Summary:
In the wake of a major unforeseen public event, there have been sustained and severe impacts on both supply and demand, with the 2020 COVID-19 outbreak serving as a quintessential example of such a phenomenon. This event prompted widespread adoption of social distancing and home isolation measures across nations, leading to rapid and substantial shifts in consumer behavior, and thereby triggering a range of economic risks. The imperative for governments globally is to accurately assess the economic implications of such events, formulate responsive policies, and ensure stable economic functioning and recovery of employment and production. Nevertheless, a challenge arises with the fixed-basket approach underlying official price indices, which struggles to swiftly reflect notable shifts in consumer spending. This lag exacerbates inaccuracies in inflation measurement, complicating the development of macroeconomic policies, especially in terms of monetary policy formulation. In 2020, economic activities encountered unprecedented shocks, with numerous countries and regions implementing varying degrees of lockdown measures. Access to goods and services was hindered, leading to product scarcity and significant reductions in spending, thereby altering consumption patterns and the diversity of products and promotions. Conventionally, inflation metrics such as the Consumer Price Index (CPI) are constructed upon a static basket of commonly purchased goods. The pandemic's disruption caused a radical change in the actual mix of goods and services consumed, rendering the pre-pandemic basket ineffective in reflecting current consumer spending patterns under such extraordinary public emergencies. This has led to a decrease in the accuracy of inflation and consumption measurements. Research into the deviations of price indices during the pandemic has become a pivotal case for policymakers in adapting to rapidly evolving economic scenarios. Insights derived from the analysis of such unforeseen public events are invaluable for guiding future policy in the face of rapid changes in economic conditions, including consumer behavior. Utilizing data from the 2019-2022 China Urban and Rural Integration Household Survey and real-time U.S. bank card consumption data, this study estimates the post-impact differences in consumption basket weights and CPI discrepancies between China and the U.S. The findings reveal: (1) A marked differentiation in the weights of China's consumption basket, particularly in the early stages of the impact, where CPI underestimation was notable. However, the overall deviation was limited, indicating a more manageable situation in comparison to the U.S. This attenuation of inflation discrepancy post-impact correlates with economic recovery. (2) There are limitations in current methods for correcting inflation measurement discrepancies. The quarterly consumption expenditure surveys, as opposed to real-time data, demonstrate inadequacies. Thus, there is a critical need to refine these surveys and adapt promptly to consumption pattern changes to derive more accurate price indices. In light of these findings, several policy implications are suggested. First, the timely publication of CPI weight information, with increased frequency of weight updates, should be considered. If feasible, releasing detailed weight information for CPI categories and enhancing the frequency of updates can better reflect changes in consumption expenditure structures and reduce biases in inflation index compilation. Second, enhancing and refining consumption expenditure surveys is imperative. This includes addressing the treatment of legacy products, adjusting for product quality, broadening the statistical coverage of goods and services such as financial insurance and legal consulting, and leveraging big data from e-commerce and financial institutions to track expenditure changes. These measures aim to more accurately represent actual resident expenditure and minimize biases in inflation index calculation. Third, the adoption of higher international standards and emphasizing forward-looking inflation assessments are advised. While adhering to the CPI handbook's basic requirements, there should be an active assessment and implementation of international standards and correction methodologies. In the future, utilizing high-frequency trading and other micro data for quantitative analysis can help estimate and correct inflation measurement biases, thereby addressing the lag in consumption expenditure surveys. Building upon this foundation, empirical analysis can be conducted on changes in consumption expenditures and the mechanisms influencing inflation measurement biases under different shocks. The potential contributions of this paper span several dimensions. Firstly, it provides empirical evidence on inflation measurement discrepancies in shock events through a comparative analysis of China's adjusted and official CPI trends during shocks, and by examining discrepancies between China and the U.S. in the early stages of these events. The goal is to identify both commonalities and differences in inflation measurement across both nations under similar shocks, with a view to refining China's CPI measurement approach. Secondly, by focusing on the initial 2020 shock case, the study enriches the understanding of the impact of such events on macroeconomic indicators like consumption expenditure and inflation. It offers a unique perspective on real-time changes in consumption expenditure resulting from demand shocks, providing key insights into the assessment of specific industry impacts and the precise implementation of targeted structural policies. Lastly, in terms of policy response and evaluation, the proposed real-time price index adjustment methodology facilitates prompt identification and calibration of inflation risks during shock events. This methodology offers guidance to fiscal and monetary authorities for accurate price trend monitoring, reducing the risk of inflation miscalculation, and enabling effective shock response strategies. Furthermore, this approach is instrumental in leveraging real-time data to inform decision-making, thereby enhancing the efficacy of China's macroeconomic policy frameworks, improving governance mechanisms in the face of shocks, and providing robust support for risk prevention strategies pertinent to such events.
蒋晓宇, 李宏瑾, 林楠. 重大突发公共事件下的通胀测量偏差——来自中国的证据[J]. 金融研究, 2023, 521(11): 21-38.
Jiang Xiaoyu, Li Hongjin, Lin Nan. Inflation Measurement Bias under Major Public Emergencies: Evidence from China. Journal of Financial Research, 2023, 521(11): 21-38.
[1]李宏瑾,2023,《从通胀目标制到平均通胀目标制:理论、实践与反思》,《经济评论》第2期,第156～170页。 [2]苏乃芳、李宏瑾和张怀清,2016,《有关GDP平减指数的再认识》,《经济学动态》第5期,第62～73页。 [3]张成思和田涵晖,2023,《货币政策重新寻锚:通货膨胀的概念与度量再研究》,《经济研究》第1期,第73～89页。 [4]张雪春,2010,《我国通货膨胀的城乡差异分析》,《金融研究》第10期,第11～24页。 [5]赵达和沈煌南,2021,《中国CPI感知偏差再评估:新视角、新方法与新证据》,《经济学动态》第5期,第48～63页。 [6]周小川,2020,《拓展通货膨胀的概念与度量》,《中国金融》第24期,第9～11页。 [7]Alexander, D. and E. Karger, 2023, “Do Stay-at-Home Orders Cause People to Stay at Home？ ”, Review of Economics and Statistics, 105 (4), pp.1017～1027. [8]Bartik, A. W., M. Bertrand, Z. B. Cullen, E. L. Glaeser, M. Luca and C.T. Stanton, 2020, “How Are Small Businesses Adjusting to Covid-19？ ”, NBER Working Paper, No.26989. [9]Boskin, M., E. Dulberger, Z. Griliches, R. Gordon and D. Jorgenson, 1996, “Toward a More Accurate Measure of the Cost of Living”, Final Report to the Senate Finance Committee, Advisory Commission to Study the Consumer Price Index, Dec., 4th. [10]Braun, R. A. and S. M. Lein, 2021, “Sources of Bias in Inflation Rates and Implications for Inflation Dynamics”, Journal of Money, Credit and Banking, 53(6), pp.1553～1572. [11]Carvalho, V. M., J. R. Garcia, S. Hansen, Á. Ortiz, T. Rodrigo, M. J. V. Rodríguez and P. Ruiz, 2021, “Tracking the COVID-19 Crisis with High-resolution Transaction Data”, Royal Society Open Science, 8(8), No. 210218. [12]Cavallo, A., 2020, “Inflation with Covid Consumption Baskets”, NBER Working Paper, No.27352. [13]Cavallo A. and O. Kryvtsov, 2023, “What can Stockouts Tell Us about Inflation？ Evidence from Online Micro Data”, Journal of International Economics, 146, No.103769. [14]Cunningham, A., 1996, “Measurement Bias in Price Indices: An Application to the UK's RPI”, Bank of England, No.47. [15]Deaton, A., 1998, “Getting Prices Right: What Should be Done？”, Journal of Economic Perspectives, 12(1), pp.37～46. [16]Diewert, W. E. and K. J. Fox, 2022. “Measuring Real Consumption and CPI Bias under Lockdown Conditions”, Canadian Journal of Economics, 55, pp.480～502. [17]Dunn, A., K. Hood, A. Batch and A. Driessen, 2021, “Measuring Consumer Spending Using Card Transaction Data”, AEA Papers and Proceedings, 111, pp.321～325. [18]Faria-e-Castro, M., 2021, “Fiscal Policy during a Pandemic”, Journal of Economic Dynamics and Control, 125, No.104088. [19]Grigoli, F. and E. Pugacheva, 2022, “COVID-19 Inflation Weights in the UK and Germany”, IMF Working Papers, No. 2022/204. [20]Hausman, J., 2003, “Sources of Bias and Solutions to Bias in the Consumer Price Index”, Journal of Economic Perspectives, 17(1), pp.23～44. [21]Hill, R., 2004, “Inflation Measurement for Central Bankers”, Reserve Bank of Australia, pp.141～159. [22]Jaravel, X. and M. O'Connell, 2020, “Real-time Price Indices: Inflation Spike and Falling Product Variety during the Great Lockdown”, Journal of Public Economics, 191, No.104270. [23]Jordà, Ò., C. Liu, F. Nechio and F. Rivera-Reyes, 2022, “Why is US inflation higher than in other countries？”, FRBSF Economic Letter, 7, pp.1～6. [24]Kurmann, A., E. Lale and L. Ta, 2020, “The Impact of Covid-19 on us Employment and Hours: Realtime Estimates with Homebase Data”, Working Paper. [25]Miles, D., U. Panizza, R. Reis and A. Ubide, 2017, “And Yet It Moves: Inflation and the Great Recession”, Geneva Report, No.19. [26]Mitchell, T., G. O'Donnell, R. Taves, Z. Weselake-George and A. Xu, 2020, “Consumer Expenditures during COVID-19”, Statistics Canada, Prices Analytical Series, No.62F0014M. [27]Redding S. J. and D. E. Weinstein, 2020, “Measuring Aggregate Price Indices with Taste Shocks: Theory and Evidence for CES Preferences”, Quarterly Journal of Economics, 135 (1), pp.503～560. [28]Reinsdorf, M., J. Tebrake, N. O’Hanlon and B. Graf, 2020, “CPI Weights and COVID-19 Household Expenditure Patterns”, IMF Special Series on COVID-19, Aug., 17th. [29]Shapiro, A. H., 2022, “A Simple Framework to Monitor Inflation”, Federal Reserve Bank of San Francisco, Working Paper, No.2020～29.
