The Gross Domestic Product (GDP) in China was worth 18743.80 billion US dollars in 2024, according to official data from the World Bank. The GDP value of China represents 17.65 percent of the world economy. This dataset provides - China GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Gross Domestic Product (GDP) in China expanded 4.80 percent in the third quarter of 2025 over the same quarter of the previous year. This dataset provides - China GDP Annual Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

🧾 About Dataset

🌍 Global GDP by Country — 2024 Edition

📖 Overview

The Global GDP by Country (2024) dataset provides an up-to-date snapshot of worldwide economic performance, summarizing each country’s nominal GDP, growth rate, population, and global economic contribution.

This dataset is ideal for economic analysis, data visualization, policy modeling, and machine learning applications related to global development and financial forecasting.

📊 Dataset Information

• Total Records: 181 countries
• Time Period: 2024 (latest available global data)
• Geographic Coverage: Worldwide
• File Format: CSV
• File Size: ~10 KB
• Missing Values: None (100% complete dataset)

🎯 Target Use-Cases:
- Economic growth trend analysis
- GDP-based country clustering
- Per capita wealth comparison
- Share of world economy visualization

🧩 Key Features

📈 Statistical Summary

Population Overview

• Mean Population: 43.6 million
• Standard Deviation: 155.5 million
• Minimum Population: 9,816 (small island nations)
• Median Population: 9.1 million
• Maximum Population: 1.43 billion (China)

🌟 Highlights

💰 Top Economies (Nominal GDP):
United States, China, Japan, Germany, India

📈 Fastest Growing Economies:
India, Bangladesh, Vietnam, and Rwanda

🌐 Global Insights:
- The dataset covers 181 countries representing 100% of global GDP.
- Suitable for data visualization dashboards, AI-driven economic forecasting, and educational research.

💡 Example Use-Cases

• Build a choropleth map showing GDP distribution across continents.
• Train a regression model to predict GDP per capita based on population and growth.
• Compare economic inequality using population vs GDP share.

📚 Dataset Citation

Source: Worldometers — GDP by Country (2024)
Dataset compiled and cleaned by: Asadullah Shehbaz
For open research and data analysis.

Description

This dataset provides monthly economic indicators examining the relationship between US protectionist trade policies and Chinese economic growth from May 2022 to May 2025. The dataset can be used for academic research, statistical analysis, and educational purposes in international economics and trade policy studies.

Context

The dataset captures the economic dynamics during a period of heightened trade tensions between the United States and China. It includes comprehensive indicators of US protectionist measures and their potential impact on various dimensions of Chinese economic performance.

Dataset Structure

Time Period: May 2022 - May 2025 Frequency: Monthly Total Observations: 1127 Total Variables: 14

Column Descriptions

Date

• Type: Categorical (YYYY-MM format)
• Description: Month and year of observation

US Protectionism Indicators

US_Tariff_Index

• Type: Continuous
• Range: 100-120
• Description: Tariff rate index where 100 represents the baseline. Higher values indicate increased protectionist measures.

US_Trade_Restrictions

• Type: Discrete
• Range: 5-15
• Description: Number of new trade restrictions implemented per month, including tariffs, quotas, and non-tariff barriers.

US_AntiDumping_Cases

• Type: Discrete
• Range: 0-12
• Description: Number of anti-dumping cases filed against Chinese products per month.

US_Trade_Policy_Uncertainty

-Type: Continuous - Range: 90-160 - Description: Index measuring uncertainty in trade policy (0-200 scale). Higher values indicate greater uncertainty.

Chinese Growth Indicators

China_GDP_Growth_YoY

• Type: Continuous
• Unit: Percentage (%)
• Range: 3.5-5.5%
• Description: Year-over-year GDP growth rate for China.

China_Industrial_Production_YoY

• Type: Continuous
• Unit: Percentage (%)
• Range: 2.5-5.0%
• Description: Year-over-year change in industrial production output.

China_Exports_Growth_YoY

• Type: Continuous
• Unit: Percentage (%)
• Range: 0-15%
• Description: Year-over-year growth rate of Chinese exports.

China_Manufacturing_PMI

• Type: Continuous
• Range: 47-52
• Description: Purchasing Managers' Index for manufacturing sector. Values >50 indicate expansion, <50 indicate contraction.

China_FDI_Inflow_USD_Billion

• Type: Continuous
• Unit: Billion USD
• Range: 7-15
• Description: Monthly foreign direct investment inflows into China.

Bilateral Trade Indicators

US_China_Trade_Volume_USD_Billion

• Type: Continuous
• Unit: Billion USD
• Range: 40-60
• Description: Total monthly bilateral trade volume between US and China.

US_Trade_Deficit_China_USD_Billion

• Type: Continuous
• Unit: Billion USD
• Range: -32 to -24
• Description: US trade deficit with China. Negative values indicate deficit.

Additional Economic Indicators

USD_CNY_Exchange_Rate

• Type: Continuous
• Range: 6.7-7.3
• Description: Exchange rate expressing yuan per US dollar.

China_Stock_Index

• Type: Continuous
• Range: 2,800-3,500
• Description: Shanghai Composite Stock Index (normalized).

The Gross Domestic Product per capita in China was last recorded at 13121.68 US dollars in 2024. The GDP per Capita in China is equivalent to 104 percent of the world's average. This dataset provides - China GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Gross Domestic Product per capita in China was last recorded at 23845.62 US dollars in 2024, when adjusted by purchasing power parity (PPP). The GDP per Capita, in China, when adjusted by Purchasing Power Parity is equivalent to 134 percent of the world's average. This dataset provides - China GDP per capita PPP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

About this dataset

Context

The dataset presents median household incomes for various household sizes in China, TX, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, China did not include 4, 5, 6, or 7-person households. Across the different household sizes in China the mean income is $59,611, and the standard deviation is $38,331. The coefficient of variation (CV) is 64.30%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2023, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $24,167. It then further increased to $54,375 for 3-person households, the largest household size for which the bureau reported a median household income.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific household size.

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. You can refer the same here

This dataset provides key economic indicators for five of the world's largest economies, based on their nominal Gross Domestic Product (GDP) in 2022. It includes the GDP values, population, GDP growth rates, per capita GDP, and each country's share of the global economy.

Columns: Country: Name of the country. GDP (nominal, 2022): The total nominal GDP in 2022, represented in USD. GDP (abbrev.): The abbreviated GDP in trillions of USD. GDP growth: The percentage growth in GDP compared to the previous year. Population: Total population of each country in 2022. GDP per capita: The GDP per capita, representing average economic output per person in USD. Share of world GDP: The percentage of global GDP contributed by each country. Key Highlights: The dataset includes some of the largest global economies, such as the United States, China, Japan, Germany, and India. The data can be used to analyze the economic standing of countries in terms of overall GDP and per capita wealth. It offers insights into the relative growth rates and population sizes of these leading economies. This dataset is ideal for exploring economic trends, performing country-wise comparisons, or studying the relationship between population size and GDP growth.

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.

Explore annual GDP growth rates for various countries with this dataset. Analyze trends and patterns related to GDP growth to make informed decisions. Click here for more information!

GDP growth (annual %), GDP, Growth Rates

Kenya, Spain, Syrian Arab Republic, Bosnia and Herzegovina, El Salvador, Italy, Sint Maarten (Dutch part), Comoros, Kosovo, Argentina, Bulgaria, Guinea-Bissau, Slovenia, Guinea, Belize, Low income, Lower middle income, New Caledonia, St. Kitts and Nevis, Benin, World, Kyrgyz Republic, United Arab Emirates, Ethiopia, Burundi, Korea, Rep., Low & middle income, Euro area, Libya, Luxembourg, Namibia, Kiribati, India, Burkina Faso, East Asia & Pacific (excluding high income), Tajikistan, Lao PDR, Equatorial Guinea, Niger, Liechtenstein, Palau, Hong Kong SAR, China, Switzerland, Tonga, Qatar, Turkiye, Middle East & North Africa (excluding high income), Indonesia, Iraq, Fiji, Central Europe and the Baltics, Isle of Man, Costa Rica, Finland, Small states, Singapore, Slovak Republic, Netherlands, Turks and Caicos Islands, Europe & Central Asia (IDA & IBRD countries), Japan, Bhutan, Belgium, Australia, Denmark, Heavily indebted poor countries (HIPC), Middle East & North Africa (IDA & IBRD countries), Uzbekistan, Pacific island small states, Mongolia, Gabon, St. Vincent and the Grenadines, Ukraine, Venezuela, RB, Latvia, Macao SAR, China, Vietnam, Arab World, Myanmar, Latin America & Caribbean (excluding high income), Haiti, Micronesia, Fed. Sts., Nicaragua, Panama, San Marino, Gambia, The, Guatemala, IDA & IBRD total, Azerbaijan, Chad, Zimbabwe, Mali, Bolivia, Grenada, Mexico, East Asia & Pacific (IDA & IBRD countries), Timor-Leste, Dominica, Peru, Malawi, Trinidad and Tobago, Nauru, Monaco, Tuvalu, Egypt, Arab Rep., Virgin Islands (U.S.), Sao Tome and Principe, Cabo Verde, IDA only, Mozambique, Oman, Yemen, Rep., Albania, New Zealand, Latin America & Caribbean, Rwanda, Cameroon, Lesotho, Solomon Islands, Germany, Bangladesh, Papua New Guinea, Maldives, Moldova, Antigua and Barbuda, Congo, Dem. Rep., Romania, Portugal, Africa Western and Central, Mauritius, France, Uruguay, Tanzania, Colombia, South Asia (IDA & IBRD), Honduras, South Sudan, Sudan, Cuba, Least developed countries: UN classification, South Asia, Tunisia, Guyana, Nepal, Barbados, Brunei Darussalam, United States, Canada, Lebanon, Africa Eastern and Southern, Sub-Saharan Africa (excluding high income), Angola, Bahamas, The, Fragile and conflict affected situations, Malta, Middle East & North Africa, Turkmenistan, Cote d'Ivoire, Northern Mariana Islands, Thailand, Seychelles, North Macedonia, Afghanistan, Russian Federation, IBRD only, Iran, Islamic Rep., Malaysia, Djibouti, Europe & Central Asia (excluding high income), Norway, Dominican Republic, French Polynesia, Jordan, Nigeria, Lithuania, Estonia, Eswatini, Vanuatu, Late-demographic dividend, St. Lucia, Cambodia, Curacao, Kuwait, Belarus, American Samoa, Bahrain, Somalia, Pre-demographic dividend, Ghana, Sierra Leone, Jamaica, Ecuador, European Union, Post-demographic dividend, Brazil, Central African Republic, Chile, Puerto Rico, Pakistan, Uganda, United Kingdom, IDA total, Marshall Islands, Czechia, Channel Islands, Poland, Togo, Latin America & the Caribbean (IDA & IBRD countries), Sweden, Iceland, Armenia, Georgia, Montenegro, Europe & Central Asia, Hungary, IDA blend, Sub-Saharan Africa (IDA & IBRD countries), Paraguay, Zambia, Andorra, OECD members, Bermuda, Early-demographic dividend, Croatia, Upper middle income, Algeria, Samoa, Eritrea, Suriname, Mauritania, Guam, China, Sri Lanka, Congo, Rep., Liberia, Greece, Botswana, East Asia & Pacific, West Bank and Gaza, Philippines, Cayman Islands, Saudi Arabia, South Africa, High income, Serbia, Caribbean small states, Greenland, Cyprus, Aruba, Ireland, Israel, Kazakhstan, Morocco, Madagascar, Other small states, Sub-Saharan Africa, Senegal, Middle income, Austria, North America Follow data.kapsarc.org for timely data to advance energy economics research.

China Exports to United States was US$525.65 Billion during 2024, according to the United Nations COMTRADE database on international trade. China Exports to United States - data, historical chart and statistics - was last updated on November of 2025.

An increase in a currency internationalization levels can positively impact its credibility in international economic activities, and expand the effective demand and optimize the supply structure for the country’s financial service trade. In this way, a state can improve its financial service trade competitiveness in the international market. This study builds a vector autoregressive model based on time-series data of China-US financial services trade from 2010 to 2021, analyzes the impact of different quantitative indicators of RMB internationalization on this trade from the impulse response results, and validates the conclusions using various inspection methods. The results show that the increase in RMB internationalization helps to narrow the China-US financial services trade balance, but with a significant lag. And this effect is heterogeneous in different dimensions, demonstrated by the fact that the development of overseas RMB securities business is more important for the level of RMB internationalization to narrow the China-US financial services trade balance. Finally, among the specific measures to improve its financial services trade, China should focus on developing the international competitiveness of the traditional RMB deposit and loan financial sector, while the competition in the overseas market for high value-added financial businesses must also not be neglected. Furthermore, China needs to implement more targeted RMB internationalization development policies at different levels in the future to provide high-quality financial services to the rest of the world and aid in the economic recovery of the world in the "post-pandemic" era.

SpaceKnow uses satellite (SAR) data to capture activity in electric vehicles and automotive factories.

Data is updated daily, has an average lag of 4-6 days, and history back to 2017.

The insights provide you with level and change data that monitors the area which is covered with assembled light vehicles in square meters.

We offer 3 delivery options: CSV, API, and Insights Dashboard

Available companies Rivian (NASDAQ: RIVN) for employee parking, logistics, logistic centers, product distribution & product in the US. (See use-case write up on page 4) TESLA (NASDAQ: TSLA) indices for product, logistics & employee parking for Fremont, Nevada, Shanghai, Texas, Berlin, and Global level Lucid Motors (NASDAQ: LCID) for employee parking, logistics & product in US

Why get SpaceKnow's EV datasets?

Monitor the company’s business activity: Near-real-time insights into the business activities of Rivian allow users to better understand and anticipate the company’s performance.

Assess Risk: Use satellite activity data to assess the risks associated with investing in the company.

Types of Indices Available Continuous Feed Index (CFI) is a daily aggregation of the area of metallic objects in square meters. There are two types of CFI indices. The first one is CFI-R which gives you level data, so it shows how many square meters are covered by metallic objects (for example assembled cars). The second one is CFI-S which gives you change data, so it shows you how many square meters have changed within the locations between two consecutive satellite images.

How to interpret the data SpaceKnow indices can be compared with the related economic indicators or KPIs. If the economic indicator is in monthly terms, perform a 30-day rolling sum and pick the last day of the month to compare with the economic indicator. Each data point will reflect approximately the sum of the month. If the economic indicator is in quarterly terms, perform a 90-day rolling sum and pick the last day of the 90-day to compare with the economic indicator. Each data point will reflect approximately the sum of the quarter.

Product index This index monitors the area covered by manufactured cars. The larger the area covered by the assembled cars, the larger and faster the production of a particular facility. The index rises as production increases.

Product distribution index This index monitors the area covered by assembled cars that are ready for distribution. The index covers locations in the Rivian factory. The distribution is done via trucks and trains.

Employee parking index Like the previous index, this one indicates the area covered by cars, but those that belong to factory employees. This index is a good indicator of factory construction, closures, and capacity utilization. The index rises as more employees work in the factory.

Logistics index The index monitors the movement of materials supply trucks in particular car factories.

Logistics Centers index The index monitors the movement of supply trucks in warehouses.

Where the data comes from: SpaceKnow brings you information advantages by applying machine learning and AI algorithms to synthetic aperture radar and optical satellite imagery. The company’s infrastructure searches and downloads new imagery every day, and the computations of the data take place within less than 24 hours.

In contrast to traditional economic data, which are released in monthly and quarterly terms, SpaceKnow data is high-frequency and available daily. It is possible to observe the latest movements in the EV industry with just a 4-6 day lag, on average.

The EV data help you to estimate the performance of the EV sector and the business activity of the selected companies.

The backbone of SpaceKnow’s high-quality data is the locations from which data is extracted. All locations are thoroughly researched and validated by an in-house team of annotators and data analysts.

Each individual location is precisely defined so that the resulting data does not contain noise such as surrounding traffic or changing vegetation with the season.

We use radar imagery and our own algorithms, so the final indices are not devalued by weather conditions such as rain or heavy clouds.

→ Reach out to get a free trial

Use Case - Rivian:

SpaceKnow uses the quarterly production and delivery data of Rivian as a benchmark. Rivian targeted to produce 25,000 cars in 2022. To achieve this target, the company had to increase production by 45% by producing 10,683 cars in Q4. However the production was 10,020 and the target was slightly missed by reaching total production of 24,337 cars for FY22.

SpaceKnow indices help us to observe the company’s operations, and we are able to monitor if the company is set to meet its forecasts or not. We deliver five different indices for Rivian, and these indices observe logistic centers, employee parking lot, logistics, product, and prod...

This paper investigates the effects of United States sanctions on Chinese public and private overseas foreign direct investment (FDI). Using data for up to 112 developing countries from 2005-2015, we find that Chinese state-owned enterprises (SOEs) are more likely to invest in countries threatened or targeted with U.S. sanctions relative to Chinese privately-owned enterprises (POEs) because they have the Chinese government’s backing and are larger in number and size, motivating them to invest in higher-risk states. The Chinese government also reaps political benefits by Chinese SOEs investing abroad, enhancing China’s economic strength and decreasing its rivals’ influence. We also obtain similar results for Chinese SOEs and POEs regardless of the investment sector and conduct additional robustness checks that further reinforce the main findings. Our study provides insights into how China’s overseas FDI increases its economic and political reach across the globe at the possible expense of the U.S.

Key information about China Foreign Direct Investment

• China Foreign Direct Investment (FDI) increased by 8.5 USD bn in Sep 2025, compared with an increase of 17.4 USD bn in the previous quarter.
• China Foreign Direct Investment: USD mn net flows data is updated quarterly, available from Mar 1998 to Sep 2025.
• The data reached an all-time high of 107.2 USD bn in Mar 2022 and a record low of -15.0 USD bn in Jun 2024.

• In the latest reports of China, Current Account recorded a surplus of 195.6 USD bn in Sep 2025.
• China Direct Investment Abroad expanded by 39.9 USD bn in Sep 2025.
• Its Foreign Portfolio Investment increased by 8.3 USD bn in Jun 2025.
• The country's Nominal GDP was reported at 4,953.4 USD bn in Sep 2025.

China recorded a trade surplus of 90.07 USD Billion in October of 2025. This dataset provides - China Balance of Trade - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The United States recorded a trade deficit of 59.55 USD Billion in August of 2025. This dataset provides the latest reported value for - United States Balance of Trade - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

When permitted by law, employers sometimes state the preferred age and gender of their employees in job ads. The researchers study the interaction of advertised requests for age and gender on one Mexican and three Chinese job boards, showing that firms’ explicit gender requests shift dramatically away from women and towards men when firms are seeking older (as opposed to younger) workers. This ‘age twist’ in advertised gender preferences occurs in all four of our datasets and survives controls for occupation, firm, and job title fixed effects. Chinese Data The two new Chinese data sources used are job boards serving the city of Xiamen. In part because Xiamen was one of the five economic zones established immediately after China’s 1979 economic reforms, it is highly modernized relative to other Chinese cities, with an economy based on electronics, machinery and chemical engineering. One of these job boards, XMZYJS (the Xia-Zhang-Quan city public job board) is operated directly by government employees of the local labor bureau. Like state-operated Job Centers in the U.S., XMZYJS has a history as a brick-and-mortar employment service. XMZYJS’s mandate is to serve the less-skilled portion of the area’s labor market, and operates purely as a jobposting service: workers cannot post resumes or apply to jobs on the site. In fact, while XMZYJS now posts all its job ads online, many of these ads are viewed in XMZYJS‘s offices by workers who visit in person. This is done both on individual computer terminals and on a large electronic wall display. Applications are made by calling the company that placed the ad or by coming to a specific window on XMZYJS’s premises that has been reserved by the employer at a posted date and time. The second Xiamen-based job board, XMRC , is a for-profit, privately-operated company that is sponsored by the local government. Its mandate is to serve the market for skilled workers in the Xiamen metropolitan area. XMRC operates like a typical U.S. job board: both job ads and resumes are posted online, workers can submit applications to specific jobs via the site, and firms can contact individual workers through the site as well. By design, XMZYJS aggregates job postings from all local and specialized job boards for less-skilled workers in the metropolitan area, and XMRC is the main job board for skilled workers in the area. While there is potentially some cross-posting of job ads across the two sites, descriptive statistics on the types of jobs on offer suggest the sites do, indeed, serve very different populations. Like all our data sets, XMZYJS and XMRC serve private sector employers almost exclusively. Recruiting for public sector jobs, and most recruiting for State-Owned-Enterprises (SOEs) takes place via a different process. The third Chinese database represents Zhaopin as the third-largest Internet job board in China; it operates nationally and serves workers who on average are considerably more skilled than even those on XMRC. This sample is based on all unique ads posted in four five-week observation periods in 2008-2010. In contrast to XMRC and XMZYJS where the data were supplied by the job boards, the Zhaopin data were collected by a web crawler. The sample is based on all unique ads posted in four five-week observation periods in 2008-2010. The Chinese data have 141,188, 39,727, and 1,051,038 ads in the XMZYJS, XMRC and Zhaopin samples respectively. Mexican Data The Mexican data allows to ascertain whether main results extend to a nation with different economic conditions, labor market institutions and culture. The Mexican data is a sample of job ads posted on Computrabajo. Of the new data sets explored, the Computrabajo data are most similar to Zhaopin in the sense that they come from a national online site that disproportionately serves highly skilled workers. To construct an analysis sample from the Computrabajo website, the authors collected advertisements daily for approximately 18 months between early 2011 and mid-2012 using a web crawler. Both the standardized fields and the open text portions of each ad were parsed to extract variables for the analysis. Computrabajo analysis sample contains 90,487 ads.

An increase in a currency internationalization levels can positively impact its credibility in international economic activities, and expand the effective demand and optimize the supply structure for the country’s financial service trade. In this way, a state can improve its financial service trade competitiveness in the international market. This study builds a vector autoregressive model based on time-series data of China-US financial services trade from 2010 to 2021, analyzes the impact of different quantitative indicators of RMB internationalization on this trade from the impulse response results, and validates the conclusions using various inspection methods. The results show that the increase in RMB internationalization helps to narrow the China-US financial services trade balance, but with a significant lag. And this effect is heterogeneous in different dimensions, demonstrated by the fact that the development of overseas RMB securities business is more important for the level of RMB internationalization to narrow the China-US financial services trade balance. Finally, among the specific measures to improve its financial services trade, China should focus on developing the international competitiveness of the traditional RMB deposit and loan financial sector, while the competition in the overseas market for high value-added financial businesses must also not be neglected. Furthermore, China needs to implement more targeted RMB internationalization development policies at different levels in the future to provide high-quality financial services to the rest of the world and aid in the economic recovery of the world in the "post-pandemic" era.

United States Imports from China was US$462.62 Billion during 2024, according to the United Nations COMTRADE database on international trade. United States Imports from China - data, historical chart and statistics - was last updated on December of 2025.