Reals wage in Germany increased by around 2.5 percent as of the fourth quarter of 2024, while nominal wages grew by close to five percent. In contract to nominal wages, real wages are adjusted for inflation. Among leading developed countries, Germany was among those with the highest average annual wages.

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. Overviews A.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. Desai D.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. Orsagh O. Adjusted indices for costs of living and real wages after Orsgah, index 1913 = 100 (1871-1913)

Over this 23-year period, annual wages in Spain fluctuated greatly, ranging from a low of 29,892 euros in 2006 to a high of approximately 33,253 euros in 2009. The average annual wage stood at approximately 31,945 euros in 2023. Compared to other European countries, Spain ranked fairly low in 2023. The annual salary in the Iberian country was similar to salaries in Italy and Slovenia, but remained far from the figures that were registered in France, Ireland, and Germany. Minimum wage Spain's minimum monthly wage was 1,134 euros as of 2024. Unlike the average annual wage, it has been constantly increasing on a nearly continuous basis since 2008, when the minimum wage was 600 euros per month. In 2019, the Socialist government of Spain passed a law by that increased the national minimum wage by 164 euros, therefore making it stand at 900 euros per month and reflecting the largest increase to date. Along with the monthly wage, the national minimum daily wage has also been raised consistently over the past years. In 2024, the gross minimum was 37.8 euros a day, whereas in 2000 it was 20 euros a day. Unequal pay The average salary in Spain diverges considerably according to different factors. For instance, the gender salary gap remains significant in the Mediterranean country, although it has shrunk in recent years. In 2022, the average salary for a male full-time employee was around nine percent higher than his female counterpart. The gender gap is even wider for permanent positions: that year, average annual salaries for women were roughly 6,000 euros less than average salaries for men. The salary gap is also conspicuous when looking at the wage for workers with disabilities, a gap that has increased in recent years. Geographic location is also important; the average net salary in regions such as Extremadura and the Canary Islands was less than 23,100 euros per year in 2022, far from the salary in the Basque Country and Madrid (32,300 and 31,200 euros, respectively).

The study constructs new series of nominal wages in industry and crafts as well as a new consumer goods price index for the period 1850-1889; the coefficient of the two series gives the real wage. While such information was collected and published by government agencies from the late 1880s onwards, the decades before are part of the pre-statistical age. After all, information is available from municipal authorities, from branches of territorial state authorities and from individual companies. For the construction of a new nominal wage series, the current study refer to Kuczynski´s material (1961/62), supplements it with information from individual studies of the past 50 years, and constructs wage indices for the heavy ironware, machine construction, mining, printing, and municipal construction industries on this basis by means of unbalanced panel regression with fixed effects. Of the 38 individual wage series on which these sector indices are based, 27 come from Kuczynski, the remainder from more recent studies. Wages in the textile sector are represented by those in the cotton industry. The study uses the wage series published by Kirchhain (1977). Weighted according to employment figures, all these sector-specific series (excluding miners´ wages) are aggregated into a Fisher index of nominal wages in industry and crafts. Both this index and the indices at sector level are linked in 1888/89 with the series by Hoffmann (1965); the resulting values denote annual earnings in Marks. The sector indices differ little from those of Kuczynski and Hoffmann despite the expansion of the database and the different methodology of index construction, but the aggregated index shows a stronger growth rate than that of Kuczynski; the latter index is obviously erroneous (Pfister 2018, 576).

The consumer goods price index is based on five sub-indices for (1) food, (2) beverages and luxury foods, (3) rent, (4) furniture, household goods and heating, and (5) clothing. The sub-indices for food and rent are new, the other three are from Hoffmann (1965). Weights are determined for 1848/49 and 1889 on the basis of research literature, values in between are interpolated linearly. Both the sub-index of food prices and the overall index are constructed as Fisher indices.

Both the rental index and the food prices rise more strongly in the long term than the two corresponding Hoffmann indices (Pfister 2018, 578 and 582). Hoffmann constructs the rental price index only indirectly by multiplying the estimated building capital by an assumed interest rate. The rent index of the current study is based on data from three major cities. Only if it is assumed that large cities are completely unrepresentative for the entire real estate market should Hoffmann´s series still be considered.

In the case of food prices, the comparatively stronger long-term increase - compared to previous research - results from the higher weight of prices from the southern parts of the country far from the sea in the new sub-index. Here, the price dampening effect of growing imports of American grain had a weaker effect than in the coastal regions in the north. Thus, one of the main findings of the study is that the assessment of the development of the living standards of urban workers from the 1850s to 1880s strongly depends on how one determines the effect of the first wave of modern globalization on the German price structure. The greater consideration given in this study to food prices in areas distant from the sea results in a more pessimistic view of the development of real wages during this period than has been the case with some previous research.

To the data:

1. individual wage series (table set A.01)

This set of tables contains wage series from six branches at the level of regions, cities, individual enterprises and in one case (cotton industry) an entire branch. Only series containing data for at least 15 years were taken into account. In detail, the series are the following: Heavy Ironware Bochum 1869-1889: Average annual income of the workers of the Bochumer Verein (steelworks) in Mark; Däbritz (1934, Annex Table 4). Essen 1848-1889: Average annual income of the workers of the Krupp works in Mark; Kuczynksi (1961-62, vol. I, 377, vol. II, 227, vol. III, 426). Ruhr 1855-1889: Average annual income of the workers at the blast furnaces in the Ruhr district in Mark; banks (2000, Table A59). Saar 1869-1889: Day wage of workers at the blast furnaces of the Burbach Ironworks in Mark; Kuczynksi (1961-62, vol. III, 426). Silesia 1869-1889: Average annual income of workers at the blast furnaces in Silesia in Mark; banks (2000, Table A59).

Machine construction Augsburg 1851-1889: Average annual income of the workers of the Machine Factory Augsburg in Mark; Vol. II, 227; Kuczynski (1961-62, Vol. III, 426). Chemnitz 1860-1887: Weekly wage of machinists in Mark; Kuczynski (1961-62, vol. II, 227; vol. III, 426). Esslingen 1848-1889: Average annual income of...

Wages In the Euro Area increased 4.10 percent in December of 2024 over the same month in the previous year. This dataset provides the latest reported value for - Euro Area Wage Growth - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The present study aims to estimate the development of employment and wages in Germany based on accident insurance statistics. Data on the number of insured persons allow an estimation of employment by economic groups. Thereby it is important to take the increasing share of insured persons in the entire labor force in consideration. Data from the accident insurance is suitable for wage statistics because besides the values of the earned wages it also contains numbers on the yearly average of employees corrected for the number of working days. The investigation period is from 1888 to 1954 with the exception of the years of war and hyperinflation. The first three years after the introduction of the accident insurance are not taken into account as there are no reliable documents for this period. The analysis is restricted to the economic sectors which were subject to compulsory insurance since the beginning of the investigation period: industry, crafts and traffic. In the sector of traffic extra sources for data on railways were used. The increasing significance of the industrial sector regarding the overall economic employment volume as well as the income generation can be seen looking at the development of the number of employees and wages in relation to the per capita income growth. The industrialization process leads to structural changes in the entire economy which results in a steady relative decline in the agrarian sector. Within the industrial sector most chances and developments are in favor of the industries producing mainly investment goods. This process causes that the growth rates of industrial employment, of average wages and of the wage level primarily depend on those industry groups. Due to these different growth processes within the industrial sector a theoretical differentiation of the wage structure of both groups is necessary because the investment goods industries which has a higher need of expansion need to pay higher wages in order to get the necessary workforce for their expansion. At the beginning of the first world war the wage difference between the two industry groups has increased to 36,5% in 1913 ( it was only 26,5% in 1888). But in the following years there is not such a strong tendency. Probably the increasing power of trade unions caused a consolidation of the “traditional” wage structure. This is also supported by the fact that wage differences between all industries are quite small in the period after the First World War. The increases in real wages during the investigation period are smaller than 100%. This results in a yearly average increase of ca 1%. This is a development of real wages on a significantly lower level compared to other countries such as Sweden, France, Great Britain and The US. A reason for this is the missing real wage increase during the years of war and the first years after the war.

Register of tables in HISTAT: - Working population in thousands with their main profession in Germany (1882-1950) - Employees in Germany (1882-1954) - Index number for costs of living, nominal wages and real wages in Germany (1888-1954) - Development of average wages in the industry groups in the German (1888-1912) - Shares of different industry groups in the total labor force in Germany (1882-1954)

The present study aims to estimate the development of employment and wages in Germany based on accident insurance statistics. Data on the number of insured persons allow an estimation of employment by economic groups. Thereby it is important to take the increasing share of insured persons in the entire labor force in consideration. Data from the accident insurance is suitable for wage statistics because besides the values of the earned wages it also contains numbers on the yearly average of employees corrected for the number of working days. The investigation period is from 1888 to 1954 with the exception of the years of war and hyperinflation. The first three years after the introduction of the accident insurance are not taken into account as there are no reliable documents for this period. The analysis is restricted to the economic sectors which were subject to compulsory insurance since the beginning of the investigation period: industry, crafts and traffic. In the sector of traffic extra sources for data on railways were used. The increasing significance of the industrial sector regarding the overall economic employment volume as well as the income generation can be seen looking at the development of the number of employees and wages in relation to the per capita income growth. The industrialization process leads to structural changes in the entire economy which results in a steady relative decline in the agrarian sector. Within the industrial sector most chances and developments are in favor of the industries producing mainly investment goods. This process causes that the growth rates of industrial employment, of average wages and of the wage level primarily depend on those industry groups. Due to these different growth processes within the industrial sector a theoretical differentiation of the wage structure of both groups is necessary because the investment goods industries which has a higher need of expansion need to pay higher wages in order to get the necessary workforce for their expansion. At the beginning of the first world war the wage difference between the two industry groups has increased to 36,5% in 1913 ( it was only 26,5% in 1888). But in the following years there is not such a strong tendency. Probably the increasing power of trade unions caused a consolidation of the “traditional” wage structure. This is also supported by the fact that wage differences between all industries are quite small in the period after the First World War. The increases in real wages during the investigation period are smaller than 100%. This results in a yearly average increase of ca 1%. This is a development of real wages on a significantly lower level compared to other countries such as Sweden, France, Great Britain and The US. A reason for this is the missing real wage increase during the years of war and the first years after the war. Register of tables in HISTAT: - Working population in thousands with their main profession in Germany (1882-1950)- Employees in Germany (1882-1954)- Index number for costs of living, nominal wages and real wages in Germany (1888-1954)- Development of average wages in the industry groups in the German (1888-1912)- Shares of different industry groups in the total labor force in Germany (1882-1954)

Germany DE: Standardised Price-Income Ratio: sa data was reported at 88.538 Ratio in 2024. This records a decrease from the previous number of 93.578 Ratio for 2023. Germany DE: Standardised Price-Income Ratio: sa data is updated yearly, averaging 95.901 Ratio from Dec 1980 (Median) to 2024, with 45 observations. The data reached an all-time high of 146.141 Ratio in 1981 and a record low of 76.343 Ratio in 2010. Germany DE: Standardised Price-Income Ratio: sa data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Germany – Table DE.OECD.AHPI: House Price Index: Seasonally Adjusted: OECD Member: Annual. Nominal house prices divided by nominal disposable income per head. Net household disposable income is used. The population data come from the OECD national accounts database. The long-term average is calculated over the whole period available when the indicator begins after 1980 or after 1980 if the indicator is longer. This value is used as a reference value. The ratio is calculated by dividing the indicator source on this long-term average, and indexed to a reference value equal to 100.

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)

Net annual earnings for a single earner family with two children in the European Union have increased from 25,434 euros in 2013 to 33,939 euros over the period from 2013 to 2023. Net earnings received a boost during the pandemic years of 2020 and 2021, in spite of gross earnings decreasing in 2020, due to reduced taxes and increased family allowances.

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 century A.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 Germany B.1 Nominal annual earnings of employees by industrial sector in Germany in Mark, 1885-1985 B.2 Nominal earnings of white collar workers and blue collar workers in Germany, 1890-1940

C. Living costs, prices and earnings, consumer price index C.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)

Germany DE: GDP: USD: Gross National Income per Capita: Atlas Method data was reported at 54,800.000 USD in 2023. This records an increase from the previous number of 53,800.000 USD for 2022. Germany DE: GDP: USD: Gross National Income per Capita: Atlas Method data is updated yearly, averaging 24,195.000 USD from Dec 1962 (Median) to 2023, with 62 observations. The data reached an all-time high of 54,800.000 USD in 2023 and a record low of 1,370.000 USD in 1962. Germany DE: GDP: USD: Gross National Income per Capita: Atlas Method data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Gross Domestic Product: Nominal. GNI per capita (formerly GNP per capita) is the gross national income, converted to U.S. dollars using the World Bank Atlas method, divided by the midyear population. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States.;World Bank national accounts data, and OECD National Accounts data files.;Weighted average;

This study emerged in the context of a research project on “historical regional research” carried out between 1981 and 1985 at the central institute for social science research at the Free University of Berlin. It was financed by the free university of Berlin. The members of the research group were social and economic historians from different subject areas at the Free University of Berlin. They all shared the interest for a comparison of historical regions in Germany in the 19th and 20th century. In the opinion of the researchers this comparison was very suitable for the explanation of certain economic development and social movements because it gives a better impression of the importance of conditions and barriers in the different regions compared to the analysis of individual regions. Register of tables in HISTAT: A. Basic economic conditions A.1 Workers and relatives by economic sectors in the German Reich (1882-1907) A.2 Workers in mining, industry and crafts in the German Reich by industry (1875-1913) A.3 Population movement in the German Reich (1870-1914) A.4 Population growth in the Prussian provinces and in larger states of the German Reich (1871-1910)

B. Development of wages B1. Annual nominal average wages in some German regions after Rüdiger Hohls (1891-1914) B.2 Companies, workers and wages by regions and trade associations (1885-1913)

The house price to income ratio in Germany in the first quarter of 2024 declined notably from its peak in 2022. The ratio measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. Germany's index score in the first quarter of 2024 amounted to 109.7, which means that house price growth had outpaced income growth by about 10 percent since 2015. This was below the average house price to income area in the Euro area 16.

Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

The statistic shows the gross domestic product (GDP) per capita in the United States from 1987 to 2024, with projections up until 2030. In 2024, the gross domestic product per capita in the United States amounted to around 85,812.18 U.S. dollars. Thus, the United States is one of the countries with the largest GDP per capita worldwide. See the U.S. GDP growth rate here and the US GDP for further information. For comparison, per capita GDP in China had reached about 5,553 U.S. dollars in 2011. Gross domestic product of the United States The gross domestic product (GDP) of a country is an economic key figure, as it represents the market value of goods and services produced in a country within one year. The United States’ GDP) is increasing consistently, and it is expected to continue growing. On a global scale, the U.S. share of GDP adjusted for Purchasing Power Parity has been in the range of 20 percent over the last few years, give or take a few percentage points. The United States has the largest GDP worldwide, with a significant lead over China, Japan and Germany. Gross domestic product per capita is annual GDP divided by the average population from the same year, which allows for a GDP calculation per inhabitant of a country. Thus, a country with a high GDP, like the United States, can still have a low GDP per capita. Consequently, if compared to other countries, the United States does not rank among the top ten on this list .

France FR: GDP: USD: Gross National Income per Capita: Atlas Method data was reported at 37,970.000 USD in 2017. This records a decrease from the previous number of 38,780.000 USD for 2016. France FR: GDP: USD: Gross National Income per Capita: Atlas Method data is updated yearly, averaging 20,090.000 USD from Dec 1962 (Median) to 2017, with 56 observations. The data reached an all-time high of 44,190.000 USD in 2011 and a record low of 1,560.000 USD in 1962. France FR: GDP: USD: Gross National Income per Capita: Atlas Method data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s France – Table FR.World Bank: Gross Domestic Product: Nominal. GNI per capita (formerly GNP per capita) is the gross national income, converted to U.S. dollars using the World Bank Atlas method, divided by the midyear population. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted Average;

United Kingdom UK: GDP: USD: Gross National Income per Capita: Atlas Method data was reported at 40,530.000 USD in 2017. This records a decrease from the previous number of 42,370.000 USD for 2016. United Kingdom UK: GDP: USD: Gross National Income per Capita: Atlas Method data is updated yearly, averaging 21,055.000 USD from Dec 1970 (Median) to 2017, with 48 observations. The data reached an all-time high of 48,420.000 USD in 2008 and a record low of 2,440.000 USD in 1970. United Kingdom UK: GDP: USD: Gross National Income per Capita: Atlas Method data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s UK – Table UK.World Bank: Gross Domestic Product: Nominal. GNI per capita (formerly GNP per capita) is the gross national income, converted to U.S. dollars using the World Bank Atlas method, divided by the midyear population. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted Average;