meet its reparation payments, the Great Depression in the U.S. had dire consequences for the German economy. From 1929 until 1932, industrial output fell once more, and many historians point to this economic difficulty as a catalyst for the rise of nationalism and fascism in Germany. The Nazi Party then ascended to power in 1933, the year the Depression ended, and the economy was restructured to support a war of expansion. Among other factors, this involved tax breaks for large businesses, allowing cartels to control local business, increasing average working hours, and prioritizing industrial employment by importing food from the east. The strength of Germany's industry then allowed the Axis powers to take control of most of Europe during the Second World War, but it was ultimately defeated by 1945. Post-war split Following the war, Germany was split into two separate states; commonly referred to as East and West Germany. The west was a liberal democracy with a free-market economy, while the east was a communist state with a command economy, yet both became leaders in their respective trading blocks during the Cold War. When looking at industrial growth over the next three decades, using output in 1963 as a benchmark, East Germany's output grew over nine times larger from 1949 to 1975, whereas West Germany's grew by a factor of six. It is important to remember, however, that the west was larger, more populous, and starting from a more industrially developed point than the east, therefore it was consistently more advanced. The West also had fewer restrictions placed on it from other nations after the war, and it played a leading role in European integration; whereas the East was influenced more heavily by the USSR and it had less trade with other advanced nations, which hindered its technological development. West Germany's output took a hit in the 1970s due to the 1973-1975 Recession, whereas the East's economy was protected as it had little trade with the U.S. and its partners. However, the West quickly recovered and economic stagnation in the East throughout the 1980s would contribute to the eventual collapse of the Eastern Bloc, and Germany was officially reunified in 1990.

This dataset contains three spreadsheets. The first spreadsheet consists of the three variables I use for the main regression of tea, hours and wages. The second spreadsheet has the variables that I use for my robustness check with consumption instead of tea imports. Last spreadsheet has the preliminary regression variables with real prices and quantity of tea imports. I use this regression to predict the non smuggling quantity of tea.