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).

ObjectiveThe objective of this study was to identify factors influencing the development of China-ASEAN trade- from the total economic volume of both sides, distance, the population size of ASEAN countries, the construction of a free trade area, and the signing of the Belt and Road initiative, resource endowment per capita, the exchange rate between RMB and ASEAN countries, and the land area of ASEAN countries—to develop a conceptual framework for China-ASEAN trade potential.Study designThis study uses panel data from 2001 to 2021 that is evenly distributed among 10 ASEAN countries to serve as the dataset. Firstly, the unit roots are checked and the cointegration relationships are examined, focusing on the heterogeneity test. Based on the classical trade gravity model, the innovative trade gravity model with key influencing factors is constructed. On the basis of the classical trade gravity model, an innovative trade gravity model of key influencing factors is constructed. The trade potential model is used to calculate the direct trade potential coefficient between China and ASEAN countries, which points out the direction for the sustainability of bilateral trade.ResultsThis study finds that among the factors affecting China-ASEAN bilateral trade, the total economic output of both sides, distance, population size of ASEAN countries, the construction of the FTA, and the signing of the Belt and Road Initiative all have a positive impact on bilateral trade. Three influencing factors, namely per capita resource endowment, exchange rate between RMB and ASEAN countries, and the size of ASEAN countries, have a negative impact on bilateral trade, but to a lesser extent. The trade potential between China and Vietnam falls into the category of potential re-modelling, indicating that both sides are currently utilizing their trade potential to the greatest extent possible, that trade growth space is limited, and that new trade opportunities must be discovered. The trade potential index between China and nine ASEAN countries, excluding Vietnam, is in the potential-exploiting category, indicating that the potential has not been fully utilized by both sides and that there is still room for growth in the scale of trade between the two countries.ConclusionWith the shift of the world’s economic center of gravity in the direction of Asia following COVID-19, China and ASEAN countries should seize the opportunity to strengthen their comprehensive strength and economic aggregates and further develop China’s constructive role in the regional organization. The signing of the Belt and Road Initiative and the construction of a free trade zone has had a positive effect on the development of bilateral trade. Propose that: positive trade factors should continue to be strengthened, trade barriers should be removed, and new dynamics of bilateral trade growth should be enhanced.

This data provides a comprehensive view into how residents in key Belt and Road Initiative cities perceive China’s influence across multiple aspects of daily life. It captures nuanced sentiment related to economic opportunity, job creation, debt concerns, political influence, and infrastructure improvement, and it also produces an Overall Sentiment Index that brings these perspectives together in a single benchmark score. By incorporating demographic details such as age, income, and household type, the data creates a multidimensional understanding of how different groups within each city view China’s role. What makes this resource especially powerful is that it is not limited to a one-time snapshot. It is designed to be run repeatedly on a weekly, monthly, or quarterly basis so that changes in perception can be tracked over time and interpreted in context.

Each of the sentiment dimensions tells a different story. Economic opportunity captures whether residents believe Chinese involvement is creating pathways for trade, investment, and business growth. Job creation measures whether these investments translate into employment for locals or remain limited to outside contractors. Debt concerns reflect whether residents feel financing arrangements are sustainable or whether they put their country at risk. Political influence expresses how much China is seen as shaping governance, elections, or policy priorities. Infrastructure improvement reflects the tangible benefits that people associate with new ports, power plants, railways, or digital networks. When these are combined into the Overall Sentiment Index, it becomes possible to see a distilled score for each city at any given time while retaining the ability to drill down into the drivers of that score.

Running this data once is valuable because it shows the present balance of perception. Running it regularly transforms it into a monitoring tool. Over time, it becomes clear whether optimism is building or eroding, whether concerns are intensifying or easing, and whether residents feel more or less positively about China’s role in their city. Weekly runs allow short-term fluctuations to be observed, which is especially important when external events like debt renegotiations or infrastructure launches occur. Monthly runs strike a balance, capturing trends that are still timely but not so volatile that they obscure underlying movement. Quarterly runs provide a strategic rhythm that aligns with government planning cycles, investor reporting, and long-term program design. Whatever cadence is chosen, the ability to compare one wave of sentiment to the next adds an entirely new layer of value.

Consider the implications for economic opportunity. In one quarter, residents may feel optimistic because new trade zones are announced, but by the next quarter that optimism may fade if jobs or contracts do not materialize locally. Debt concerns may remain stable for months and then spike suddenly when repayment deadlines become politically controversial. Infrastructure satisfaction may begin high at the ribbon-cutting of a new port but then decline if maintenance is poor or if local communities feel excluded from its benefits. Political influence sentiment may ebb and flow with election cycles, reflecting moments when Chinese involvement is spotlighted in domestic debates. Without recurring data, these shifts would be invisible or anecdotal. With recurring data, they become measurable, comparable, and actionable.

Demographic segmentation intensifies the usefulness of this time-series view. Younger residents may consistently report higher enthusiasm for economic opportunity, while older residents may be more cautious. Over time, the gap between those groups can widen or narrow, revealing intergenerational dynamics that matter for future policy and business planning. Lower-income households may express higher debt concerns, while wealthier households emphasize infrastructure benefits. Families with children may be focused on long-term job creation, while singles are more attuned to short-term opportunities. Seeing these divergences move over time is more valuable than seeing them once because it highlights whether divisions are hardening, softening, or shifting to new areas.

The geographic coverage of this data spans ten strategically important BRI cities, from Karachi and Colombo to Nairobi, Addis Ababa, Almaty, Athens, Gwadar, Jakarta, Dushanbe, and Belgrade. These cities were selected not only for their individual significance but also because, taken together, they represent a cross-section of the initiative’s global reach. By comparing sentiment across these cities at multiple time points, it becomes possible to identify where China’s influence is gaining legitimacy, where it is facing skepticism, and how those dynamics differ between regions. The standardized structure of the data ensures that these comparisons are meaningful, turning local snapshots into part of a...

ObjectiveThe objective of this study was to identify factors influencing the development of China-ASEAN trade- from the total economic volume of both sides, distance, the population size of ASEAN countries, the construction of a free trade area, and the signing of the Belt and Road initiative, resource endowment per capita, the exchange rate between RMB and ASEAN countries, and the land area of ASEAN countries—to develop a conceptual framework for China-ASEAN trade potential.Study designThis study uses panel data from 2001 to 2021 that is evenly distributed among 10 ASEAN countries to serve as the dataset. Firstly, the unit roots are checked and the cointegration relationships are examined, focusing on the heterogeneity test. Based on the classical trade gravity model, the innovative trade gravity model with key influencing factors is constructed. On the basis of the classical trade gravity model, an innovative trade gravity model of key influencing factors is constructed. The trade potential model is used to calculate the direct trade potential coefficient between China and ASEAN countries, which points out the direction for the sustainability of bilateral trade.ResultsThis study finds that among the factors affecting China-ASEAN bilateral trade, the total economic output of both sides, distance, population size of ASEAN countries, the construction of the FTA, and the signing of the Belt and Road Initiative all have a positive impact on bilateral trade. Three influencing factors, namely per capita resource endowment, exchange rate between RMB and ASEAN countries, and the size of ASEAN countries, have a negative impact on bilateral trade, but to a lesser extent. The trade potential between China and Vietnam falls into the category of potential re-modelling, indicating that both sides are currently utilizing their trade potential to the greatest extent possible, that trade growth space is limited, and that new trade opportunities must be discovered. The trade potential index between China and nine ASEAN countries, excluding Vietnam, is in the potential-exploiting category, indicating that the potential has not been fully utilized by both sides and that there is still room for growth in the scale of trade between the two countries.ConclusionWith the shift of the world’s economic center of gravity in the direction of Asia following COVID-19, China and ASEAN countries should seize the opportunity to strengthen their comprehensive strength and economic aggregates and further develop China’s constructive role in the regional organization. The signing of the Belt and Road Initiative and the construction of a free trade zone has had a positive effect on the development of bilateral trade. Propose that: positive trade factors should continue to be strengthened, trade barriers should be removed, and new dynamics of bilateral trade growth should be enhanced.

ObjectiveThe objective of this study was to identify factors influencing the development of China-ASEAN trade- from the total economic volume of both sides, distance, the population size of ASEAN countries, the construction of a free trade area, and the signing of the Belt and Road initiative, resource endowment per capita, the exchange rate between RMB and ASEAN countries, and the land area of ASEAN countries—to develop a conceptual framework for China-ASEAN trade potential.Study designThis study uses panel data from 2001 to 2021 that is evenly distributed among 10 ASEAN countries to serve as the dataset. Firstly, the unit roots are checked and the cointegration relationships are examined, focusing on the heterogeneity test. Based on the classical trade gravity model, the innovative trade gravity model with key influencing factors is constructed. On the basis of the classical trade gravity model, an innovative trade gravity model of key influencing factors is constructed. The trade potential model is used to calculate the direct trade potential coefficient between China and ASEAN countries, which points out the direction for the sustainability of bilateral trade.ResultsThis study finds that among the factors affecting China-ASEAN bilateral trade, the total economic output of both sides, distance, population size of ASEAN countries, the construction of the FTA, and the signing of the Belt and Road Initiative all have a positive impact on bilateral trade. Three influencing factors, namely per capita resource endowment, exchange rate between RMB and ASEAN countries, and the size of ASEAN countries, have a negative impact on bilateral trade, but to a lesser extent. The trade potential between China and Vietnam falls into the category of potential re-modelling, indicating that both sides are currently utilizing their trade potential to the greatest extent possible, that trade growth space is limited, and that new trade opportunities must be discovered. The trade potential index between China and nine ASEAN countries, excluding Vietnam, is in the potential-exploiting category, indicating that the potential has not been fully utilized by both sides and that there is still room for growth in the scale of trade between the two countries.ConclusionWith the shift of the world’s economic center of gravity in the direction of Asia following COVID-19, China and ASEAN countries should seize the opportunity to strengthen their comprehensive strength and economic aggregates and further develop China’s constructive role in the regional organization. The signing of the Belt and Road Initiative and the construction of a free trade zone has had a positive effect on the development of bilateral trade. Propose that: positive trade factors should continue to be strengthened, trade barriers should be removed, and new dynamics of bilateral trade growth should be enhanced.

ObjectiveThe objective of this study was to identify factors influencing the development of China-ASEAN trade- from the total economic volume of both sides, distance, the population size of ASEAN countries, the construction of a free trade area, and the signing of the Belt and Road initiative, resource endowment per capita, the exchange rate between RMB and ASEAN countries, and the land area of ASEAN countries—to develop a conceptual framework for China-ASEAN trade potential.Study designThis study uses panel data from 2001 to 2021 that is evenly distributed among 10 ASEAN countries to serve as the dataset. Firstly, the unit roots are checked and the cointegration relationships are examined, focusing on the heterogeneity test. Based on the classical trade gravity model, the innovative trade gravity model with key influencing factors is constructed. On the basis of the classical trade gravity model, an innovative trade gravity model of key influencing factors is constructed. The trade potential model is used to calculate the direct trade potential coefficient between China and ASEAN countries, which points out the direction for the sustainability of bilateral trade.ResultsThis study finds that among the factors affecting China-ASEAN bilateral trade, the total economic output of both sides, distance, population size of ASEAN countries, the construction of the FTA, and the signing of the Belt and Road Initiative all have a positive impact on bilateral trade. Three influencing factors, namely per capita resource endowment, exchange rate between RMB and ASEAN countries, and the size of ASEAN countries, have a negative impact on bilateral trade, but to a lesser extent. The trade potential between China and Vietnam falls into the category of potential re-modelling, indicating that both sides are currently utilizing their trade potential to the greatest extent possible, that trade growth space is limited, and that new trade opportunities must be discovered. The trade potential index between China and nine ASEAN countries, excluding Vietnam, is in the potential-exploiting category, indicating that the potential has not been fully utilized by both sides and that there is still room for growth in the scale of trade between the two countries.ConclusionWith the shift of the world’s economic center of gravity in the direction of Asia following COVID-19, China and ASEAN countries should seize the opportunity to strengthen their comprehensive strength and economic aggregates and further develop China’s constructive role in the regional organization. The signing of the Belt and Road Initiative and the construction of a free trade zone has had a positive effect on the development of bilateral trade. Propose that: positive trade factors should continue to be strengthened, trade barriers should be removed, and new dynamics of bilateral trade growth should be enhanced.