The Gross Domestic Product (GDP) in China expanded 1.10 percent in the second quarter of 2025 over the previous quarter. This dataset provides - China GDP Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The China agricultural and economic database is a collection of agricultural-related data from official statistical publications of the People's Republic of China. Analysts and policy professionals around the world need information about the rapidly changing Chinese economy, but statistics are often published only in China and sometimes only in Chinese-language publications. This product assembles a wide variety of data items covering agricultural production, inputs, prices, food consumption, output of industrial products relevant to the agricultural sector, and macroeconomic data.

This dataset is about book series. It has 1 row and is filtered where the books is How the Chinese economy works : a multiregional overview. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This dataset is about books. It has 9 rows and is filtered where the book subjects is China-Economic conditions-Regional disparities. It features 9 columns including author, publication date, language, and book publisher.

China recorded a trade surplus of 102.33 USD Billion in August of 2025. This dataset provides - China Balance of Trade - actual values, historical data, forecast, chart, statistics, economic calendar and news.

GDP from Manufacturing in China increased to 202550.30 CNY Hundred Million in the second quarter of 2025 from 98344.50 CNY Hundred Million in the first quarter of 2025. This dataset provides - China Gdp From Manufacturing- actual values, historical data, forecast, chart, statistics, economic calendar and news.

GDP from Services in China increased to 390313.80 CNY Hundred Million in the second quarter of 2025 from 195142.30 CNY Hundred Million in the first quarter of 2025. This dataset provides - China Gdp From Services- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The contribution of women to China’s economic growth and development cannot be overemphasized. Women play important social, economic, and productive roles in any economy. China remains one of the countries in the world with severe gender inequality and sex ratio at birth (SRB) imbalance. Severe gender inequality and disenfranchisement of girls with abnormally high sex ratios at birth reflect deep-rooted sexism and adversely affect girls’ development. For China to achieve economic growth, women should not be ignored and marginalized so that they can contribute to the country’s growth, but the sex ratio at birth needs to be lowered because only women can contribute to growth. Thus, this study empirically predicts an asymmetric relationship between gender inequality, sex ratio at birth and economic growth, using NARDL model over the period 1980–2020. The NARDL results show that increases in gender inequality and sex ratio at birth significantly reduce economic growth in both the short and long term, while reductions in gender inequality and sex ratio at birth significantly boost economic growth in both the short and long term. Moreover, the results show the significant contribution of female labor force participation and female education (secondary and higher education) to economic growth. However, infant mortality rate significantly reduced economic growth. Strategically, the study recommends equal opportunities for women in employment, education, health, economics, and politics to reduce gender disparities and thereby promote sustainable economic growth in China. Moreover, policymakers should introduce new population policy to stabilize the sex ratio at birth, thereby promoting China’s long-term economic growth.

This work critically examines the emergence of a post-industrial economy in China as it continues to transform into a 21st century global leader. On August 15th, 2010, the Financial Times published an article stating that recently released figures from the International Monetary Fund show that China had surpassed Japan as the second-largest economy in the world and predicted that China will maintain its lead going forward . This is an astonishing feat for an emerging economy, as Japan had previously held the second-place position for over four decades. In recent years, China has outperformed other large emerging economies such as Brazil, Russia and India. As a result, it is important to examine China more closely and understand what is occurring within the country as it continues to grow and develop as a global leader. In the contemporary global environment, lasting economic advantage comes from attracting and retaining a talented and creative workforce. As China begins to transition from an industrial economy to a post-industrial economy, several factors including a more educated workforce, the development of domestic intellectual property and openness to a more diverse range of ideas and people are becoming more important. Against this backdrop, this report explores the emergence of a creative, service-driven, post-industrial economy in China by employing two methods of analysis developed by Richard Florida (2002). The first part of the analysis examines the changing occupational structure of China’s workforce. To execute this part of the analysis, we divide China’s workforce into the four occupational categories defined by Florida (2002): creative class, service class, working class and fishing, farming and forestry class. The second part of the analysis employs what are known as the “3Ts of economic development” to rank China’s regions according to their strengths in supporting a creative economy. The 3Ts of regional economic development include technology (high-tech employment and innovation), talent (education and skills), and tolerance (diversity and openness). The report explores China’s provincial-level regions and three of its four Municipalities, with a special interest in the dynamics and geography of the creative economy.

Time series data for the statistic Gross_Domestic_Product_Current_USD and country China. Indicator Definition:GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current U.S. dollars. Dollar figures for GDP are converted from domestic currencies using single year official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used.The statistic "Gross Domestic Product Current USD" stands at 18,743,803,170,827.20 United States Dollars as of 12/31/2024, the highest value at least since 12/31/1961, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 2.59 percent compared to the value the year prior.The 1 year change in percent is 2.59.The 3 year change in percent is 2.98.The 5 year change in percent is 28.73.The 10 year change in percent is 75.59.The Serie's long term average value is 3,590,131,888,959.60 United States Dollars. It's latest available value, on 12/31/2024, is 422.09 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1962, to it's latest available value, on 12/31/2024, is +39,518.50%.The Serie's change in percent from it's maximum value, on 12/31/2024, to it's latest available value, on 12/31/2024, is 0.0%.

This is the data used for the estimation of the GVAR model as in "China's Emergence in the World Economy and Business Cycles in Latin America" (access the study in the related URL Section). The dataset includes quarterly data for twenty-five major advanced and emerging economies plus the euro area, covering more than 90 percent of world GDP. The variables included in the dataset are real GDP, CPI inflation, real equity prices, real exchange rates, short-term and long-term interest rates, and the price of oil. Updates of this dataset -together with the baseline GVAR code- can be found in the Related URL section below. Years covered: 1979 - 2009.

Based on the Office for National Statistics’ delineation of the scope of the digital economy industry, this paper selects indicators from five industrial dimensions: digital product manufacturing, digital product service, digital technology application, digital factor drive and digital efficiency improvement, and constructs an evaluation system to measure the development level of China’s digital economy at the provincial level. It is found that there is a wide gap in the development of China’s provincial digital economy, with the eastern coastal provinces and cities having a high level of digital economy development. The coupling and coordination model was then applied to examine the interrelationships between the five industrial dimensions of the digital economy, and it was found that most of the coupling and coordination relationships of the five industrial dimensions are at the stage of medium-high coupling and low coupling and coordination, and each province and city has different coupling and coordination characteristics. The numerical evaluation results provide an intuitive understanding of the differences and deficiencies in the development of the digital economy in different regions, and serve as a reference for the medium and long-term digital economy development planning of provinces and municipalities as well as the whole country. In the future, the state should invest more in the digital economy in the central and western regions, and each province should cultivate and develop the digital economy in accordance with its own local conditions.

This dataset is about books. It has 6 rows and is filtered where the book subjects is Information technology-Economic aspects-China. It features 9 columns including author, publication date, language, and book publisher.

Annual data on the size of China Shadow Bank credit was taken from two sources. Moodys (China) produces data 2000-2012 and Goldman Sachs 2013-2018. The average growth rate 2000-2015 was applied to generate data prior to 2000. Annual data was interpolated to produce quarterly estimates using the cubic match last function in EViews so that the integrity of the annual stock data is maintained for the 4th quarter.The Chinese financial system has served the Chinese economy well in the early stages of development in channeling domestic savings to domestic investment. But, continued financial repression, along with a growing middle class and ageing population has created pressure on savings to 'search for yield'. At the same time, the dominance of lending to state-owned-enterprises, political constraints, inefficiencies and weak risk management practice by financial institutions (FI) have pushed SMEs to alternative sources of funding. The demand for yield from savers and funds from private investment has been met by the rapid growth in shadow banking. This study encompasses two of the identified themes of the research call. The research theme 'alternative strategies for reform and liberalization' covers the role of the Shadow Bank system in the credit intermediation process. This research is of critical importance because it informs the macroeconomic research necessary for investigating 'the role of the Chinese financial system in sustaining economic growth'. Addressing the first research theme we take a dual track approach to better understand the role of the financial system in sustaining in economic growth. The first track examines the role of bank and non-bank finance in promoting long-term economic growth at the regional level. The second track is aimed at the more short-term issue of identifying the potential frequency of macro-economic crises generated by a banking crisis. The finance-growth nexus is a well-established area of economic development, however the China experience questions the supposition that financial development is a necessary precondition. The empirical findings are mixed. Part of the reason for this could be the failure to distinguish between the quality of financial institutions across regions, and the openness of the local environment in terms of the balance between private and public enterprises. Our research would build on the existing literature in two ways. First, it would utilize imperfect but available data on informal finance to examine direct and spill-over effects on medium term growth from contiguous provinces. Second, primary data on the geographic dimension in shadow bank lending gleaned from Theme 2 research will be used to design a weighting system to adjust financial flows for the quality of the local financial environment. The second prong will develop a small macroeconomic model of a hybrid DSGE type that incorporates a banking sector including shadow banks. Such models have been developed for China in recent times but only a few have attempted to incorporate a banking sector.These models are mostly calibrated versions and make no attempt to test the structure against the data. Recent attempts to test a hybrid New Keynesian-RBC DSGE type model for the Chinese economy using the method of indirect inference have been successful and inclusion of a shadow banks have shown some success. The results of the Theme 2 study will inform the development of a fuller shadow banking sector in the macroeconomic model that will be used to estimate the frequency of economic crises generated by bank crises. Theme 2 research will examine the relationship between the banking system and the shadow banking system as complements or substitutes. It will aim to determine the variable interest rate on the P2P online lending platform on the basis of risk-return, the home bias in online investments, and the signaling and screening in the P2P online lending platform. Finally, it will aim to identify the impact of shadow banking on entrepreneurial activity, the industrial growth rate and regional housing investment and price differentials. These results would inform the theme 1 research on the interconnectedness of shadow banking with the mainstream and the fragility of the financial system to shocks and financial crises. Secondary data was taken from multiple Reports on China Shadow Banking published by Goldman Sachs (china) and Moodys (China). The data 2000-2018 corresponds to the annual data obtained from the reports. The quarterly data was obtained by interpolation. Data prior to 2000 was generated using the assumed average growth rate of shadow bank credit for 2000-2015.

This dataset is about book subjects. It has 3 rows and is filtered where the books is The political economy of corruption in China. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

The data contains factors which affect firm growth in the setting of the Chinese transition economy, such as size, age, entrepreneurship, resources, and environment. The data were gathered in face-to-face interviews with 83 owner-managers of the Chinese privately owned firms in P. R. China in 2004, as well as in follow-up telephone interviews in 2006. Data were gathered by face-to-face interviews. The selection criteria of the sampled firms were: (a) privately owned firms, (b) financially independent (not a subsidiary), (c) located in the territory of Guangdong Province. Contacts were gained through networks and colleagues. Critically speaking, this non-probabilistic approach resembles 'snowball sampling', depending as it does on references rather than random selections. The population of 21 major cities economic data and the population of manufacturing firms in 14 cities/counties of Guangdong Province were employed to attest to the representation of the sample, across all sectors.

About this dataset

Context

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

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 34(8.17%) households where the householder is under 25 years old, 107(25.72%) households with a householder aged between 25 and 44 years, 127(30.53%) households with a householder aged between 45 and 64 years, and 148(35.58%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 65 years and over bracket. This distribution hints at economic disparities within the city of China, showcasing varying income levels among different age demographics.

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:

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

Variables / Data Columns

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

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for China median household income by age. You can refer the same here

China's main stock market index, the SHANGHAI, rose to 3883 points on September 30, 2025, gaining 0.52% from the previous session. Over the past month, the index has climbed 0.19% and is up 11.26% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on October of 2025.

Manufacturing Production in China increased 5.70 percent in August of 2025 over the same month in the previous year. This dataset provides - China Manufacturing Production- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Loans to Private Sector in China increased to 835407.71 CNY Hundred Million in August from 835104.95 CNY Hundred Million in July of 2025. This dataset provides - China Loans To Private Sector - actual values, historical data, forecast, chart, statistics, economic calendar and news.