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 5.20 percent in the second 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.

In 2024, the industrial sector generated around **** percent of China's GDP. It was by far the largest contributor, followed by the wholesale and retail industry that was responsible for **** percent and the financial sector that produced *** percent of the country's economic output. Since China is the second-largest economy in the world, the industrial sector’s output alone exceeded the entire economy of Germany. China’s export and investment-driven economy China economic development of the early 2000s was mainly driven by investments and exports. A country's gross domestic product (GDP) consists of three parts: Consumption, investments, and net exports. Typically, emerging economies rely mainly on investments and exports for growing their economy and China was no exception. By the end of the 2010s, investments fueled more than 40 percent of China's GDP and exports were responsible for almost another 20 percent. In comparison to that, in most developed economies, investments make up only 20 percent of the economic output. Instead, the main economic driver is consumption. The economic structure in China created a huge industrial sector. For instance, China was the biggest steel exporter, the leading merchandise exporter, and exported more than a third of global household goods. Great push towards transformation In early 2018, the Chinese government proclaimed that the country's economy had reached a new development stage where consumption and services replaced investment and manufacturing as the main driver of economic growth. The fear of the middle-income trap and changing demographics were the main reasons for Beijing's emphasis on economic transformation. Although incomes in China had not stagnated, policymakers attempted to preempt “getting stuck” by steering the economy towards high-quality growth and consumption-focus. Furthermore, a society that was older and had a higher share of middle-class population had different requirements to the economy. In the case of a successful transformation, China's economy would become more similar to those of developed nations. For instance, the financial sector was the largest contributor to the United States economy. In the case of Germany, the service sector generates the largest share of gross domestic product.

This data package includes the PIIE dataset to replicate the data and charts presented in The rise of US economic sanctions on China: Analysis of a new PIIE dataset by Martin Chorzempa, Mary E. Lovely, and Christine Wan, PIIE Policy Brief 24-14.

If you use the dataset, please cite as: Chorzempa, Martin, Mary E. Lovely, and Christine Wan. 2024. The rise of US economic sanctions on China: Analysis of a new PIIE dataset, PIIE Policy Brief 24-14. Washington, DC: Peterson Institute for International Economics.

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.

ABSTRACT This article seeks to show two interconnected phenomena in China. The first is a historical process that took place in the past 40 years involving institutional and qualitative changes in the state-controlled portion of the Chinese economy. Such changes have brought about new and superior forms of economic planning, based on which a higher stage of development pattern has emerged. We call this new development pattern "New Projectment Economy" and it synthesizes a series of state capacities built over time. The second phenomenon relates to how the state capacities created in the past decades have allowed the country to show adaptive flexibility and rapid efficiency in the containment of Covid-19 crisis internally and thus explain China's successful response in the fight against the coronavirus. Such phenomena, pari passu, show China's potential and projection as an international political actor.

China New Year's Day: Number of Holiday data was reported at 1.000 Day in 2025. This records a decrease from the previous number of 3.000 Day for 2024. China New Year's Day: Number of Holiday data is updated yearly, averaging 3.000 Day from Dec 2018 (Median) to 2025, with 8 observations. The data reached an all-time high of 3.000 Day in 2024 and a record low of 1.000 Day in 2025. China New Year's Day: Number of Holiday data remains active status in CEIC and is reported by Ministry of Culture and Tourism. The data is categorized under China Premium Database’s Holiday Economy – Number of Holiday.

In 2014, the Chinese government unveiled the New Urbanization Plan and Document No. 46, which profoundly influenced the development trajectory of the regional economy and sports industry. Using the coupling coordination model, this study aimed to assess the development progress of the sports industry and urban clusters economy. This study sampled Greater Bay Area urban clusters (GBAUC) and Yangtze River Delta urban clusters (YRDUC). The statistics covered one year after the release of the policies to date. We developed a total of 15 macro indicators to evaluate the sports industry and urban cluster economy as two distinct, yet interdependent, economic systems. Using the entropy weight method, we determined the standardized values and weights for the two systems before calculating the coupling coordination degree (D). Between 2015 and 2021, the sampled sports industry and urban clusters economy exhibited coordinated high growth across all economic metrics, with multiple sports industry metrics exhibiting double-digit growth. In 2015, both showed extreme imbalance: D of GBAUC = 0.092, D of YRDUC = 0.091. In 2017, both improved to bare coordination: D of GBAUC = 0.600, D of YRDUC = 0.566. In 2019, both reached well coordination: D of GBAUC = 0.851, D of YRDUC = 0.814. By 2021, both achieved quality coordination: D of GBAUC = 0.990, D of YRDUC = 1. This study provides the first evidence from the sports industry that China’s new urbanization model and Document No. 46 are highly effective for synergistic regional economic growth.

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in China's New Economic Frontier: Overcoming Obstacles to Continued Growth, PIIE Briefing 16-5.

If you use the data, please cite as: Miner, Sean, ed., Joseph E. Gagnon, Jacob Funk Kirkegaard, Edwin M. Truman, and Nicolas Véron. (2016). China's New Economic Frontier: Overcoming Obstacles to Continued Growth. PIIE Briefing 16-5. Peterson Institute for International Economics.

This repository contains the appendix, the dataset, and the analysis files for the study "Unpacking the Nuances of Agenda-Setting in the Online Media Environment: An Hourly-Event Approach in the Context of Chinese Economic News."Except for the appendix, the "Data" folder contains 36 csv-format files, each for one specific news event. In each file, the first column "hour" denotes hourly intervals of the data, and the 2–6 columns denote the endogenous variables included in the VAR models (i.e., the raw volume of coverage or discussion in different groups concerning media, the neitizens, and other institutions of interest). The datasets have been aggregated by 19-hour lags each day, resulting in 266 lags for the 14-day time window."AnalysisFiles" folder contains the R code and copy results for analysis, in which:-TimeSeriesAnalysis" contains the R code for the time-series analysis of this study. Besides, this folder also contains copies of the results for VAR models.-"t-test & ANOVA" contains the results of 36 separate VAR models and the R code for the t-test and ANOVA for the event feature on the influence of agenda-setting. Besides, this folder also contains copies of the results of t-tests and ANOVA.-"Figure" contains the R code for creating Figure 1 and Figure 2 in the main text of this study and also contains copies of these two figures.

This dataset is about news. It has 1 row and is filtered where the keywords includes Education-Economic aspects-China. It features 10 columns including source, publication date, section, and news link.

Dataset - Education-Economic aspects-China in the news

Leading Economic Index China increased to 148.90 in May of 2025 over the same month in the previous year. This dataset provides - China Leading Economic Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

China New Year's Day: Transport: Passenger Traffic data was reported at 128.126 Person-Time mn in 2024. This records an increase from the previous number of 86.185 Person-Time mn for 2022. China New Year's Day: Transport: Passenger Traffic data is updated yearly, averaging 107.156 Person-Time mn from Dec 2022 (Median) to 2024, with 2 observations. The data reached an all-time high of 128.126 Person-Time mn in 2024 and a record low of 86.185 Person-Time mn in 2022. China New Year's Day: Transport: Passenger Traffic data remains active status in CEIC and is reported by Ministry of Transport. The data is categorized under China Premium Database’s Holiday Economy – Transport.

China New Year's Day: Holiday Entry-Exit: Entry-Exit Passenger: Daily Average: YoY data was reported at 4.500 % in 2025. China New Year's Day: Holiday Entry-Exit: Entry-Exit Passenger: Daily Average: YoY data is updated yearly, averaging 4.500 % from Dec 2025 (Median) to 2025, with 1 observations. The data reached an all-time high of 4.500 % in 2025 and a record low of 4.500 % in 2025. China New Year's Day: Holiday Entry-Exit: Entry-Exit Passenger: Daily Average: YoY data remains active status in CEIC and is reported by National Immigration Administration. The data is categorized under China Premium Database’s Holiday Economy – Entry-Exit.

Iron ore and steel sectors in China show resilience despite US tariffs, driven by strong domestic demand and infrastructure needs.

Evolution, trends - Education-Economic aspects-China in the news

Sources distribution - Education-Economic aspects-China in the news

China New Year's Day: Holiday Entry-Exit: Entry-Exit Passenger: Daily Average data was reported at 1,803.000 Person-Time th in 2025. This records an increase from the previous number of 1,726.000 Person-Time th for 2024. China New Year's Day: Holiday Entry-Exit: Entry-Exit Passenger: Daily Average data is updated yearly, averaging 1,758.000 Person-Time th from Dec 2018 (Median) to 2025, with 6 observations. The data reached an all-time high of 1,900.000 Person-Time th in 2019 and a record low of 303.000 Person-Time th in 2023. China New Year's Day: Holiday Entry-Exit: Entry-Exit Passenger: Daily Average data remains active status in CEIC and is reported by National Immigration Administration. The data is categorized under China Premium Database’s Holiday Economy – Entry-Exit.

Sentiment polarity - Education-Economic aspects-China in the news