China recorded a Government Debt to GDP of 88.30 percent of the country's Gross Domestic Product in 2024. This dataset provides - China Government Debt To GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Holdings of US Treasury Securities data was reported at 784.300 USD bn in Feb 2025. This records an increase from the previous number of 760.802 USD bn for Jan 2025. Holdings of US Treasury Securities data is updated monthly, averaging 937.400 USD bn from Mar 2000 (Median) to Feb 2025, with 300 observations. The data reached an all-time high of 1,316.700 USD bn in Nov 2013 and a record low of 58.900 USD bn in Nov 2000. Holdings of US Treasury Securities data remains active status in CEIC and is reported by U.S. Department of the Treasury. The data is categorized under China Premium Database’s Government and Public Finance – Table CN.FF: Holdings of US Treasury Securities.

China Foreign Debt: U.S. Dollar data was reported at 966.080 USD bn in Dec 2024. This records a decrease from the previous number of 1,000.160 USD bn for Sep 2024. China Foreign Debt: U.S. Dollar data is updated quarterly, averaging 856.892 USD bn from Dec 2009 (Median) to Dec 2024, with 61 observations. The data reached an all-time high of 1,290.516 USD bn in Mar 2022 and a record low of 180.883 USD bn in Dec 2009. China Foreign Debt: U.S. Dollar data remains active status in CEIC and is reported by State Administration of Foreign Exchange. The data is categorized under China Premium Database’s Government and Public Finance – Table CN.FA: Foreign Debt: Quarterly.

The yield on China 10Y Bond Yield rose to 1.64% on June 24, 2025, marking a 0.01 percentage point increase from the previous session. Over the past month, the yield has fallen by 0.05 points and is 0.59 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. China 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on June of 2025.

Abstract

This research was carried out in China between December 2011 and February 2013. Data was collected from 2,700 privately-owned and 148 state-owned firms.

The objective of Enterprise Surveys is to obtain feedback from businesses on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

Usually Enterprise Surveys focus only on private companies, but in China, a special sample of fully state-owned establishments was included as this is an important part of the economy. Data on 148 state-owned enterprises is provided separately from the data of 2,700 private sector firms. To maintain comparability of the China Enterprise Surveys to surveys conducted in other countries, only the dataset of privately sector firms should be used.

Geographic coverage

Twenty-five metro areas: Beijing (municipalities), Chengdu City, Dalian City, Dongguan City, Foshan City, Guangzhou City, Hangzhou City, Hefei City, Jinan City, Luoyang City, Nanjing City, Nantong City, Ningbo City, Qingdao City, Shanghai (municipalities), Shenyang City, Shenzhen City, Shijiazhuang City, Suzhou City, Tangshan City, Wenzhou City, Wuhan City, Wuxi City, Yantai City, Zhengzhou City.

Analysis unit

The primary sampling unit of the study is an establishment.The establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or universe of the study, is the non-agricultural economy of firms with at least 5 employees and positive amounts of private ownership. The non-agricultural economy comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for China ES was selected using stratified random sampling. Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the following way: the universe was stratified into 11 manufacturing industries and 7 services industries as defined in the sampling manual. Each manufacturing industry had a target of 150 interviews. Sample sizes were inflated by about 20% to account for potential non-response cases when requesting sensitive financial data and also because of likely attrition in future surveys that would affect the construction of a panel. Note that 100% government owned firms are categorized independently of their industrial classification. The 148 surveyed state-owned enterprises were categorized as a separate sector group to preserve the representativeness of other sector groupings for the private economy.

Size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

Regional stratification was defined in twenty-five metro areas: Beijing (municipalities), Chengdu City, Dalian City, Dongguan City, Foshan City, Guangzhou City, Hangzhou City, Hefei City, Jinan City, Luoyang City, Nanjing City, Nantong City, Ningbo City, Qingdao City, Shanghai (municipalities), Shenyang City, Shenzhen City, Shijiazhuang City, Suzhou City, Tangshan City, Wenzhou City, Wuhan City, Wuxi City, Yantai City, Zhengzhou City.

The sample frame was obtained by SunFaith from SinoTrust.

The enumerated establishments were then used as the frame for the selection of a sample with the aim of obtaining interviews at 3,000 establishments with five or more employees. The quality of the frame was assessed at the onset of the project through calls to a random subset of firms and local contractor knowledge. The sample frame was not immune from the typical problems found in establishment surveys: positive rates of non-eligibility, repetition, non-existent units, etc.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments are needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 31% (6,485 out of 20,616 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available: - Services Questionnaire, - Manufacturing Questionnaire, - Screener Questionnaire.

The Services Questionnaire is administered to the establishments in the services sector. The Manufacturing Questionnaire is built upon the Services Questionnaire and adds specific questions relevant to manufacturing.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

The number of contacted establishments per realized interview was 7.24. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.55.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond as a different option from don’t know. b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

Explore real GDP growth projections dataset, including insights into the impact of COVID-19 on economic trends. This dataset covers countries such as Spain, Australia, France, Italy, Brazil, and more.

growth rate, Real, COVID-19, GDP

Spain, Australia, France, Italy, Brazil, Argentina, United Kingdom, United States, Canada, Russia, Turkiye, World, China, Mexico, Korea, India, Saudi Arabia, South Africa, Germany, Indonesia, JapanFollow data.kapsarc.org for timely data to advance energy economics research..Source: OECD Economic Outlook database.- India projections are based on fiscal years, starting in April. The European Union is a full member of the G20, but the G20 aggregate only includes countries that are also members in their own right. Spain is a permanent invitee to the G20. World and G20 aggregates use moving nominal GDP weights at purchasing power parities. Difference in percentage points, based on rounded figures.

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 article conducts a comparative analysis of national park legislation, focusing on insights from the US National Park System to inform the development of China's emerging national park framework. Against the backdrop of a global conservation movement, the US system serves as a valuable model for China, which initiated its own national park system in 2017. On October 12, 2021, China has formally established its inaugural set of national parks, comprising the Three-River-Source National Park, the Giant Panda National Park, the Northeast China Tiger and Leopard National Park, the Hainan Tropical Forests National Park, and the Wuyishan National Park. Encompassing approximately 230,000 square kilometres, these five national parks safeguard nearly 30% of China's crucial terrestrial wildlife species. As China endeavours to enact comprehensive legislation for its national parks, this research aims to contribute to the ongoing efforts by addressing key questions such as the efficacy of the US's "One National Park, One Law" model, the governance dynamics between federal, state, and local entities, and strategies for balancing conservation with diverse land uses. The analysis spans five sections, exploring the historical evolution of the US National Park System, its legal framework, challenges faced by US national parks, and policy recommendations for China. The US experience highlights the importance of establishing clear legal authorities, fostering robust public participation mechanisms, and harmonizing relationships with Indigenous communities. The findings presented in this study aspire to facilitate a nuanced understanding of national park legislation, promoting international collaboration between the US and China for the sustainable management of natural lands and the protection of global biodiversity.

Foreign Direct Investment in China increased by 498.80 USD Hundred Million in May of 2025. This dataset provides the latest reported value for - China Foreign Direct Investment - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The dataset is a file of the raw interview scripts with my interviewees during the fieldwork conducted between 2021.6 to 2022.2.

This thesis investigates how urban middle-class working women with two children make sense of work, childcare, and self under the universal two-child policy of China. This thesis also explores how the idea of individual and family interact in these women's construction of a sense of self. On January 1st, 2016, the one-child policy was replaced by the universal two-child policy, under which all married couples in China are allowed to have two children. In the scholarships of motherhood, it is widely documented across cultures that it is a site of patriarchal oppression where women are expected to meet the unrealistic ideal of intensive mothering to be a good mother, suffer from the motherhood wage penalty and face more work-family conflict than fathers. Emprical studies of China also came to similar conclusions and such findings are not only widely regonized in scholarship but is also widespread in popular discourse in China. Despite that marriage and having children is still universal for the generation of the research target, women born in the 1970s and 1980s, due to compounding influence fo the one-child policy, increasing financial burden of raising a child etcs, having only one child has become widely acceptable and normal. Given this context, this study intend to investigate how these middle-class women, who are relatively empowered and resourceful, come to a decision that is seemingly against their own interest. Moreover, unlike in the west where the issue of childbearing and childcaring is mainly an issue of the conjugal couple and the gender realtions is at the center of the discussion, in China, extended family, especially grandparents also play a role in both the decision making process and the subsequent childcare arrangement. Therefore, to study the second-time mothers’ childcare and work experiences in contemporary urban China, we also need to situate them, as individuals, in their family. To investigate how they make sense of childcare and work is also to understand the tension between individual and family. By interviewing twenty-one parents from middle-class family in Guangzhou with a second child under six years old, this study finds that these urban working women with two children consider themselves as an individual unit and full-time paid employment is something that cannot be given up since it is the means of securing that independent self . However, they did not prioritize their personal interest to that of other family members, especially the elder child and thus the decision of having a second child is mainly for the sake of the elder child. Moreover, grandparents played an essential role to provide a childcare safety net, without which, these urban working women would not be able to work full-time and maintain the independent self as they defined it. The portrayal of these women’s experiences reflected the individualization process in China where people are indivdualized without individualism, and family are evoked as strategy to achieve personal as well as family goals. The findings of this study contributs to theories of motherhood by adding an intergenerational perspective to the existing gender perspective and also contributes to the studies of family by understanding the relation and interaction between individual and family in thse women’s construction of sense of self in the context of contemporary China.

This is a dataset used in EnergyDetective 2020, which is a competition on building energy consumption prediction hold in 2020 by Xupeng Research Group.

With the prediction case provided in the competition, which is to predict the energy consumption of a building with some physical information but without historical data of its own, the dataset will include two parts, which is the test building dataset and the reference building dataset. The test building dataset includes more data about the physical description for the building while the reference building dataset includes more historical consumption data.

All the hourly energy consumption data comes from office buildings located in Shanghai, China during 2015-2017. Energy consumption data is divided into two meter type in the data set. Energy cost by lights and plugs is one of the meter type (marked as “Q”) and that cost by the HVAC system is the other(marked as “W”). The consumption records of two meter types are gathered by raw meta data, which comes from a private dataset built by an energy management company.

In the meanwhile, weather data is given in the dataset. And the weather data is collected by a real weather station in Shanghai, China.

In the new version, we provide the historical hourly energy consumption data of the test building.

JP: Foreign Direct Investment Position: Inward: Total: China data was reported at 261,811.864 JPY mn in 2023. This records a decrease from the previous number of 313,154.134 JPY mn for 2022. JP: Foreign Direct Investment Position: Inward: Total: China data is updated yearly, averaging 266,590.627 JPY mn from Dec 2017 (Median) to 2023, with 7 observations. The data reached an all-time high of 313,154.134 JPY mn in 2022 and a record low of 179,935.472 JPY mn in 2017. JP: Foreign Direct Investment Position: Inward: Total: China 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 Japan – Table JP.OECD.FDI: Foreign Direct Investment Position: by Region and Country: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the direct investor. FDI financial flows, income flows and positions include, if they exist, resident Special Purpose Entities (SPEs) which cannot be identified separately. Valuation method used for listed inward and outward equity positions: Own funds at book value, Accumulation of FDI equity flows. Valuation method used for unlisted inward and outward equity positions: Own funds at book value, Accumulation of FDI equity flows. Valuation method used for inward and outward debt positions: Nominal value .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Framework for Direct Investment Relationships (FDIR) method. Debt between fellow enterprises are completely covered . Collective investment institutions are covered as direct investment enterprises. Non-profit institutions serving households are covered as direct investors. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

Foreign Exchange Reserves in China increased to 3285000 USD Million in May from 3282000 USD Million in April of 2025. This dataset provides - China Foreign Exchange Reserves - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Gross Domestic Product (GDP) in China expanded 5.40 percent in the first 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.

Lithuania LT: Foreign Direct Investment Income: Inward: Total: China data was reported at 4.820 EUR mn in 2023. This records an increase from the previous number of 1.850 EUR mn for 2022. Lithuania LT: Foreign Direct Investment Income: Inward: Total: China data is updated yearly, averaging 0.110 EUR mn from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 4.820 EUR mn in 2023 and a record low of -0.890 EUR mn in 2021. Lithuania LT: Foreign Direct Investment Income: Inward: Total: China 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 Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Income: by Region and Country: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

This dataset provides values for GOLD RESERVES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The United States recorded a trade deficit of 61.62 USD Billion in April 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.

This dataset provides values for PRIVATE DEBT TO GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Czech Republic CZ: Foreign Direct Investment Income: Inward: USD: Total: China data was reported at 28.541 USD mn in 2023. This records a decrease from the previous number of 47.461 USD mn for 2022. Czech Republic CZ: Foreign Direct Investment Income: Inward: USD: Total: China data is updated yearly, averaging -13.767 USD mn from Dec 2013 (Median) to 2023, with 11 observations. The data reached an all-time high of 73.532 USD mn in 2021 and a record low of -252.539 USD mn in 2018. Czech Republic CZ: Foreign Direct Investment Income: Inward: USD: Total: China 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 Czech Republic – Table CZ.OECD.FDI: Foreign Direct Investment Income: USD: by Region and Country: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is not applied in the recording of total inward and outward FDi transactions and positions. Such cases have never been observed. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the direct investor. Resident Special Purpose Entities (SPEs) do not exist or are not significant and are recorded as zero in the FDI database. Valuation method used for listed inward and outward equity positions: Own funds at book value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Nominal value.; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Framework for Direct Investment Relationships (FDIR) method. Debt between fellow enterprises are completely covered. Collective investment institutions are covered as direct investment enterprises. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

Czech Republic CZ: Foreign Direct Investment Financial Flows: Outward: USD: Total: Hong Kong SAR (China) data was reported at 4.040 USD mn in 2023. This records a decrease from the previous number of 15.791 USD mn for 2022. Czech Republic CZ: Foreign Direct Investment Financial Flows: Outward: USD: Total: Hong Kong SAR (China) data is updated yearly, averaging 0.180 USD mn from Dec 2016 (Median) to 2023, with 8 observations. The data reached an all-time high of 15.791 USD mn in 2022 and a record low of -3.061 USD mn in 2021. Czech Republic CZ: Foreign Direct Investment Financial Flows: Outward: USD: Total: Hong Kong SAR (China) 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 Czech Republic – Table CZ.OECD.FDI: Foreign Direct Investment Financial Flows: USD: by Region and Country: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is not applied in the recording of total inward and outward FDi transactions and positions. Such cases have never been observed. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the direct investor. Resident Special Purpose Entities (SPEs) do not exist or are not significant and are recorded as zero in the FDI database. Valuation method used for listed inward and outward equity positions: Own funds at book value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Nominal value.; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Framework for Direct Investment Relationships (FDIR) method. Debt between fellow enterprises are completely covered. Collective investment institutions are covered as direct investment enterprises. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.