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Building the "Tariff Impact Index" — Integrating Multi-Platform API Data
Introduction
In today's landscape of volatile global trade, tariffs have become a critical variable influencing product prices. Especially amid the global trade contraction in 2025, effectively assessing the impact of tariffs on product pricing has become a pressing challenge for cross-border e-commerce, supply chain companies, and international trade analysts. Traditional product pricing monitoring systems often rely on single-platform data, lacking the capability to perform multi-dimensional comparisons and tariff-adjusted corrections. In response, this article proposes a methodology for constructing the "Tariff Impact Index" (TII), leveraging API data from multiple e-commerce platforms such as Walmart, Amazon, and TikTok Shop provided by LuckData. The goal is to quantify and visualize tariff shock effects, offering more precise pricing strategies and trade decision support for market participants.
1. Background and Motivation: Global Pricing Games Under Tariff Shocks
In 2025, the global economy has entered an adjustment cycle. According to various forecasts, global merchandise trade is expected to decline by 0.2%, with North American exports dropping by as much as 12.6%. In this environment, frequent adjustments in international tariff policies—particularly the sharp fluctuations between the U.S. and China—have emerged as the primary factor affecting cross-border product prices. Meanwhile, differences in platform pricing mechanisms across countries, logistics costs, platform commissions, and exchange rate volatility have further widened price gaps for the same products across different platforms.
In this complex context, price tags on e-commerce platforms have become more than just a reflection of supply and demand—they serve as barometers of global trade policy. Comparing the prices of identical products across platforms and correcting for tariff policies provides the foundation for a novel metric: the Tariff Impact Index (TII).
2. Core Concept and System Framework
2.1 Definition and Objective of the Index
The "Tariff Impact Index" (TII) is a relative metric that quantifies the impact of tariff policies on product pricing. The core idea is to compare the prices of identical products across platforms and contrast the observed price variation with the theoretically expected variation due to tariffs, yielding a standardized index that measures the effect of tariffs on price changes.
Calculation Formula:
TII=ΔPobservedΔPtariff\text{TII} = \frac{\Delta P_{\text{observed}}}{\Delta P_{\text{tariff}}}
ΔPobserved\Delta P_{\text{observed}}: Actual price difference observed on platforms (difference between weighted average platform price and baseline price)
ΔPtariff\Delta P_{\text{tariff}}: Theoretical price difference estimated from tariff rates between the country of origin and the destination country
TII = 1: Price change fully driven by tariffs
TII > 1: Additional cost drivers besides tariffs (e.g., logistics, platform markup)
TII < 1: Tariff impact neutralized by subsidies, promotions, or platform pricing strategies
2.2 System Architecture
The full system comprises the following five core modules:
Platform Data Collection Module: Acquires product price data from Walmart, Amazon, TikTok Shop via LuckData API
Tariff Rate Parsing Module: Integrates official customs or third-party trade databases for up-to-date tariff information
Price Correction and Weighted Calculation Module: Compares same SKU across platforms, correcting for exchange rates, commissions, logistics, etc.
TII Generation and Visualization Module: Produces standardized TII values and visualizations such as time series plots or regional comparison charts
Alert and Reporting Module: Sends alerts via email/Slack/WeChat or generates PDF reports when TII fluctuates beyond thresholds
3. Data Collection and Modeling Process
3.1 Multi-Platform Price Data Collection
Using LuckData’s high-performance API, developers can fetch product prices and stock status for any SKU on major platforms. For example, retrieving Walmart product details via API:
import requestsheaders = {
'X-Luckdata-Api-Key': 'your_luckdata_key'
}
url = 'https://www.walmart.com/ip/NELEUS-Mens-Dry-Fit-Mesh-Athletic-Shirts/439625664'
response = requests.get(
f'https://luckdata.io/api/walmart-API/get_vwzq?url={url}',
headers=headers
)
data = response.json()
print("Product Name:", data['title'])
print("Current Price:", data['price'])
print("Stock Status:", data['stock_status'])
Similar APIs are available for Amazon and TikTok Shop, supporting extraction of price, review counts, and sales trends, enabling automated construction of SKU comparison matrices.
3.2 Retrieving Real-Time Tariff Information
For imported products, tariff policies vary significantly by country. Recommended sources for real-time tariff data include:
Public data from the World Trade Organization (WTO)
National customs authority APIs
Third-party tariff APIs (e.g., MarketAccess, ImportGenius)
By matching HS Codes and country-of-origin data with import regulations, theoretical tariff increases can be determined.
3.3 Actual Price Difference and Index Calculation
Observed platform price differences should be adjusted for factors such as:
Exchange rate fluctuations
Shipping cost (estimated by product volume/weight)
Platform service fees and commissions
Regional promotional strategies
All data should be normalized to local currency or USD. TII values are then generated and stored in a database for daily, weekly, and monthly trend analysis.
4. Visualization and Use Case Analysis
4.1 TII Visualization Charts
Use Matplotlib or Plotly to draw TII time series plots. Policy events (e.g., tariff hikes or trade agreement signings) can be annotated to help business analysts detect inflection points.
import matplotlib.pyplot as pltimport pandas as pd
df = pd.read_csv("tii_data.csv")
df['date'] = pd.to_datetime(df['date'])
plt.plot(df['date'], df['tii'], marker='o', label="TII Value")
plt.axvline(x=pd.to_datetime("2025-04-09"), color='red', linestyle='--', label="US Tariff Hike on China")
plt.title("Tariff Impact Index (TII) Trend")
plt.xlabel("Date")
plt.ylabel("TII Value")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()
4.2 Case Studies
Case 1: U.S. E-commerce Sales of China-Made Apparel
In April 2025, the U.S. raised tariffs on this category from 25% to 145%. The product price rose from $20 to $32 on Walmart and Amazon, but only to $27 on TikTok Shop due to slower inventory rotation. The TII value reached 1.4, indicating that businesses should prepare by setting up overseas warehouses or sourcing from alternative countries.
Case 2: Southeast Asian Platforms Selling U.S.-Made Electronics
After a tariff increase to 75%, the product price only rose by 12%, resulting in a TII of 0.6, suggesting that the platform may be offering subsidies or promotional pricing. Caution is needed for potential price rebounds once subsidies are removed.
5. Future Extensions and Advanced Features
5.1 TII Trend Forecasting
Apply machine learning models (e.g., LSTM, XGBoost) to forecast future TII trends and proactively support pricing strategies for operations teams.
5.2 Regional Comparative Analysis
Construct heatmaps or radar charts based on TII values from different national platforms to gain insights into global pricing volatility.
5.3 Multi-Dimensional Driver Analysis
Integrate user search trends, logistics cost indexes, and promotional cycle data to dissect the complex drivers behind TII fluctuations.
Conclusion: Gaining the Upper Hand Amid Pricing Turbulence
Building the Tariff Impact Index is not merely a technical endeavor but a strategic capability for navigating global trade complexities. By integrating multi-platform e-commerce data, real-time tariff rates, and visualization tools, companies can move beyond intuition-based decisions to data-driven responses to policy shifts.
In an era marked by fragmented supply chains and policy volatility, those who harness data hold the future. The Tariff Impact Index (TII) is set to become a core asset in this new global game.
