Price indices

Prepared by Robert Anderton, Valerie Jarvis, Vincent Labhard, Filippos Petroulakis, Ieva Rubene and Lara Vivian

1 Introduction

• Some of the key effects of digitalisation relevant to monetary policy relate to output and productivity, labour markets, wages and prices.
• The impact of digitalisation on the economy is a function, inter alia, of national economic structure and economic policies, institutions and governance.
• However, it is not clear whether digitalisation is going to deepen differences between countries or reduce them.
• This article mainly summarises and updates the evidence on the euro area and the EU digital economy, including international comparisons.
• [2] This article also takes a closer look at the impact of the coronavirus (COVID-19) pandemic on the digital economy.
• Since the start of the pandemic, both producers and consumers have become more accustomed to and more reliant on digital technologies.

2 The size and growth of the digital economy

• The digital economy is smaller in the euro area and EU than in the United States, and the gap has not changed dramatically in the past few years.
• Most euro area countries have much smaller value added from digital sectors (as a percentage of GDP) than the United States, with the euro area digital economy about two-thirds the size of that of the United States (see Chart 1).
• In the United States, the digital service sector alone contributes as much as the entire digital economy in the euro area.
• [3] For most countries in the euro area, the annual percentage point increase in the share of the digital economy has been less than 0.1, the same as in the United States, leaving the gap more or less unchanged.
• The Digital Economy and Society Index has risen from below 40 in 2015 to above 60 in 2020, as shown in Chart 2.
• [4] These differences in digital adoption across countries imply that the impacts of digitalisation may also differ across the euro area and EU countries.
• Chart 2 Digital adoption in the euro area and EU economies (Digital Economy and Society Index)

3 Productivity and the supply side

Productivity

• The last two decades have seen a protracted slowdown in productivity across advanced economies.
• Productivity growth in the euro area started to slow significantly in the mid-to-late 1990s, well before other advanced economies, but the slowdown eventually became widespread even before the financial crisis.
• As such, ICT capital is complementary to a more complex set of other inputs and synthesising them efficiently can generate higher productivity returns from ICT investment.
• [8] show that UK-based firms owned by US firms are more productive, owing to higher ICT-related productivity.
• The productivity of frontier firms has, in fact, been growing rapidly, but laggard firms have been slow to catch up, suggesting bottlenecks in innovation diffusion (Andrews et al.[11]).
• Schivardi and Schmitz[12] show that countries whose firms had adopted good management practices achieved much faster productivity growth than others in the 1995-2008 period (when ICT-driven productivity growth in the United States took off) than the previous decade.
• [16] There are substantial differences between digital and non-digital companies in their productivity distribution and leadership persistence.
• Using firm-level data for the big four euro area countries, the top panel in Chart 3 shows the relative productivity of digital, compared with non-digital, firms across the distribution.
• Chart 3 Productivity distribution and leadership persistence (Germany, Spain, France and Italy)
• There is evidence that ICTs contribution to productivity growth has declined across advanced economies.
• While the euro area performed substantially worse than its peers in terms of productivity growth in the 1995-2004 period, over the past decade, productivity gains from ICT capital have been muted across North America as well as the euro area.
• [18] Digitalisation, including more recently artificial intelligence and machine learning, is a form of general purpose technology (GPT) with wide-ranging impacts across the economy.
• It is the combined effect of these three qualities that makes GPTs unique and leads to their singular productivity effects.
• Investment in physical capital is lumpy, given adjustment costs, while the production of technology itself becomes more efficient over time.

Supply side

• [24] According to some estimates, between one-third (for the less digital economies) and two-thirds (for the more digital economies) of digital investments are in intangibles.
• The term refers not only to the size and complexity of a dataset, but also to its corresponding analytics.
• [25] As with intangible assets in general, big data can take very different forms and are often highly firm-specific, i.e.
• not particularly valuable outside of the firm (an example of the sunk aspect of intangible assets).
• [26] Big data can be collected through online platforms and service providers and can be processed and analysed to generate revenues in many ways, e.g.
• The value of such data capital is difficult to estimate, but is potentially very large.
• [27] Alternative sources of finance to traditional bank financing appear better suited to the financing of intangible investment, which is hard to collateralise.
• Many digital technologies are also associated with substantial network effects, so early movers have a sizeable advantage and dominate their markets.
• Box 1 Online platforms and the collaborative economy Prepared by Lara Vivian The collaborative or sharing economy relies on digital platforms to coordinate and supervise the matching between the supply and demand sides of the market.
• According to estimates, although the size of online platforms has grown rapidly, their contribution to the economy remains relatively small.
• [40] Although surveys and studies often rely on different definitions of platform employment, other studies confirm similar magnitudes and cross-country heterogeneity.

4 Labour markets

• This section reviews the effects of digitalisation on the labour market.
• [43] From the early 1990s, labour markets in advanced economies started to polarise, whereby the share of low and high-skilled jobs increased at the expense of middle-skilled jobs.
• While employment and wage premiums for high skills rose, there was also a substantial increase in the employment share of low-skilled labour, albeit not always necessarily accompanied by rising wages.
• In this framework, RBTC automates some tasks and creates new ones, destroying some existing jobs and creating new ones in the process.
• [47] The evolution of job polarisation by task content for selected European countries is shown in Chart 4.
• [51] find that declines in average hours worked over recent decades across a selection of EU countries have exacerbated job polarisation.
• [52] Chart 4 Evolution of the task content of the mean job in selected European countries (change in share of tasks)
• A leading example of a modern automation technology with a high potential to displace labour is that of industrial robots.
• Robots are currently primarily used to perform repetitive tasks in manufacturing and hence represent a prominent example of routine task replacement.
• Graetz and Michaels[53] show that robots raise TFP and labour productivity in Europe with no significant effects on employment except for a small shift in favour of high-skilled workers.
• Two EU countries Estonia and Sweden consistently top the digital employment charts.
• [55] Sectors with higher digital intensity made substantial contributions to employment growth across advanced economies during the decade 2006-16 (see Chart A, left-hand panel).
• The trends of those countries at the forefront of the digital transformation may hold lessons for others still in the catch-up phase.
• How can labour markets still generate enough jobs after two centuries of incredible labour-saving technological advances?
• Acemoglu and Restrepo[56] argue that technology has a reinstatement effect, which creates new tasks as it destroys others.
• They argue for a reinterpretation of the relationship between technology and labour as a race between automation and new labour-intensive tasks, which reinstates labour and increases productivity.
• At the same time, these mechanisms may lead to greater inequality in the labour market.
• [57] In addition, the higher market power of large digital firms may also compress wages and be associated with a lower labour share.
• Structural framework conditions, including both labour and product market policies, may need to be further adapted to fully reap the potential gains from digitalisation while maintaining inclusiveness.

5 Digitalisation and consumer price inflation

• Digitalisation is often associated with a negative impact on the price of some goods and services and on overall inflation.
• online retail, effects on inflation as cost savings, higher price transparency, intensified competition, and productivity gains which are generally very difficult to disentangle empirically.
• [63] Finally, it is important to distinguish the impact of digitalisation on price level from the rate of change, i.e.
• inflation, and to examine if there is a bias in measuring inflation using the Harmonised Index of Consumer Prices (HICP) given the increasing importance of online retail for household consumption.

The direct effects

• The direct impact channel of digitalisation on consumer prices functions via the prices of digital products purchased by consumers.
• Because such products are part of the HICP for the euro area and its member countries, this will have a direct impact on inflation as measured by this index.
• Over the same period, the range of impacts for individual euro area countries was around 0.1 to 0.2 percentage points per year on average.
• [65] Chart 5 ICT product contribution to headline HICP annual inflation rate across euro area countries (percentage points)
• First, digital products in the consumer basket do not comprise only the four categories used for the reported index.
• Many other goods and services are also exposed to ICT developments to various degrees.
• Failure to appropriately incorporate the prices of such products in the HICP basket can lead to a bias (upward or downward) in the respective price indices.

The indirect effects

• The indirect impact channels of digitalisation operate via cost savings and higher competition owing to increased price transparency.
• Considering e-commerce between businesses and consumers, the inflation-lowering impact of growing e-commerce occurs in two ways.
• online sales require lower expenditures than maintaining shops), which both traditional and online retailers may pass on to consumers.
• Second, e-commerce may lower prices (or constrain their increase following cost rises) because of higher transparency and intensified competition between suppliers.
• Both effects can take place when the share of e-commerce retail in total retail trade is still low.
• The extent to which the indirect effects described above have an impact on inflation partly depends on the prevalence of e-commerce in the euro area.
• The empirical evidence on the effects of e-commerce penetration on inflation so far is scarce but points to a small negative effect.
• [67] Overall, however, price-lowering impacts from a more intensified use of e-commerce, if there are any, will only last until the diffusion of e-commerce technologies through markets has levelled off.
• [69] Changes in market power and digitalisation in general may have implications not only for inflation but also for the transmission of monetary policy.
• Companies with high market power in general respond less to changes in costs, and hence to monetary policy, than perfectly competitive firms.
• The transmission of monetary policy will depend on how the pricing decisions of firms change as market power changes.
• [75] Overall, the impact of digitalisation on monetary policy needs further research, both to enrich structural models to capture its effects and to verify their empirical implications.

Online retail and measurement of the HICP

• The inclusion of goods and services traded online in the HICP will have an impact on HICP inflation only if the prices of such products and services change at different rates than the prices of goods and services traded offline.
• The methodology for compiling the HICP implies that price-level differences between online and offline shop prices do not have a direct effect on the HICP.
• Moreover, the statistical offices of the euro area countries continuously enhance their data collection methods and some online prices are already reflected in the HICP.
• [77] The available evidence on possible measurement error in the consumer price indices resulting from the incomplete incorporation of online sales is scarce and inconclusive.
• [79] Overall, there is still not enough evidence to conclude that the partial exclusion of online sales leads to measurement error in price indices (upward or downward).

6 Digitalisation and the COVID-19 pandemic

• Since the onset of the pandemic there has been an increase in the take-up of digital technologies, especially in connection with lockdowns restricting physical mobility within and across regions and countries.
• Chart 7 Euro area retail trade July 2020 compared with February 2020 (percentage change)
• This may be an important step towards a larger digital economy in the euro area and EU; whether this represents a permanent change will be a key factor for the likely medium to long-term impact of the COVID-19 pandemic on the digital and broader economy in the euro area, EU and elsewhere.
• As the COVID-19 pandemic is still unfolding, its impact on the digital economy remains uncertain, especially beyond the short term.
• Its impact on the digital and broader economy depends both on digital supply and demand.

7 Conclusion

• The pandemic has generated two main challenges when measuring consumer price inflation.
• First, the pandemic triggered unusually large changes in household spending patterns which are not reflected in aggregated consumer price indices.
• [3] The HICP is compiled using consumption weights that are kept constant within a given calendar year.
• When constructing the HICP, the price changes of individual items are weighted using household consumption shares that are fixed for the calendar year.
• This reflects the intended purpose of the HICP of estimating pure price changes without accounting for shifts in household consumption patterns.
• While keeping the weights constant within a calendar year does not generate measurement issues in normal times, the nature of the pandemic shock has triggered large consumption shifts over a short period of time.
• There is a growing body of literature documenting large pandemic-induced changes in household consumption and discussing their implications for inflation.

• The available data suggest that household consumption patterns have changed significantly during the pandemic.
• Relative consumption patterns were stable until the beginning of the pandemic (see Chart A), but the pandemic and the lockdown measures led to a large increase in the weight of some categories (such as food items and communication services) and a reduction in other categories (such as recreation and energy goods).
• As Chart A reports relative weights, some categories show an increase in March/April because the nominal spending in that category contracted less than overall consumption.
• Table A shows our estimates of the development of nominal household spending across categories.

• Since the beginning of the pandemic, inflation as measured by our experimental index has been running higher than HICP inflation, and the difference has remained broadly stable in recent months.
• Chart B shows the gap between the annual rates of change (year-on-year) of the experimental index and the HICP (the orange line in panel a).
• This gap started to open up in March (as shown by the orange bars in panel b) and increased to about 0.2 percentage points in April.
• [14] Intuitively, this reflects consumers switching from lower-than-average inflation categories (such as fuel for transport, covered by Energy) to higher-than-average inflation categories (such as food items).

• The lockdown period also caused issues for HICP price collection.
• [15] Price collection in bricks-and-mortar stores stopped where outlets were closed.
• In addition, sampling in supermarkets and drugstores was largely discontinued in order to protect price collectors.
• Chart C shows the evolution of price imputation in HICP categories from March to August.

• The dunnhumby Consumer Pulse Survey found that 49% of U.S. consumers surveyed reported that their personal finances were poor, an increase of nearly 20% since July.
• In addition, 68% reported that the economy wasnt doing well and 91% said they are closely watching store prices.
• Survey respondents concerns with higher food prices align with the latest Consumer Price Index for Food , which reported that August food prices were 4.1 percent higher than in 2019.
• In addition, Food-at-home (FaH) prices have increased 3.3 percent and food-away-from-home (FAFH) prices have increased 2.6 percent in the same timeframe.

• Changes in indirect tax rates can have a visible impact on consumer prices.
• This assumes the full and immediate pass-through of changes in indirect taxes to consumer prices and therefore, on balance, tends to overstate the effects of tax changes.
• [1] Based on this measure, the contribution from changes in indirect taxes to euro area HICP inflation has been, on average, 0.2 percentage points, but was much stronger during periods when tax rates increased, such as in 2007 and between 2011 and 2014 (see Chart A).
• Chart A HICP and HICP at constant tax rates (annual percentage changes; percentage point contributions)

• However, in response to the coronavirus (COVID-19) pandemic, several euro area countries have reduced indirect tax rates on a scale not seen before in the euro area.
• In addition to temporary reductions in broad-based value added taxes (VAT) in Germany and Ireland, many other euro area countries have recently introduced targeted reductions in indirect taxes (see Chart B).
• [2] Assuming full and immediate pass-through, Eurostats HICP at constant tax rates implies that the reduction in VAT in Germany would have a downward impact on euro area HICP inflation in July 2020 of around 0.6 percentage points.
• [3] Chart B Impact of changes in indirect taxes on HICP inflation (percentage point contributions based on difference between HICP and HICP at constant tax rates)

• The actual impact of the recent reductions in indirect taxes on inflation is surrounded by considerable uncertainty.
• First, historically there are few examples of cuts in indirect tax rates in euro area countries that could shed light on the likely degree of pass-through.
• Lastly, the lions share of the current reduction in indirect taxes results from the VAT rate cut in Germany, which is only temporary (and very rare in euro area countries), and might thus generate unusual anticipation effects.
• [6],[7] The pass-through of recent reductions in indirect taxes is likely to vary across sectors and to be overall incomplete.
• The reductions in indirect tax rates in euro area countries shape the inflation profile for 2020 and 2021 in the September 2020 ECB staff projections.
• Understanding the impact of indirect taxes on the inflation profile and outlook is relevant for the communication of monetary policy.
• Chart C Impact of changes in indirect taxes on HICPX inflation projections (annual percentage changes; percentage point contributions)