Predict your next investment

COMPUTER HARDWARE & SERVICES | IT Services / Data Storage & Security
microdata.com

See what CB Insights has to offer

Founded Year

1992

Stage

Acquired | Acquired

About MicroData

MicroData provides outsourced, private cloud, hybrid-cloud and compliant IT security solutions to small and mid-sized businesses. It is based in Beverly, Massachusetts.

MicroData Headquarter Location

100 Cummings Center Suite 146N

Beverly, Massachusetts, 01915,

United States

Latest MicroData News

The role of demand and supply factors in HICP inflation during the COVID-19 pandemic – a disaggregated perspective

Feb 8, 2021

02/08/2021 | 10:33am EST Message : Introduction The economic impact of the coronavirus (COVID-19) pandemic is not a standard textbook shock. Instead the shock is multidimensional, with sources on both the external and the domestic side, on both the demand and the supply side, and at both the aggregate and the sector-specific level. This poses challenges for the assessment of inflation. Established relationships between inflation and its determinants may not hold up or may not be scalable, given the magnitude of disturbances in product and labour markets. Moreover, the increasing emphasis of the inflation literature on distributions rather than point outcomes for future inflation is relevant for analysing the impact that the COVID-19 shock has had on inflation risks. Understanding the drivers of inflation during the pandemic is helped by adopting a more granular perspective than usual. A disaggregated approach is often used by central banks to complement assessments based on headline inflation. Typically, such an approach is used to distil underlying (common) trends in inflation or to improve forecast accuracy. To understand the drivers of inflation, the ECB's analysis regularly looks at the main components of inflation, such as energy, food, non-energy industrial goods (NEIG) and services. By moving to a higher level of granularity than usual (i.e. the 12 sub-components of the Harmonised Index of Consumer Prices, HICP), the analysis in this article helps to better understand the diverse impact of the pandemic across components and ultimately to enhance our understanding of the current drivers of headline inflation. The role of supply-side effects in particular is likely to be larger than usual for a number of inflation components. The nature of the lockdowns and containment measures imposed after the outbreak of the pandemic implied a shutdown of business and/or an increase in costs for some sectors. Price changes associated with such supply-side effects may, in the first instance, change relative price developments and not necessarily aggregate inflation. It is common in regular inflation analysis to assess short-term supply disturbances in energy and food prices due to the often large magnitude of these types of shocks. What is distinctive about the pandemic, however, is the larger than usual role of supply effects on core inflation that stem from the lockdowns. A disaggregated approach extended to core components can also shed light on the consequences of the demand shock associated with COVID-19-related income losses or uncertainty. Given the magnitude of the shock, there can be implications for both aggregate price levels and, depending on income and substitution elasticities, relative prices. This article illustrates how a more disaggregated perspective can help to gauge the implications of COVID-19, augmenting the regular inflation analysis. Section 2 first describes the evolution of aggregate inflation during the COVID-19 period and explains the motivation for the level of granularity adopted in the analysis. Section 3 then examines the drivers of the inflation response, component by component, mainly focusing on the role of domestic factors that are unique to the pandemic. The section also examines the role of demand and supply effects and includes a component-level decomposition of inflation into the structural shock contributions of such effects. Section 4 provides some concluding messages. 2 How has HICP inflation adjusted so far? The main components of HICP inflation responded heterogeneously to the pandemic shock. Headline inflation declined from 1.2% in February to 0.1% in May, before dropping into negative territory in August (Chart 1). However, at the level of main components, the response was uneven in terms of both speed and magnitude. The initial steep decline in headline inflation was mainly due to a fall in the contribution of energy inflation from 0.0 to -1.2 percentage points between February and May. The declining contribution of energy can be clearly ascribed to a commodity (oil) related external supply price shock. During the same period, however, the contribution of food inflation increased, mainly owing to the unprocessed food component. The rising contribution of food inflation cannot easily be ascribed to a particular type of shock, as it is likely that there were upward effects from food commodity prices owing to the H1N1 swine flu and higher costs in international and domestic supply chains, but also higher demand as households were forced to shift expenditure from restaurants and canteens to food for home consumption during lockdown. From the middle of the year onwards, headline inflation fell further as HICP inflation excluding energy and food (HICPX) also increasingly contributed to the disinflationary tendencies, mainly owing to a decline in services inflation and, to a lesser extent, a decline in NEIG inflation. Chart 1 Decomposition of HICP inflation Until the third quarter of 2020 the response of HICPX inflation was broadly in line with historical regularities, pointing to a clear role for downward demand effects. The response of HICPX inflation during the pandemic was modest relative to the decline in activity. Such short-term persistence can reflect a range of factors, including menu costs, pre-existing supply contracts or a higher priority assigned to maintaining good relationships with business clients. In this respect, HICPX evolved broadly in line with a Phillips curve-based forecast conditioned on developments in standard activity and slack indicators. Assuming that the recessionary impact of the pandemic became fully pervasive in the second quarter, the response of HICPX was broadly in line with expectations (Chart 2). This response in line with slack indicators suggests that weaker (net) demand is likely to have played an important role, but does not preclude the possibility that the multidimensional COVID-19 shock was also characterised by larger than usual supply effects. Indeed, a more structural analysis of the drivers of aggregate headline inflation points to a role for domestic supply effects in the recent dynamics of headline inflation (see Box 1). The remainder of this article examines the adjustment in HICPX inflation during the pandemic in terms of its short-term persistence and its main drivers. A component-by-component approach based on a higher level of disaggregation for HICPX inflation is used, which can also be related more easily to other sector-specific effects, including measurement issues relating to price imputations. Chart 2 3 What explains the adjustment of HICP inflation so far? 3.1 Overview of factors unique to the pandemic A diverse mix of domestic and global pandemic-related factors have influenced recent inflation dynamics (Figure 1). These factors are of both direct and indirect relevance for inflation, but have in common that they are unparalleled in scale. This holds for the sharp decline in domestic demand, especially in the consumer-facing sectors most exposed to the impact of social distancing. It also holds for the large-scale responses from both monetary and fiscal authorities to the consequences of the pandemic. Given the global nature of the pandemic, the confluence of domestic and external factors has also been unusually strong, including, on the external side, the impact of much weaker global demand, lower prices for oil and other non-food commodities, and, from the third quarter of 2020, the appreciation of the euro effective exchange rate. Figure 1 Factors that affected the response of inflation to the pandemic shock Fiscal and regulatory factors have directly influenced inflation dynamics. The pandemic has triggered fiscal and regulatory responses with a direct, albeit temporary, impact on inflation. In response to the pandemic, several euro area countries have reduced indirect tax rates on a scale not seen before. Taking into account their net effect on a mechanical basis, the impact on HICPX inflation is estimated to be around -0.7 percentage points in the second half of 2020. This compares with an average contribution of 0.2 percentage points since 2004. Regulatory changes have also influenced recent inflation dynamics. For example, the sales season for clothing and footwear in some euro area countries, including Italy and France, was postponed from July to August and extended into September. This added to the volatility of annual inflation rates, making it more challenging to gauge underlying price trends. In assessing the impact of such developments, and pricing behaviour more generally, the availability of timely micro price data has proved helpful (see Box 2). Pandemic-related factors with an impact on prices beyond the near term have also emerged. The pandemic has had a profound impact on consumer behaviour. Demand for travel and tourism is depressed and seems likely to remain so until there has been a widespread roll-out of effective vaccines. This not only dampened inflation at the aggregate euro area level, but also led to increased heterogeneity in inflation developments across euro area countries, given the important role of tourism in some of them. Moreover, some prices that are typically resilient in crises have also weakened. One example is rents, for which the annual growth rate declined from 1.4% in February 2020 to 1.2% in October 2020. The downward pressure on rents could stem from the indexation of rents to past inflation. However, it could also reflect the introduction of rent freezes in certain cities in response to the pandemic. The pandemic may have also provided some support to price developments in other areas. For example, the prevalence of remote working arrangements has seen an increase in the share of expenditure on personal IT equipment. Other goods for which demand has been boosted include gardening equipment and bicycles. The lockdowns are unique to the pandemic, especially in terms of the magnitude of the supply effects they have generated. The lockdowns triggered by the pandemic led to severe disruptions to labour supply and production supply chains, particularly during April and May and, to a somewhat lesser extent, in November and December. As noted above, recent evidence on the impact of the initial lockdowns suggests that the associated supply effects have exerted upward pressure on inflation to some extent. Lockdowns also presented price collection difficulties for statisticians. The remainder of this article contains an empirical analysis of the impact of the lockdowns. Box 2 Prepared by Lukas Henkel, Alberto Lentini and Federico Rodari Microdata on prices complement inflation analysis based on official price indices by providing additional information on the behaviour of individual prices. While official price indices allow price levels and inflation rates of narrowly defined product groups to be tracked, these do not allow the tracking of individual prices. Microdata on prices allow additional aspects of price movements to be analysed, e.g. whether price changes become less or more common over time. Microdata on prices are available from three different sources: web-scraped information collected from online stores, shop scanner data and household scanner data. The latter two are collected by, for example, market research companies. , This box provides an example of the use of web-scraped information. Web-scraped data provide highly granular price information in a timely fashion. The data are collected directly from websites of online retailers, making it possible to monitor price movements in quasi-real time. In addition to tracking individual prices over time, these data provide additional information on prices and products offered. For example, online retailers often include information on whether a product price is currently discounted, thereby allowing, for example, the behaviour of discounts during the COVID-19 pandemic to be analysed. An analysis of web-scraped supermarket data provided by PriceStats shows that during the first wave of the virus both the number of distinct products available online and the share of products offered at a discount decreased. Panel a of Chart A shows that the number of products available online started to decrease at the beginning of March 2020 and, for most online supermarkets in the sample, had not recovered by the end of April. While the number of products available online decreased in all supermarkets in the sample, it did so to different extents, with the largest drop being observed in Germany, where the number of products available in early April was less than 60% of the number available in January 2020. Panel b of Chart A shows that temporary price discounts also became less common during the first wave of the virus, compared to the same period in the previous year. For example, in the Italian supermarket in our sample, the share of products at temporarily reduced prices was nearly 40% lower in mid-April 2020 than a year earlier. This decrease in discounts could be one factor that contributed to the temporary surge in food prices observed in the spring of 2020. Chart A Number of distinct products available online by country and annual percentage change in the share of products offered at a discount Microdata on prices will be further analysed within the Price-setting Microdata Analysis Network ( PRISMA ), which was set up by the European System of Central Banks to deepen the understanding of price-setting behaviour and inflation dynamics in the EU. 3.2 Lockdown-induced inflation persistence While there is some evidence of postponements of price reviews, it is likely that the impact on inflation persistence was at most modest and temporary. During the initial phase of lockdowns, many firms were closed. Subsequently, during the containment phases, social distancing meant that some firms (e.g. in the tourism and travel sectors) continued to face difficulties in enticing customers. Indeed, reducing prices appears to have generated little or no rebound in demand. Such extraordinary conditions could have resulted in an unanticipated change in pricing behaviour, i.e. the response of the profit margins of firms was fundamentally different to before. Partly to avoid incurring additional menu costs, firms may have also preferred to delay changing prices until the degree of uncertainty surrounding their business outlook eased. The ECB's Corporate Telephone Survey, for example, indicates that price reviews were pushed down the list of priorities, with postponements not uncommon among firms (see also Box 3). However, other studies based on different data sources point to a quicker reaction in the pricing behaviour of firms, suggesting that the overall impact on frequency of price changes is not clear cut. Price imputations are also likely to have imparted some short-lived increase in inflation persistence. Price collection by statisticians faced severe challenges during the lockdown. For example, price collection could not take place in stores that were closed. In addition, sampling in supermarkets and drugstores was largely discontinued in order to protect price collectors. The recreation sector was heavily affected by price imputations, owing to the non-availability of package holidays and the cancellation of entertainment events. Thus, several prices needed to be imputed, sometimes based on the patterns of previous years. This was especially the case for items that typically exhibit relatively low persistence (Chart 3). For example, the share of imputation for air fares jumped in April and remained elevated for some euro area countries until the autumn. The high level of imputations is likely to mean that these published price indices did not fully capture the impact of the severe downturn, but instead generally reflected developments in past data from more normal times. As a result, the overall persistence of inflation during the pandemic may have appeared higher than it actually was for certain components of core inflation, particularly for the second quarter of 2020. Chart 3 3.3 Lockdown-induced supply effects Different approaches can be used to assess whether adverse supply effects may have played a role in the response of some components of inflation to the pandemic. All such approaches are subject to caveats and, in the context of the complex nature of the COVID-19 crisis, should be seen as contributing to an approximation of what is going on rather than as conclusive pieces of evidence. One approach used to shed light on the role of demand and supply effects is based on unconditional out-of-sample forecasting exercises. The forecasting errors for the prices and quantities of components for the second and third quarters of 2020 are compared with their respective average historical forecasting errors. A larger than usual positive forecasting error for prices accompanied by a larger than usual negative forecasting error for quantities or vice versa would tentatively point to a more dominant than usual role of supply shocks. This assumes the broad characterisation of a supply shock in the economic literature as a movement of prices and quantities in opposite directions. On that basis, there is some evidence of supply effects in the second quarter of 2020, mainly for food and non-durable goods (Chart 4). At the same time, for some other components where demand fell more than expected (e.g. semi-durables), supply effects cannot be ruled out, as high imputation shares could mask underlying upward movements in inflation. In the third quarter of 2020, any supply effects that existed tended to ease. Chart 4 Relative forecasting errors for inflation components A more clear-cut distinction between demand and supply effects ideally relies on a structural identification. Hence, another approach to disentangling demand and supply effects uses conventional VAR models, each containing seven variables: volumes and prices per HICPX component, real GDP, real GDP relative to world real GDP, oil prices, HICP and the short-term interest rate. Five structural drivers are identified: global demand, domestic demand, domestic supply, oil supply and monetary policy. The identification relies on a mix of zero and sign restrictions as informed by theory. The model is estimated using Bayesian techniques. The historical decompositions of the first three quarters of 2020 point to a pervasive and dominant downward impact of both domestic and global demand effects (Chart 5). Furthermore, the decompositions point to a more limited role for adverse supply shocks having an upward impact on inflation even if these were unusually large compared with the typical size of previous supply shocks. These upward supply effects mainly related to certain non-energy industrial goods and miscellaneous services in the second quarter of 2020. Overall, although the two approaches individually come with important caveats and are intended to provide a first-pass assessment, both tend to point to some role for supply effects in explaining the behaviour of inflation during the pandemic, but indicate that demand effects were the dominant factor. Chart 5 Historical decompositions based on a structural VAR for inflation components for the first to the third quarters of 2020 4 Conclusions Summing up, a disaggregated perspective can help to better comprehend the response of inflation to the multi-dimensional COVID-19 shock. A disaggregated approach, which goes beyond just analysing the main components of inflation, is particularly suited to current circumstances where past empirical regularities in the interpretation of recent aggregate and core inflation may not apply. By taking a disaggregated approach, the analysis in this article points to a dominant role for downward domestic and global demand effects. This was only partly offset by upward supply effects, which were strongest in the second quarter of 2020 and were more prevalent in goods than in services. Increased use of price imputations, such as for travel-related services, may also help to explain the response of inflation, although these effects appear to have eased, which may partly explain why the decline in inflation gained further momentum during the second half of 2020. It is likely that a more granular than usual perspective will continue to be needed to assess the evolution of the pandemic and its implications for the drivers of inflation. For monetary policy it is important to identify and look beyond any supply-side effects in order to gain a clearer picture of the disinflationary demand effects that inevitably come with income losses and uncertainty. Moreover, recent research also raised the possibility that supply effects could morph into larger negative demand effects. Given the clear policy relevance of such a scenario, further consideration of this mechanism in the context of the euro area, which partly depends on the degree of its inter-sectoral linkages, would be useful. Finally, although generally weaker, pre-pandemic, non-commodity-related supply effects (e.g. technology shocks) are an ever-present factor in price dynamics. In this regard, the more granular analysis of inflation drivers presented here can also continue to be useful after the pandemic. Attachments

Predict your next investment

The CB Insights tech market intelligence platform analyzes millions of data points on venture capital, startups, patents , partnerships and news mentions to help you see tomorrow's opportunities, today.

MicroData Web Traffic

Rank
Page Views per User (PVPU)
Page Views per Million (PVPM)
Reach per Million (RPM)
CBI Logo

MicroData Rank

CB Insights uses Cookies

CBI websites generally use certain cookies to enable better interactions with our sites and services. Use of these cookies, which may be stored on your device, permits us to improve and customize your experience. You can read more about your cookie choices at our privacy policy here. By continuing to use this site you are consenting to these choices.