Ning Jingxin | Tariffs Harm the Poor Even More: An Empirical Study Based on Household Consumption du

12moon5On that day, the event was hosted by the Tsinghua University PBC School of Finance and co-organized by the Tsinghua University PBC School of Finance International Finance and Economics Research Center.CIFERThe "organized by"2025International Trade Disputes and the Restructuring of Globalization: A Symposium on New International Economics"Successfully held in Beijing."

Foreign economic and tradeUniversityLecturer Ning JingxinAttendees were from the University of International Business and EconomicsDu Yingxin, Associate Researcher at the China Institute of World Trade OrganizationSub-forums hostedtwo"International trade disputes"Share its title:Tariffs hurt the poor the most: Empirical evidence of household consumption during the US-China trade war》（Tariffs Tax the Poor More: Evidence from Household Consumption During the US-China Trade War(Article).

Ning JingxinLecturer and Master's Supervisor at the University of International Business and Economics.2023He graduated from the School of Economics and Management at Tsinghua University in [year] with a Ph.D. in Economics. His main research areas are international trade and economic development. His research findings have been published in [journal name missing].China Economic Review, China & World Economy， China Economic Quarterly International,Published in domestic and international journals such as *International Trade* and *Education and Economics*. Participated in major projects funded by the National Social Science Fund of China, emergency projects funded by the National Natural Science Foundation of China, and general projects funded by the National Natural Science Foundation of China.

In her presentation, Ning Jingxin pointed out that the US-China trade war has had a more regressive negative impact on the cost of living for low-income families. This research is based on a two-layered nested approach.THESEModel, usingNielsenIQProvided "family"-Product Barcode-Calculate the high-frequency expenditure data at the micro level of "time" dimension.2018to2019During that period, the US tariffs on China led to an average increase in the cost of living for American families.1.09%More importantly, the impact of tariffs varies significantly across different income groups: the lowest income group (Bottom 20%The cost of living in the highest-income group has increased more than that in the highest-income group.Top 20%(Higher than)0.88Percentage points. This is achieved through adjustments to price increases, expenditure share, and...Reduction of categoriesThrough quantitative analysis of the three channels, the article finds that this distributional disparity is primarily attributed to the stronger consumption substitution capacity of affluent families.——They can adjust their spending structure more flexibly, and at the external margin (extensive marginThe losses from reducing product categories are relatively lower.

Wei Yaning, Lecturer at Zhongnan University of Economics and LawComment on the articleHe believes this article is highly innovative in its research perspective and data selection. By combining micro-level household consumption behavior with macro-level trade policy shocks, it is the first to use barcode-level scanning data to assess the distributive effects of trade wars. It not only accurately identifies the heterogeneous responses of households with different incomes in terms of product category selection and expenditure share, but also quantifies the relative contributions of each channel through structural index decomposition, greatly enriching the research on the micro-mechanisms of trade friction redistribution effects. However, regarding potential problems with the article, he points out: First, in terms of data, existing barcode data may have biases in accurately capturing the coverage of products impacted by tariffs. Second, regarding the identification strategy, he suggests introducing a more stringent control group, while controlling for shocks on both the supply and demand sides, to more clearly isolate changes in demand factors and enhance the persuasiveness of causal inferences. Finally, regarding the reliability of model parameters, given that price index decomposition is highly dependent on parameter settings, he suggests supplementing the report with parameter distribution and confidence intervals, and introducing multiple robustness tests to cross-validate the reliability of the results.