Data from the Federal Reserve Board's Survey of Consumer Finances show a striking pattern of growth in family income and net worth between 1998 and 2001. Inflation-adjusted incomes of families rose broadly, although growth was fastest among the group of families whose income was higher than the median. The median value of family net worth grew faster than that of income, but as with income, the growth rates of net worth were fastest for groups above the median. The years between 1998 and 2001 also saw a rise in the proportion of families that own corporate equities either directly or indirectly (such as through mutual funds or retirement accounts); by 2001 the proportion exceeded 50 percent. The growth in the value of equity holdings helped push up financial assets as a share of total family assets despite a decline in the overall stock market that began in the second half of 2000. The level of debt carried by families rose over the period, but the expansion in equities and the increased values of principal residences and other assets were sufficient to reduce debt as a proportion of family assets. The typical share of family income devoted to debt repayment also fell over the period. For some groups, however--particularly those with relatively low levels of income and wealth--a concurrent rise in the frequency of late debt payments indicated that their ability to service their debts had deteriorated.

In this paper we develop a vintage model to gain a better understanding of the semiconductor industry and its role in recent U.S. productivity gains. Unlike previous work, in our model the observed price declines of individual chips are driven by the introduction of better vintages rather than by learning economies. Dominated chips, nonetheless, continue to be produced, for a time, due to sunk investments in chip-specific production equipment. The model lends partial support to Jorgenson's hypothesis that an exogenous increase in Moore's Law could have generated the more rapid price declines, and faster productivity growth, seen after 1995.

A key economic indicator is real output. To get this right, we need to measure accurately both the value of nominal GDP (done by Bureau of Economic Analaysis) and key price indexes (done mostly by Bureau of Labor Statisticcs). All of us have worked on these measurements while at the BLS and the BEA. In this article, we explore some of the thorny statistical and conceptual issues related to measuring a dynamic economy. An often-stated concern is that the national economic accounts miss some of the value of some goods and services arising from the growing digital economy. We agree that measurement problems related to quality changes and new goods have likely caused growth of real output and productivity to be understated. Nevertheless, these measurement issues are far from new, and, based on the magnitude and timing of recent changes, we conclude that it is unlikely that they can account for the pattern of slower growth in recent years. First we discuss how the Bureau of Labor Statistics currently adjusts price indexes to reduce the bias from quality changes and the introduction of new goods, along with some alternative methods that have been proposed. We then present estimates of the extent of remaining bias in real GDP growth that stem from potential biases in growth of consumption and investment. And we take a look at potential biases that could result from challenges in measuring nominal GDP, including those involving the digital economy. Finally, we review ongoing work at BLS and BEA to reduce potential biases and further improve measurement.

Researchers have disagreed about factors driving up health care spending since the 1980s. One camp, led by Kenneth Thorpe, identifies rising numbers of people being treated for chronic diseases as a major factor. Charles Roehrig and David Rousseau reach the opposite conclusion: that three-quarters of growth in average spending reflects the rising costs of treating given diseases. We reexamined sources of spending growth using data from four nationally representative surveys. We found that rising costs of treatment accounted for 70 percent of growth in real average health care spending from 1980 to 2006. The contribution of shares of the population treated for given diseases increased in 1997-2006, but even then it accounted for only one-third of spending growth. We highlight the fact that Thorpe's inclusion of population growth as part of disease prevalence explains the appreciable difference in results. An important policy implication is that programs to better manage chronic diseases may only modestly reduce average spending growth.

The utilization of health care services has undergone several important shifts in recent years that have implications for the cost of medical care. We empirically document the presence of these shifts for a broad list of medical conditions and assess the implications for price indexes. Following the earlier literature, we compare the growth of two price measures: one that tracks expenditures for the services actually provided to treat conditions and another that holds the mix of those services fixed over time. Using retrospective claims data for a sample of commercially-insured patients, we find that, on

Price deflators for semiconductors fell rapidly over the 1990s, pulled down by steep declines in the deflator for the microprocessor (MPU) segment that accelerated around 1995. A decomposition of a price index for Intel's MPUs suggests that virtually all of the declines in the price index—and the acceleration—can be attributed to quality increases associated with product innovation, rather than declines in the cost per chip. The sizable decline in Intel's margins from 1993–99 only accounted for about 6 percentage points of the average 24% decline per quarter in the price index and cannot explain the acceleration. (JEL D42, L63, O47)

Hedonic techniques were developed to control for quality differences across goods and over time in order to construct constant-quality aggregate price measures. When the available data are a panel of high-frequency data on models whose characteristics are constant over time, matched-model price indexes can also be used to obtain constantquality price measures. We show this by demonstrating that, given data of this type, certain matched-model indexes yield price measures that are numerically close to those obtained using hedonic techniques.

This paper examines three questions motivated by previous research on semiconductors and productivity growth: Why did semiconductor prices fall so rapidly in the second half of the 1990s, why has the rate of price decline slowed since 2001, and to what extent are these price swings associated with changes in the rate of advance in semiconductor technology? We show that the price swings are statistically significant and that they reflect changes in both price-cost markups and cost trends. Further analysis indicates that the shift to faster cost declines in the mid-1990s likely corresponded to a speed-up in the pace of advance in semiconductor technology; however, the slower cost declines since 2001 appear not to have been mirrored by a deceleration in technology. Consequently, researchers should be cautious about associating price or cost movements for semiconductors with changes in the pace of underlying technology even over moderately long periods. *Aizcorbe is affiliated with the Bureau of Economic Analysis; Oliner and Sichel are affiliated with the Federal Reserve Board.