Board of Governors of the Federal Reserve System

July 15, 2020

The Increasing Deflationary Impact of Consumer Digital Access Services

David M. Byrne and Carol A. Corrado^*

1. Introduction

Although U.S. consumer price statistics are prone to upward bias, they remain useful if imperfect accounting has a known and stable effect (Boskin et al., 1998; Moulton, 2018). For example, monetary authorities may set a target for measured inflation higher than their objective for true inflation if official measures are routinely overstated (Bernanke and Mishkin, 1997). Similarly, fiscal authorities may treat reported inflation as a biased measure of the true cost of living when indexing program benefits (Burdick and Fisher, 2007). But, in recent years, an undetected increasing source of upward bias has crept into consumer price measures, confounding their use as an indicator: digital access services.

Figure 1. Digital Access Services

Digital access services deliver data, voice, and video to households over the internet, mobile cellular networks, and cable television networks. They have become a major component of U.S. household consumption (figure 1). In light of cost-saving technical advances in information technology (IT), the modest price decline for these services since the late 1980s is puzzling. We construct a price index with alternative sources and methods and find that prices for consumer digital access services have fallen rapidly, and at an accelerating pace, for 30 years. We argue that to get prices right, it is essential to account for the massive increase in data and programming delivered to users in conjunction with largely stable nominal service fees.

2. Price Index Construction

We construct prices for four types of digital access service by dividing spending by a suitable measure of volume. This approach is predicated on the assumption that each service is homogeneous enough that a simple price per unit can capture secular price trends.¹ We discuss alternative approaches in section 4.

For digital access service spending, we use the consumption recorded in the following lines of Bureau of Economic Analysis (BEA) table 2.5.4U. Personal Consumption Expenditures by Type of Product: "Internet access" (line 285); "Cellular telephone services" (line 281); "Cable, satellite, and other live television services" (line 215); and "Video and audio streaming and rental" (line 219).² In choosing volume indicators for each service, we distinguish between commodities and services delivering differentiated products (table 1).

Table 1. Measures of Volume for Digital Access Services

• Internet access service operates in a fashion similar to the postal service. Much the same as the letter carrier is not responsible for the information contained in a letter, the internet service provider delivers packets containing content, but is not the creator of the World Wide Web.³ Thus, the natural measure of volume is units of data delivered, namely fixed consumer internet protocol (IP) traffic.⁴
• Mobile phone service consists of two quite different services. Smartphone service is essentially internet service delivered under different conditions of sale, i.e. via radio transmission, and we employ consumer mobile IP traffic as our indicator of volume. Feature phone service provides the ability to talk, text, and access rudimentary data services. We use talk time as an indicator of overall feature phone use.⁵
• Cable television service providers deliver a differentiated product in that they take responsibility for the content delivered. Consequently simple viewing hours is not an adequate indicator of volume. We adjust hours of viewing for quality using the log of the national average number of channels per provider. That is, increasing the number of available channels from 10 to 20 yields the same increase in quality as an increase from 100 to 200 channels.⁶
• Streaming service is highly differentiated as well. Similar to the index for cable service, we adjust viewing hours by a measure of programming availability. In this case, rather than channels, we use (the log of) the number of programs available in the service library.⁷ Sufficient information on revenue, users, and program library to construct a full history was only obtained for Netflix, but calculations with sporadic data on Hulu and Amazon Prime streaming suggest that Netflix is suitable proxy. We did not obtain data on audio streaming.

3. Results

Inflation for consumer digital access services from 1988 to 2018 is shown in table 2. Internet access service prices fell 36 percent per year on average and slowed substantially in the last ten year period. Prices for mobile access services fell 20 percent per year on average, somewhat slower than the price for internet services, but accelerated as the composition shifted toward smartphone service, for which prices fall faster, in the most recent period.⁸ Cable access service prices behave quite differently, edging up a little under 1 percent per year over time. In contrast, streaming services, able to leverage innovations in IT capital more effectively than cable (e.g. by using cloud computing services), fall 23 percent per year on average. Combining the four service indexes, our aggregate price index for consumer digital access services fell 12 percent per year from 1988 to 2018. The index accelerates noticeably over time as the composition shifts away from cable services.

Table 2. Consumer Digital Access Services

Official prices for both internet and mobile access service also move down over time (table 2b), but at a pace an order of magnitude slower than the alternative indexes. Cable access service prices move up somewhat faster than the alternative index, and the official price index for streaming services is essentially flat. Official inflation for access services overall, our aggregate of the indexes used in the national income and product accounts, was 1 percent per year on average in this period, 13 percentage points higher than the alternative index. The gap between the official and alternative inflation measures has increased over time, from a difference of 6 percentage points in the 1988 to 1997 period, to 13 percentage points in the following ten-year period, and to 19 percentage points from 2008 to 2018.

4. Discussion

The alternative index for digital services, constructed at an annual frequency and focused on the characteristic most relevant for each of four types of service, is best interpreted as an indicator of secular price trends. As such, the index has implications for low-frequency changes in aggregate prices. Combining our alternative measure of digital service prices with official prices for the remainder of the index basket, total PCE prices increase ¼ percentage point more slowly on average than the NIPA PCE deflator over the 30-year period beginning in 1987 (table 2). The alternative index rises at an average rate of 1.9 percent while the official index rises at rate of 2.1 percent.

Figure 2. PCE Inflation

Evidently, the choice between these two indexes for digital access services is consequential. Additional sources provide insight into the puzzling tension between the alternative and official indexes.

• Capital theory predicts inflation for capital services will roughly align with investment price growth for the related capital assets (Jorgenson, 1963). Prices for the information technology used to produce digital access services fell 13 percent per year on average from 1986 to 2015 (Byrne and Corrado, 2017a,b), quite similar to the alternative index for digital access services.
• Hedonic analysis, employed in the construction of the offical index, provides a more nuanced approach to accounting for quality change than the "unit value" approach employed in the alternative index.⁹ For example, studies have found that the type of channels provided by cable networks affects pricing, not just the number of channels (Astine, 2001; Corrado and Ukaneva (2016, 2019)). Price-determining characteristics omitted from the unit value calculation may contribute to the difference between the indexes. Results of internal BLS hedonic studies are not routinely provided to the public, but available research on hedonic prices for internet access service points to modest declines in recent years, akin to the official index (Williams (2008), Greentein and McDevitt (2011), Flamm and Herrera (2017)).
• We speculate that flat item price trajectories may be confounding the official indexes. Casual observation of the pricing practices of major firms in these markets reveals that the component item prices used in the calculation of the index—i.e. prices for specific service plans—are typically unchanged over time.¹⁰ This phenomenon presents a challenge for the matched-model index approach used for the official indexes.¹¹ Any change in service quality (per dollar) will only be captured when an item exits the basket and is replaced by another, the point at which hedonic adjustment is employed.¹² In the event, the premium or discount paid for the replacement item relative to the exiting item must be apportioned between a difference in quality and pure inflation. If item turnover is infrequent, this approach may not introduce sufficient quality adjustment to produce an accurate index.

Leaving aside technical issues and data quality, ultimately the tension between the approaches stems from differing views on the appropriate units for consumption. The official indexes employ monthly prices paid for services of varying descriptions, implicitly assuming that the unit of consumption is the connection itself. In contrast, the alternative index assumes that each megabyte of data and each program watched represents additional consumption, much the same as using more kilowatts of electricity represents more consumption. The operative question is whether, for example, once you pay for access to the internet for a month, your consumption is the same whether you go online or not. Or, from a production perspective, does the output of internet service providers increase if they transmit more data?

In theory, with agreement on the question of units, the two approaches converge. An index using the alternative approach for fine categories distinguishing all price determining characteristics is conceptually the same as a hedonic index using the price per unit of volume rather than the price per contract. In practice, data availability precludes a test of this proposition. Conversely, if the four coarse service categories employed in this analysis are sufficient to account for quality, constructing the alternative index using price per contract, rather than price per unit of volume, should yield a similar result to the official index. As shown in figure 3, this is in fact the case.

Figure 3. Consumer Digital Access Service Price Indexes

5. Conclusion

Accounting for the volume of data, voice and programming consumed using digital access services yields a price index that has fallen rapidly and at an accelerating pace for 30 years. The markedly different price trajectory for this noteworthy component of the consumption basket amplifies the slowdown in consumer prices in recent years, a fact unremarked upon in previous literature.

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