Price Indexes - General

The internationally agreed guidelines for national economic accounts, System of National Accounts 2008 (hereafter referred to as SNA 2008) (United Nations Statistics Division 2008), explicitly recommend that illegal market activity should be included in the measured economy. This recommendation has not yet been implemented by the U.S. Bureau of Economic Analysis (BEA) because of challenges inherent in identifying suitable source data and differences in conceptual traditions. This paper explores how tracking illegal activity in the U.S. national economic accounts might impact nominal Gross Domestic Product (GDP), real GDP, productivity, and other economic statistics. Nominal GDP rises in 2017 by more than 1 percent when illegal activity is tracked in the U.S. National Income and Product Accounts (NIPAs). By category, illegal drugs add $108 billion to measured nominal GDP in 2017, illegal prostitution adds $10 billion, illegal gambling adds $4 billion, and theft from businesses adds $109 billion. Real GDP and productivity growth also change. Real illegal output grew faster than overall GDP during the 1970s and post–2008. As a result, tracking illegal activity ameliorates both the 1970s economic slowdown and the post–2008 economic slowdown considerably.

This paper provides new benchmark estimates of industry-level price differentials between Japan and the U.S. for 2011 based on a bilateral price accounting model anchored to the Japan-US input-output tables. We apply the model to translate available demand-side data on purchaser’s price PPPs for final uses (e.g. the Eurostat-OECD PPPs) and intermediate uses (e.g. the METI survey) to unmeasured producer’s price PPPs for industry output. These PPPs allow us to produce price level indexes at the industry level, which we use to assess price competitiveness between Japan and the U.S. Under the nominal exchange rate of 110.6 yen per dollar as of the beginning of July 2018, we estimate that producers in Japan have a pricing advantage in 66 of 106 industries in the manufacturing sector, and in 24 of 50 industries in the service sector. We conclude that price competitiveness of Japanese service industries has considerably improved in the more recent time period. However, Japanese producers have a significant price disadvantage in comparison to their U.S. counterparts in electricity and gas supply, and most of the agricultural producing industries.

Accurate price indexes are crucial for preparing accurate estimates of real gross domestic product and corresponding productivity measures. The price index must capture price change for a ‘relevant’ market basket goods, while at the same time controlling for changes in characteristics and/or quality of these goods. Traditional price indexes (i.e. ‘matched model’) are well suited to capturing price change for goods that exhibit little or no quality change over time, however, for products whose characteristics and/or quality are changing rapidly (e.g. ICT goods), hedonic methods may be more suitable.

This paper provides a brief history of hedonic methods employed by U.S. statistical agencies and specifically examines the role of hedonic price indexes in the U.S. National Income and Product Accounts. It also attempts to dispel some popular misconceptions about hedonic methods.

More information on Prices and Output for ICT

Updated Hedonic Inventory: A listing of NIPA prices that reflect hedonic techniques - August 2016

The Bureau of Labor Statistics (BLS) prepares the Consumer Price Index for All Urban Consumers (CPI-U), and the Bureau of Economic Analysis prepares the Personal Consumption Expenditures (PCE) chain-type price index. Both indexes measure the prices paid by consumers for goods and services. Because the two indexes are based on different underlying concepts, they are constructed differently, and tend to behave differently over time. From the first quarter of 2002 through the second quarter of 2007, the CPI-U increased 0.4 percentage point per year faster than the PCE price index. This paper details and quantifies the differences in growth rates between the CPI-U and the PCE price index; it provides a quarterly reconciliation of growth rates for the 2002:Q1-2007:Q2 time period.

There are several factors that explain the differences in growth rates between the CPI and the PCE price index. First, the indexes are based on difference index-number formulas. The CPI-U is based on a Laspeyres index; the PCE price index is based on a Fisher-Ideal index. Second, the relative weights assigned to the detailed item prices in each index are different because they are based on different data sources. The weights used in the CPIU are based on a household survey, while the weights used in the PCE price index are based on business surveys. Third, there are scope differences between the two indexes—that is, there are items in the CPI-U that are out-of-scope of the PCE price index, and there are items in the PCE price index that are out-of-scope of the CPI-U. And finally, there are differences in the seasonal-adjustment routines and in the detailed price indexes used to construct the two indexes.

Over the 2002:Q1-2007:Q2 time period, this analysis finds that almost half of the 0.4 percentage point difference in growth rates between the CPI-U and the PCE price index was explained by differences in index-number formulas. After adjusting for formula differences, differences in relative weights—primarily “rent of shelter”—more than accounted for the remaining difference in growth rates. Net scope differences, in contrast, partly offset the effect of relative weight differences.
 

Foreign trade enables a nation to consume a different mix of goods and services than it produces, so to measure real gross domestic income (GDI) for an open economy, we must deflate by an index of the prices of the things that this income is used to buy, not the price index for GDP. The differences between these two indexes come from the export and import components of the GDP, and are measured by the trading gains index. Fisher indexes are a natural way to estimate the conceptual economic indexes of trading gains and real GDI because they are averages of the theoretical upper and lower bounds of the economic indexes. They can be decomposed in a way that permits analyses of the factors driving changes in trading gains, such as changes in the terms of trade and in the relative price of tradables, or changes in the prices of particular commodities. Applying these methods to the United States, we find that trading gains have a median absolute effect on US real GDI of 0.2 percentage points in annual data. The petroleum price shocks that occurred in late 1973 and in 1980 subtracted more than a full percentage point from the annual growth of real GDI, and in the first half of 2008 price increases in petroleum and other imported commodities subtracted 2 percentage points from the annual rate of growth of real GDI, making it negative despite the steady growth of real GDP. On the other hand, with petroleum prices excluded, US terms of trade begin to improve steadily starting in 1995 and the relative price of tradables falls. These effects increase the growth rate of US real GDI by 0.15 percent per year on average.