one. for goods that have ownership rates that are relatively equal across deciles, regardless of the level of the ownership rate, the probability distributions are fairly flat with values for pi close to 0. 1. for goods that are more concentrated among the affluent households, the probability distributions tend to rise across the income deciles. - 5 * * * footnotes 1 arthur b. kennickell and r. louise woodburn, “ consistent weight design for the 1989, 1992 and 1995 scfs, and the distribution of wealth, ” manuscript, august 1997. 2 arthur b. kennickell and annika e. sunden, “ pensions, social security, and the distribution of wealth, ” finance and economics discussion series, 1997 - 55, board of governors of the federal reserve system, november 1997. 3 martha starr - mcclure, “ stock market wealth and consumer spending, ” finance and economics discussion series, 1998 - 20, board of governors of the federal reserve system, april 1998. 4 these results were originally reported in report on the american workforce, u. s. department of labor, 1995 and will appear in david s. johnson and stephanie shipp, “ inequality and the business cycle : a consumption viewpoint, ” forthcoming, journal of empirical economics. david s. johnson of the u. s. bureau of labor statistics provided the updated data shown in table 1. 5 david s. johnson and timothy m. smeeding, “ measuring trends in inequality and individuals and families : income and consumption, ” mimeo., march 1998.

context of weaker - than - expected demand for their products, and hence weaker cash flow, businesses may decide that they prefer to hang onto their existing computer hardware, software, and communications equipment for longer than they otherwise might. accordingly, it might presently be the case that high - tech equipment is not as short - lived as we think it usually is. adding further to firms'willingness to hold on to their existing equipment through the current period is the apparent absence of new “ killer apps ”, break - through technological advances in applications such as e - mail and web browsers that in the past have spurred investment in software and in complementary goods like computers and communications equipment. a third reason for caution in assuming that the overhang will be worked off quickly is that even if hightech depreciation rates remain elevated, new investment could continue feeding into the capital stock because of planning lags, lengthy building times, or costly irreversibilities. for example, a national optical network can take years to roll out – longer than the average time to build a nonresidential structure. if demand falls below expectations, a firm might decide to persevere to completion of an investment project with a lengthy building time, but may be left with a capital good that is ill - suited to the conditions that prevail at the time of completion. lastly, the situation of the high - tech industries is also complicated by some considerations that are difficult to capture in the normal calculations of investment and depreciation. one such factor is the changing market structure of the high - tech sector. in the mid - 1990s, deregulation in the telecom industry and the rise of the internet led to a surge of entrants into the high - tech areas. these startups not only generated a good deal of spending on capital goods themselves, they also forced incumbent firms to invest and innovate to keep up, even as profits were eroding. survivor mentalities may have caused the competing firms to push capital outlays further than they might have otherwise, on the notion that the short - term losses recorded in stretching to expand market share could be made up later as competitors fell by the wayside. the creation of the long - haul fiber optic network, the part of the network that runs from city to city, provides perhaps the best illustration of the excesses that can develop as firms rush to establish their market positions. the creation of these networks may also indicate the complexities involved in determining the