Internet-based Consumer Price Index

Online Prices and Inflation during the Nationwide COVID-19 Quarantine Period: Evidence from 107 Chinese Websites

Author: Tingfeng Jianga , Taoxiong Liub , Ke Tangb,* , Jiaqing Zengb
(a : School of Banking and Finance, University of International Business and Economics, China; b: School of Social Science, Tsinghua University, China)

Time：2022-07-19

Abstract: Given the lack of activity in China’s offline economy during the COVID-19 quarantine period, online prices provide new insights for analyzing the impacts of the pandemic on the economy. Using online prices from 107 websites in China and the DiD method to remove the Spring Festival effect, we show that the pandemic leads to a 0.4% surge in the overall inflation rate, a 20% decrease in the price change probability, and a 1% decline in the size of absolute price changes. Moreover, the pandemic had heterogeneous impacts on different sectors, leading to significant structural changes in inflation. Specifically, the pandemic hindered the price correction behavior after Spring Festival, and whether products could be consumed while customers stayed at home was an important factor affecting price adjustment and inflation dynamics.
Keywords: COVID-19 pandemic, online prices, stay-at-home economy, inflation, price stickiness
Suggested Citation:
Jiang T.F., Liu T.X., Tang K., Zeng J.Q. (2022). Online Prices and Inflation during the Nationwide COVID-19 Quarantine Period: Evidence from 107 Chinese Websites[J]. Finance Research Letters, 49, 103166, https://doi.org/10.1016/j.frl.2022.103166.

Design and Application of Novel CPI Based on Online Big Data

Author：Liu Taoxiong 1,2 Tang Ke 1,2 Jiang Tingfeng 3 Zhang Li 4

( 1. Institute of Economics, School of Social Sciences, Tsinghua University;
2. Institute for Innovation and Development, Tsinghua University;
3. School of Banking & Finance, University of International Business and Economics；
4. Institute of World Economics and Politics, Chinese Academy of Social Sciences)

Time：2019-09-09

Research Objectives: The paper aims to explore the design and application of real-time high-frequency price indicators with online big data in the era of digital economy. Research Methods: We design the first set of Internet-based Consumer Price Indices (iCPI) in China, and analyze the index quality and its applications from multiple aspects. Research Findings: Firstly, iCPI can realize real-time updating of daily, weekly, and monthly indices of various levels of products and service (see official website www.bdecon.com). Secondly, iCPI is automatically generated in the computation procedure from the data collection, cleaning, processing and final online publishing, which effectively saves time and avoids human intervention. Thirdly, High-frequency iCPI performs well in representing general price changes, accurately capturing the effects of special events, nowcasting the inflation and real-time monitoring the macroeconomic situation. Research Innovations: We firstly employ online big data to design CPI in China, which makes up for the research gap of real-time high-frequency price indicators. Research Value: Online iCPI is the beneficial complement of official CPI, and the proposed realization roadmap could be applied to construct different high-frequency macroeconomic indicators in the future.
Key Words: Online Big Data; iCPI; Real-time High-Frequency Indicators; Macroeconomic Nowcasting
Suggested Citation:
Liu T X, Tang K, Jiang T F, Zhang L, (2019). Design and Application of Novel CPI Based on Online Big Data [J]. The Journal of Quantitative & Technical Economics, 9:81-101.

The Stickiness of Chinese Online Prices

Author：JIANG Tingfeng a TANG Ke b, c and LIU Taoxiong b,c

(a: School of Banking & Finance, University of International Business and Economics;
b: Institute of Economics, School of Social Sciences, Tsinghua University;
c: Institute for Innovation and Development, Tsinghua University)

Time：2020-06-20

Abstract: The rapid development of the digital economy and big data technology has profound impacts on the economy and society. In this paper, we measure static price stickiness indicators and identify the dynamic price adjustment patterns in China using a unique daily online price dataset. The data come from the iCPI project (www.bdecon.com) of Tsinghua University and contain online prices from more than 100 websites covering the whole basket of goods used in the Chinese Consumer Price Index (CPI) , with over 19 million price records broken down into 8 divisions,46 groups and 262 classes. We find that online prices in China are not very sticky: the average price change duration is less than two months (about 45 days), lower than those found by most other studies. The weighted average absolute price change is about 14%, higher than what is found in the literature. The overall price-adjusting frequency is about 17% higher during price surges relative to price drops. The magnitude of the price increases is about 38% higher on average than that of the price decreases but with heterogeneity by class. Additionally, the empirical results indicate that the price adjustment pattern in China follows a typical combination of the time-dependent pricing model (TDP) and the state-dependent pricing model (SDP). The price change frequency, kurtosis of the price change size, price adjustment mode and heterogeneity can all affect monetary non-neutrality. Considering more heterogeneous sectors improves the explanation for economic fluctuations. This paper provides insights into the impacts of the digital economy on price adjustments and macroeconomic dynamics.
Keywords: Online Big Data; Price Stickiness; Price Adjustment Model; Monetary Non-neutrality; Digital Economy Era
Suggested Citation:
Jiang T F, Tang K, Liu T X, (2020). The Stickiness of Online Prices in China [J]. Economic Research Journal, 55(6): 56-72.

Platform Competition and Market Segmentation
---- Evidence from China’s Online Market

Author：Zhen SUN 1 Jianping LIU 2 Taoxiong LIU 1

(1.School of Social Sciences, Tsinghua University, Beijing 100084, China;
2. E Fund Management Co., Ltd., Beijing 100033, China)

Time：2021-06-22

Abstract: Using a unique dataset from Tsinghua University's iCPI project, we investigate platform competition and market segmentation in China's online market. Using price dispersion as a measurement of market integration and segregation, we find a significant positive relation between price dispersion and the number of platforms. We further investigate the movements of price and price dispersion before and after the change in number of platform, and find that consumers do not substantially change their prices responding to the new competitors. These findings are consistent with platforms segmenting the market when they compete for consumers. Therefore, in China, platform competition does not lead to market integration, but instead further market segmentation. Furthermore, using the ranking of prices across platforms, we find the relative ranking switches within a 2-month observational period for over half of the products, suggesting that platform services are not the major factors in contributing to the price dispersion. Lastly, we find that the relation between price dispersion and platform competition turns out to be stronger during weekends, when search cost is considerably lower and competition is more intense, ruling out convolution of the findings due to search cost. The findings provide some new empirical evidence regarding the cross-platform competition and platform market segmentation, and have implications on government’s anti-trust policies and regulation of platforms.
Key Words: platform market segmentation, price dispersion, platform competition
Suggested Citation:
Sun Z, Liu J P, Liu T X, (2020). Cross-platform competition and market segmentation in China's online market -- evidence based on paltform prices and price dispersion[J]. China Industrial Economics, 6: 118-136.

Nowcasting the Inflation with Online Big Data

JIANG Tingfeng 1 , TANG Ke 2 , LIU Taoxiong 2, 3
( 1. School of Banking & Finance, University of International Business and Economics, Beijing 100029, China; 2. School of Social Sciences, Tsinghua University, Beijing 100084, China; 3. Institute for Innovation and Development, Tsinghua University, Beijing 100084, China)

Time：2022-08-01

Abstract:The COVID-19 epidemic has led to structural changes in the economy, posing new challenges to inflation forecasting. Meanwhile, the arrival of big data era has provided new opportunities for improving the timeliness of inflation prediction. Accordingly, this paper focuses on nowcasting the inflation with big data, and proposes a basic framework for the nowcasting by introducing new real-time macroeconomic indicators and prediction methods. We adopt the Internet-based Consumer Price Indices (iCPI), including daily, weekly, ten-day and monthly frequencies of the total category and eight divisions, and employ the least absolute shrinkage and selection operator (LASSO) and the Mixed Data Sampling (MIDAS) model, which effectively improves the timeliness and accuracy of inflation prediction. It is found that iCPI at different frequencies are conducive to improving the accuracy of inflation prediction, and their performances are better than the benchmark model and traditional indicators of the same frequency. Specifically, when iCPI are combined with traditional indicators, the prediction error can be further reduced, indicating the necessity of incorporating some traditional variables and methods. Besides, with different frequencies indicators (except daily ones), the prediction accuracy of eight divisions of iCPI is better than that of the total category. Big data indicators of various frequencies have different advantages in the accuracy and timeliness of inflation prediction, which is related to the information structure reflected by them. In particular, the nowcasting performance of ten-day year-on-year iCPI is quite prominent, which can better balance the timeliness and accuracy of nowcasting. This paper provides a beneficial reference for employing big data to improve the accuracy and timeliness of inflation prediction and innovating the macroeconomic monitoring system in the digital economy era.
Key Words: inflation rate; big data; nowcasting, iCPI; MIDAS
Suggested Citation:
Jiang T F, Tang K, Liu T X, (2020). Nowcasting the Inflation with Online Big Data[J]. China Journal of Econometrics, 2(3): 597-619.