Measuring consumption: the post-1973 slowdown and the research issues - commentary

Consumption (Economics) ; Economic indicators

Measuring consumption growth: the impact of new and better products

This study describes how the U.S. government measures real consumption growth and how it tries to take account of a complicating factor: that the goods and services offered to consumers change over time; new products are introduced and old products are improved. The 1996 Boskin Commission critique of this government methodology is described, along with the changes made in response to that critique. Also described is recent research related to how real consumption growth should be measured in the presence of new and better products.Consumption (Economics)

Beyond GDP? Welfare Across Countries and Time

We propose a simple summary statistic for a nation’s flow of welfare, measured as a consumption equivalent, and compute its level and growth rate for a broad set of countries. This welfare metric combines data on consumption, leisure, inequality, and mortality. Although it is highly correlated with per capita GDP, deviations are often economically significant: Western Europe looks considerably closer to U.S. living standards, emerging Asia has not caught up as much, and many African and Latin American countries are farther behind due to lower levels of life expectancy and higher levels of inequality. In recent decades, rising life expectancy boosts annual growth in welfare by more than a full percentage point throughout much of the world. The notable exception is sub- Saharan Africa, where life expectancy actually declines.Welfare, life expectancy, living standards

Valuing Consumer Products by the Time Spent Using Them: An Application to the Internet

For some goods, the main cost of buying the product is not the price but rather the time it takes to use them. Only about 0.2% of consumer spending in the U.S., for example, went for Internet access in 2004 yet time use data indicates that people spend around 10% of their entire leisure time going online. For such goods, estimating price elasticities with expenditure data can be difficult, and, therefore, estimated welfare gains highly uncertain. We show that for time-intensive goods like the Internet, a simple model in which both expenditure and time contribute to consumption can be used to estimate the consumer gains from a good using just the data on time use and the opportunity cost of people's time (i.e., the wage). The theory predicts that higher wage internet subscribers should spend less time online (for non-work reasons) and the degree to which that is true identifies the elasticity of demand. Based on expenditure and time use data and our elasticity estimate, we calculate that consumer surplus from the Internet may be around 2% of full-income, or several thousand dollars per user. This is an order of magnitude larger than what one obtains from a back-of-the-envelope calculation using data from expenditures.

State-Dependent or Time-Dependent Pricing: Does it Matter for Recent U.S. Inflation?

Inflation equals the product of two terms: an extensive margin (the fraction of items with price changes) and an intensive margin (the average size of those price changes). The variance of inflation over time can be decomposed into contributions from each margin. The extensive margin figures importantly in many state-dependent pricing models, whereas the intensive margin is the sole source of inflation changes in staggered time-dependent pricing models. We use micro data collected by the U.S. Bureau of Labor Statistics to decompose the variance of consumer price inflation from 1988 through 2003. We find that around 95% of the variance of monthly inflation stems from fluctuations in the average size of price changes, i.e., the intensive margin. When we calibrate a prominent state-dependent pricing model to match this empirical variance decomposition, the model's shock responses are very close to those in time-dependent pricing models.

Evidence on Learning and Network Externalities in the Diffusion of Home Computers

In this paper we examine the importance of local spillovers such as network externalities and learning from others in the diffusion of home computers using data on 110,000 U.S. households in 1997. Controlling for many individual characteristics, we find that people are more likely to buy their first home computer in areas where a high fraction of households already own computers or when a large share of their friends and family own computers. Further results suggest that these patterns are unlikely to be explained by city-specific unobserved traits. Looked at in more detail, the spillovers appear to come from experienced and intensive computer users. They are not associated with the use of any particular type of software but do seem to be highly tied to the use of e-mail and the Internet, consistent with computers being part of a local information and communications network.

Some Evidence on the Importance of Sticky Prices

We examine the frequency of price changes for 350 categories of goods and services covering about 70% of consumer spending, based on unpublished data from the BLS for 1995 to 1997. Compared with previous studies we find much more frequent price changes, with half of prices lasting less than 4.3 months. The frequency of price changes differs dramatically across categories. We exploit this variation to ask how inflation for 'flexible-price goods' (goods with frequent changes in individual prices) differs from inflation for 'sticky-price goods' (those displaying infrequent price changes). Compared to the predictions of popular sticky price models, actual inflation rates are far more volatile and transient, particularly for sticky-price goods. The data appendix for this paper can be found at http://www.nber.org/data-appendix/w9069/

Quantifying Quality Growth

We introduce an instrumental variables approach to estimate the importance of unmeasured quality growth for a set of 66 durable consumer goods. Our instrument is based on predicting which of these 66 goods will display rapid quality growth. Using pooled cross- relatively sections of households in the 1980 through 1996 U.S. Consumer Expenditure Surveys, we estimate quality Engel curves' for 66 durable consumer goods based on the extent richer households pay more for a good, conditional on purchasing. We use the slopes of these curves to predict the rate of quality-upgrading. Just as if households are ascending these quality Engel curves over time, we find that the average price paid rises faster for goods with steeper quality slopes. BLS prices likewise increase more quickly for goods with steeper quality slopes, suggesting the BLS does not fully net out the impact of quality-upgrading on prices paid. We estimate that quality growth averages about 3.7% per year for our goods, with about 60% of this, or 2.2% per year, showing up as higher inflation rather than higher real growth.

Sticky information and sticky prices

In the U.S. and Europe, prices change somewhere between every six months and once a year. Yet nominal macro shocks seem to have real effects lasting well beyond a year. "Sticky information" models, as posited by Sims (2003), Woodford (2003), and Mankiw and Reis (2002), can reconcile micro flexibility with macro rigidity. We simulate a sticky information model in which price setters do not update their information on macro shocks as often as they update their information on micro shocks. Compared to a standard menu cost model, price changes in this model reflect older macro shocks. We then examine price changes in the micro data underlying the U.S. CPI. These price changes do not reflect older information, thereby exhibiting a similar response to that of the standard menu cost model. However, the empirical test hinges on staggered information updating across firms; it cannot distinguish between a full information model and a model where firms have equally old information.Prices