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Despite recent interest in measuring household activities, investment in household RD that is a household intangible asset. This paper takes a step toward valuing household innovation in the U.S. by developing time series of nominal and real investment and capital stocks for household R&D. We find that household investment in R&D was more than 11 percent of R&D funded by the private business sector in 2017 and about half of what businesses spent on R&D to develop new products for consumers. If household R&D were judged to be in scope for GDP, GDP would have been 0.2 percent higher in 2017. We conclude that household R&D is important and warrants closer attention.

This article provides a nontechnical review of the literature and issues related to the measurement of aggregate productivity. I begin with a discussion of productivity measures, their performance in recent decades, and key measurement puzzles that emerge from the data. The remainder of the review focuses on two important questions. First, how do we make more accurate the measures of prices used to deflate nominal output so as to win (or at least not lose) the race for economic measurement to keep up with a changing economy? I frame the issues and point to the most important and promising areas for further research. Second, what does or should GDP measure? I defend GDP as a valuable measure of production and offer suggestions for improving it. At the same time, I emphasize the importance of measuring economic welfare (well-being) and argue that GDP should be supplemented with a satellite account that measures economic welfare.

This paper focuses on the price of nails since 1695 and the proximate source of changes in those prices. Why nails? They are a basic manufactured product whose form and quality have changed relatively little over the last three centuries, yet the process for producing them has changed dramatically. Accordingly, nails provide a useful prism through which to examine a wide range of economic and technological developments that touch on multiple areas of both micro- and macroeconomics. Several conclusions emerge. First, from the late 1700s to the mid-twentieth century, real nail prices fell by a factor of about 10 relative to overall consumer prices. These declines had important effects on downstream industries, most notably construction. Second, while declining materials prices contribute to reductions in nail prices, the largest proximate source of the decline during this period was multifactor productivity growth in nail manufacturing, highlighting the role of the specialization of labor and reorganization of production processes. Third, the share of nails in GDP dropped back from 0.4 percent of GDP in 1810—comparable to today’s share of household purchases of personal computers—to a de minimis share more recently; accordingly, nails played a bigger role in American life in that earlier period. Finally, real nail prices have increased since the mid-twentieth century, reflecting in part an upturn in materials prices and a shift toward specialty nails in the wake of import competition, though the introduction of nail guns partly offset these increases for the price of installed nails.

Infrastructure provides critical support for economic activity, and assessing its role requires reliable measures. This paper provides an overview of U.S. infrastructure data in the National Economic Accounts. After developing definitions of basic, social, and digital infrastructure, we assess trends in each of these categories and their components. Results are mixed depending on the category. Investment in some important types of basic infrastructure has barely or not kept up with depreciation and population growth in recent decades, while some other categories look better. We also show that the average age of most types of infrastructure in the U.S. has been rising, and the remaining service life has been falling. This paper also presents new prototype estimates of state-level investment in highways, highlighting the wide variation across states. In addition, we present new prototype data on maintenance expenditures for highways. In terms of future research, we believe that deprecation rates warrant additional attention given that current estimates are based on 40-year old research and are well below those used in Canada and other countries. We also believe that additional creative work on price indexes for infrastructure would be valuable. Finally, all of the data in this paper will be downloadable on the BEA website, and we hope that the analysis in this paper and the availability of data will spur additional research. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

The Producer Price Index (PPI) for the United States suggests that semiconductor prices have barely been falling in recent years, a dramatic contrast to the rapid declines reported from the mid-1980s to the early 2000s. This slowdown in the rate of decline is puzzling in light of evidence that the performance of microprocessor units (MPUs) has continued to improve at a rapid pace. Over the course of the 2000s, the MPU prices posted by Intel, the dominant producer of MPUs, became much stickier over the chips' life cycle. As a result of this change, we argue that the matched-model methodology used in the PPI for MPUs likely started to be biased after the early 2000s and that hedonic indexes can provide a more accurate measure of price change since then. MPU prices fell rapidly through 2004 on every price measure we present, with the PPI declining at an even quicker pace than the hedonic indexes. However, from 2004 to 2009, our preferred hedonic index fell faster than the PPI, and from 2009 to 2013 the gap widened further, with our preferred index falling at an average annual rate of 42 percent, while the PPI declined at only a 6 percent rate. Given that MPUs currently represent about half of U.S. shipments of semiconductors, this difference has important implications for gauging the rate of innovation in the semiconductor sector.

Two recent papers have made compelling cases that mismeasurement of prices of high-tech products cannot explain the slow pace of labor productivity growth that has prevailed since the mid-2000s. Does that result indicate that mismeasurement of high-tech products has limited implications for patterns of economic growth? The answer in this paper is “no.”. We demonstrate that the understatement of price declines for high-tech products in official measures has a dramatic effect on the pattern of multifactor productivity (MFP) growth across sectors. In particular, we show that correcting this mismeasurement implies faster MFP growth in high-tech sectors and slower MFP advance outside the high-tech sector. If MFP growth is taken as a rough proxy for the pace of innovation, our results suggest that innovation in the tech sector has been more rapid than the rate that would be inferred from official statistics (and less rapid outside high-tech). These results deepen the productivity puzzle. If the pace of innovation in high-tech sectors has been more rapid than indicated by official statistics, then it is perhaps even more puzzling that overall labor productivity growth has been so sluggish in recent years.

This chapter addresses two measurement issues for mobile phones. First, we develop a new mobile phone price index using hedonic quality-adjusted prices for smartphones and a matched-model index for feature phones. Our index falls at an average annual rate of 17% during 2010–18, close to the rate of decline in the price index used in the GDP accounts. Given relatively flat average prices over this period, our index points to substantial quality improvement. Second, we propose a methodology to disentangle purchases of phones and wireless services when they are bundled together as part of a long-term service contract. Getting the allocation right is especially important for real personal consumption expenditure (PCE) because the price deflators for phones and wireless services exhibit very different trends. Our adjusted estimates suggest that real PCE spending currently captured in the category Cellular Phone Services increased 4 percentage points faster than is reflected in published data.

Household R&D (or household innovation) is an important source of innovation that has to date been largely overlooked in research related to national accounts. Indeed, it is not currently counted as investment in the literatures on household production and human capital. This paper develops time series estimates of nominal investment, real investment, and real capital stocks for household R&D for product innovations in the United States. (We focus on product innovations because survey data on services innovations in the household sector are not yet available.) In the U.S., we find that household product R&D is significant. Our estimate of real investment in 2017 is $41 billion (2012 dollars). This is about half of what producers spend in R&D to develop new products for consumers – a sizable fraction. Our estimate of the real capital stock of household product R&D in 2017 is $233 billion. We conclude that household R&D is an important feature of household activity and, more generally, of the overall landscape of innovation.

This paper addresses two measurement issues for mobile phones. First, we develop a new mobile phone price index using hedonic quality-adjusted prices for smartphones and a matched-model index for feature phones. Our index falls at an average annual rate of 17 percent during 2010-2018, close to the rate of decline in the price index used in the GDP Accounts. Given relatively flat average prices over this period, our index points to substantial quality improvement. Second, we propose a methodology to disentangle purchases of phones and wireless services when they are bundled together as part of a long-term service contract. Getting the allocation right is especially important for real PCE because the price deflators for phones and wireless services exhibit very different trends. Our adjusted estimates suggest that real PCE spending currently captured in the category Cellular Phone Services increased 4 percentage points faster than is reflected in published data.

Gestation lags have long been understood to be an important feature of the investment process. However, previous research has focused on the time-to-build part of the gestation period and has provided little information on the earlier time-to-plan period during which key decisions are made about the project's scope and financing. We develop new estimates of time-to-plan lags for commercial construction projects in the United States, using a large project-level dataset that allows direct measurement of planning lags. We find that these time-to-plan lags are long, averaging about 16 months when we aggregate the projects without regard to size and about 26 months when we weight the projects by their construction cost. The full distribution of time-to-plan lags is very wide, and we relate this variation to the characteristics of the project and its location. In addition, we show that time-to-plan lags lengthened by 3 to 4 months, on average, over our sample period (1999 to 2010). Regulatory factors are associated with the variation in planning lags across locations, and we present anecdotal evidence that links at least some of the lengthening over time to heightened regulatory scrutiny.

Cloud computing—computing done on an off-site network of resources accessed through the Internet—is revolutionizing how computing services are used. However, because cloud is so new and it largely is an intermediate input to other industries, it is difficult to track in the U.S. statistical system. Moreover, there is a paucity of systematic information on the prices of cloud services. To begin filling this gap, this paper does three things. First, we define the different segments of cloud computing and document its explosive expansion. Second, we develop new hedonic prices indexes for cloud services based on quarterly data for compute, database, and storage services offered by Amazon Web Services (AWS) from 2009 to 2016. These indexes fall rapidly over the sample period, with quickening (and double digit) rates of decline for all three products starting at the beginning of 2014. Finally, we highlight the puzzle of why investment in IT equipment in the NIPAs has been so weak while capital expenditures have exploded for IT equipment associated with cloud infrastructure. We suggest that cloud service providers are undertaking large amounts of own-account investment in IT equipment and that some of this investment may not be captured in GDP.

This note considers a puzzle: why has information technology (IT) equipment investment in the National income and Product Accounts (NIPAs) been so weak since 2007 at the same time that financial reports indicate massive increases in capital expenditures by IT service companies?

We use a large project-level dataset to estimate the length of the planning period for commercial construction projects in the United States. We find that these time-to-plan lags are long, averaging about 17 months when we aggregate the projects without regard to size and more than 28 months when we weight the projects by their construction cost. The full distribution of time-to-plan lags is very wide, and we relate this variation to the characteristics of the project and its location. In addition, we show that time-to-plan lags lengthened by 3 to 4 months, on average, over our sample period (1999 to 2010). Regulatory factors help explain the variation in planning lags across locations, and we present anecdotal evidence that links at least some of the lengthening over time to heightened regulatory scrutiny.

Published macroeconomic data traditionally exclude most intangible investment from measured GDP. This situation is beginning to change, but our estimates suggest that as much as $800 billion is still excluded from U.S. published data (as of 2003), and that this leads to the exclusion of more than $3 trillion of business intangible capital stock. To assess the importance of this omission, we add intangible capital to the standard sources-of-growth framework used by the BLS, and find that the inclusion of our list of intangible assets makes a significant difference in the observed patterns of U.S. economic growth. The rate of change of output per worker increases more rapidly when intangibles are counted as capital, and capital deepening becomes the unambiguously dominant source of growth in labor productivity. The role of multifactor productivity is correspondingly diminished, and labor's income share is found to have decreased significantly over the last 50 years.

This paper analyzes the sources of U.S. productivity growth in recent years using both aggregate and industry-level data. We confirm the central role for information technology (IT) in the productivity revival during 1995–2000 and show that IT played a significant, though smaller, role after 2000. Productivity growth after 2000 appears to have been boosted by industry restructuring and cost cutting in response to profit pressures, an unlikely source of future strength. In addition, the incorporation of intangible capital into the growth accounting framework takes some of the luster off the performance of labor productivity since 2000 and makes the gain during 1995–2000 look larger than in the official data. Finally, we examine the outlook for trend growth in labor productivity; our estimate, though subject to much uncertainty, is centered at 2 - 1 4 % a year, faster than the lackluster pace that prevailed before 1995 but somewhat slower than the 1995–2006 average.

The composition of the U.S. population will change significantly in coming decades as the decline in fertility rates following the baby boom, coupled with increasing longevity, leads to an older population. This demographic shift will likely have a dramatic effect on the long-run prospects for living standards. Moreover, as has been widely discussed in the media and by policymakers, population aging also has significant implications for social programs for the elderly, such as Social Security and Medicare. In this paper, we discuss the consequences of population aging from a macroeconomic perspective and consider alternative paths the economy could follow in response to population aging. The choices society makes among those alternatives will be closely linked to decisions about how to reform entitlement programs for the elderly. The fundamental conclusion of our study is that, barring a significant increase in labor force participation, population aging will lead to a reduction in per capita consumption relative to a baseline in which the demographic composition of the population does not change. The size of any consumption reduction depends critically on whether the adjustment happens sooner or later and on whether the labor force participation of the elderly changes. Important policy questions, then, are whose consumption path falls, by how much, when, and by what means? Decisions about Social Security and Medicare reform are integrally bound up with these fundamental policy questions.

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.

Business outlays on intangible assets are usually expensed in economic and financial accounts. Following Hulten (1979), this paper develops an intertemporal framework for measuring capital in which consumer utility maximization governs the expenditures that are current consumption versus those that are capital investment. This framework suggests that any business outlay that is intended to increase future rather than current consumption should be treated as capital investment. Applying this principle to newly developed estimates of business spending on intangibles, we find that, by about the mid-1990s, business investment in intangible capital was as large as business investment in traditional, tangible capital. Relative to official measures, our framework portrays the U.S. economy as having had higher gross private saving and, under plausible assumptions, fractionally higher average annual rates of change in real output and labor productivity from 1995 to 2002.

This paper provides new estimates of depreciation rates for personal computers using an extensive database of prices of used PCs. Our results show that PCs lose roughly half their remaining value, on average, with each additional year of use. We decompose that decline into age-related depreciation and a revaluation effect, where the latter effect is driven by the steep ongoing drop in the constant-quality prices of newly-introduced PCs. Our results are directly applicable for measuring the depreciation of PCs in the National Income and Product Accounts (NIPAs) and were incorporated into the December 2003 comprehensive NIPA revision. Regarding tax policy, our estimates suggest that the current tax depreciation schedule for PCs closely tracks the actual loss of value in a zero-inflation environment. However, because the tax code is not indexed for inflation, the tax allowances would be too small in present value for inflation rates above the very low level now prevailing.

Productivity growth in the U.S. economy jumped during the second half of the 1990s, a resurgence that many analysts linked to developments in information technology (IT). However, shortly after this consensus emerged, demand for IT products fell sharply, leading to a debate about the connection between IT and productivity and about the sustainability of the faster growth. ; This article contributes to this debate in two ways. First, the authors provide updated estimates of the proximate sources of growth using a growth accounting framework that focuses on information technology. Their results confirm that the acceleration in labor productivity after 1995 was driven by the greater use of IT capital goods and the more rapid efficiency gains in the production of these goods. Second, to assess whether the pickup in productivity growth is sustainable, the authors analyze the steady-state properties of a multisector growth model. This exercise generates a range for labor productivity growth of 2 percent to 2 3/4 percent per year, which suggests that much-and possibly all-of the resurgence is sustainable. ; The analysis also highlights that future increases in output will depend on the pace of technological advance in the semiconductor industry and on the extent to which products embodying these advances diffuse through the economy.

The productivity slowdown of the early 1970s continues to puzzle economists. A frequent explanation of this puzzle is that mismeasurement of output has worsened enough to help account for the apparent shortfall of output growth. Griliches (1994) highlighted one channel through which this worsening measurement could occur. He raised the possibility that—because output growth in the service sector likely is undermeasured—the rising share of services has led to greater undermeasurement of overall economic growth. This paper demonstrates that this argument is of little quantitative significance. Even under assumptions most favorable to the hypothesis, the rising share of services has had only a small impact on measurement error. These results—along with evidence from Baily and Gordon (1988)—make mismeasurement of output an improbable explanation for the productivity slowdown in aggregate data. © 1997 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology

Using a representative longitudinal survey of U.S. households, we find that household income became noticeably more volatile between the early 1970s and the late 2000s despite the moderation seen in aggregate economic activity during this period. We estimate that the standard deviation of percent changes in household income rose about 30 percent between 1971 and 2008. This widening in the distribution of percent changes was concentrated in the tails. The share of households experiencing a 50 percent plunge in income over a two-year period climbed from about 7 percent in the early 1970s to more than 12 percent in the early 2000s before retreating to 10 percent in the run-up to the Great Recession. Households’ labor earnings and transfer payments have both become more volatile over time. As best we can tell, the rise in the volatility of men’s earnings appears to owe both to greater volatility in earnings per hour and in hours worked.

This paper distinguishes two types of asymmetry in business cycles: deepness and steepness. Deepness is defined as the characteristic that troughs are further below trend than peaks are above. Most previous research has focused exclusively on steepness, which refers to cycles in which contractions are steeper than expansions. A test for deepness is proposed and applied to U.S. post-war quarterly unemployment, real GNP, and industrial production. Evidence of deepness is found for unemployment and industrial production, while the evidence for real GNP is weaker. Previous evidence of steepness in unemployment is confirmed.

This article models the conditional mean and variance of real gross national product (GNP) and its components using asymmetric exponential generalized autoregressive conditional hetero-scedasticity, a model previously applied only to financial variables. The results imply that the variance of real GNP is higher following negative innovations than positive innovations and that this asymmetry arises in the cyclically sensitive sectors. Further evidence links this asymmetry to the phase of the business cycle: The conditional variance appears to be largest around business-cycle troughs. In addition, shocks to the conditional variance of GNP and its components typically persist for long periods. The evidence of asymmetry in conditional variance is robust to a variety of alternative specifications.

Using data from the PSID, we find that household income has become noticeably more volatile during the past thirty years. We estimate that the standard deviation of percent changes in household income rose one-fourth between the early 1970s and early 2000s. This widening in the distribution of percent changes is concentrated in the tails of the distribution, and especially in the lower tail: Changes between the 25th and 75th percentiles are almost the same size now as thirty years ago, but changes at the 10th percentile look substantially more negative. The boost in volatility occurred throughout the 1970s, 1980s, and 1990s, albeit not at a steady pace. Households' labor earnings and transfer payments have both become more volatile over time.

Published macroeconomic data traditionally exclude most intangible investment from measured GDP. This situation is beginning to change, but our estimates suggest that as much as $800 billion is still excluded from U.S. published data (as of 2003), and that this leads to the exclusion of more than $3 trillion of business intangible capital stock. To assess the importance of this omission, we add intangible capital to the standard sources-of-growth framework used by the BLS, and find that the inclusion of our list of intangible assets makes a significant difference in the observed patterns of U.S. economic growth. The rate of change of output per worker increases more rapidly when intangibles are counted as capital, and capital deepening becomes the unambiguously dominant source of growth in labor productivity. The role of multifactor productivity is correspondingly diminished, and labor's income share is found to have decreased significantly over the last 50 years.

Published macroeconomic data traditionally exclude most intangible investment from measured GDP. This situation is beginning to change, but our estimates suggest that as much as $800 billion is still excluded from U.S. published data (as of 2003), and that this leads to the exclusion of more than $3 trillion of business intangible capital stock. To assess the importance of this omission, we add intangible capital to the standard sources-of-growth framework used by the BLS, and find that the inclusion of our list of intangible assets makes a significant difference in the observed patterns of U.S. economic growth. The rate of change of output per worker increases more rapidly when intangibles are counted as capital, and capital deepening becomes the unambiguously dominant source of growth in labor productivity. The role of multifactor productivity is correspondingly diminished, and labor's income share is found to have decreased significantly over the last 50 years.

Aggressive deregulation of the household debt market in the early 1980s triggered innovations that greatly reduced the required home equity of U.S. households, allowing them to cash-out a large part of accumulated equity. In 1982, home equity equaled 71 percent of GDP; so this generated a borrowing shock of huge macroeconomic proportions. The combination of increasing household debt from 43 to 56 percent of GDP with high interest rates during the 1982-1990 period is consistent with such a shock to households’ demand for funds. This paper uses a quantitative general equilibrium model of lending from the wealthy to the middle class to evaluate the positive and normative aspects of the transition to a high debt economy. Using the model, we interpret evidence on the changing distribution of assets and debt as well as macro time series since 1982.

The stabilization of economic activity in the mid 1980s has received considerable attention. Research has focused primarily on the role played by milder economic shocks, improved inventory management, and better monetary policy. This paper explores another potential explanation: financial innovation. Examples of such innovation include developments in lending practices and loan markets that have enhanced the ability of households and firms to borrow and changes in government policy such as the demise of Regulation Q. We employ a variety of simple empirical techniques to identify links between the observed moderation in economic activity and the influence of financial innovation on consumer spending, housing investment, and business fixed investment. Our results suggest that financial innovation should be added to the list of likely contributors to the mid-1980s stabilization.

As the accelerated technological advances of the past two decades continue to reshape the United States' economy, intangible assets and high-technology investments are taking larger roles. These developments have raised a number of concerns, such as: how do we measure intangible assets? Are we accurately appraising newer, high-technology capital? The answers to these questions have broad implications for the assessment of the economy's growth over the long term, for the pace of technological advancement in the economy, and for estimates of the nation's wealth. In Measuring Capital in the New Economy, Carol Corrado, John Haltiwanger, Daniel Sichel, and a host of distinguished collaborators offer new approaches for measuring capital in an economy that is increasingly dominated by high-technology capital and intangible assets. As the contributors show, high-tech capital and intangible assets affect the economy in ways that are notoriously difficult to appraise. In this detailed and thorough analysis of the problem and its solutions, the contributors study the nature of these relationships and provide guidance as to what factors should be included in calculations of different types of capital for economists, policymakers, and the financial and accounting communities alike.

This paper provides new estimates of depreciation rates for personal computers (PCs) using an extensive database on prices of used PCs. Our results show that PCs lose roughly half their remaining value, on average, with each additional year of use. We decompose that decline into age-related depreciation and a revaluation effect driven by the steep ongoing drop in the constant-quality prices of newly introduced PCs. Our results are directly applicable for measuring the depreciation of PCs in the National Income and Product Accounts (NIPAs) and were incorporated into the December 2003 comprehensive NIPA revision. Regarding tax policy, our estimates suggest that the current tax depreciation schedule for PCs closely tracks their actual loss of value in a zero-inflation environment. However, because the tax code is not indexed for inflation, the tax allowances would be too small in present value for inflation rates above the very low level now prevailing.

Business outlays on intangible assets are usually expensed in economic and financial accounts. Following Hulten (1979), this paper develops an intertemporal framework for measuring capital in which consumer utility maximization governs the expenditures that are current consumption versus those that are capital investment. This framework suggests that any business outlay that is intended to increase future rather than current consumption should be treated as capital investment. Applying this principle to newly developed estimates of business spending on intangibles, we find that, by about the mid-1990s, business investment in intangible capital was as large as business investment in traditional, tangible capital. Relative to official measures, our framework portrays the U.S. economy as having had higher gross private saving and, under plausible assumptions, fractionally higher average annual rates of change in real output and labor productivity from 1995 to 2002.

The performance of the U.S. economy over the past several years has been remarkable, including a rebound in labor productivity growth after nearly a quarter century of sluggish gains. To assess the role of information technology in the recent rebound, this paper re-examines the growth contribution of computers and related inputs with the same neoclassical framework that we have used in earlier work. Our results indicate that the contribution to productivity growth from the use of information technology -- including computer hardware, software, and communication equipment -- surged in the second half of the 1990s. In addition, technological advance in the production of computers appears to have contributed importantly to the speed-up in productivity growth. All in all, we estimate that the use of information technology and the production of computers accounted for about two-thirds of the 1 percentage point step-up in productivity growth between the first and second halves of the decade. Thus, to answer the question posed in the title of this paper, information technology largely is the story.

In the United States, as in other countries, the share of employment in service-producing industries has increased over time. While the implications of this sectoral shift for issues such as productivity and wages has received attention, there has been little work considering the implications of this shift for economic stability. This paper takes a step toward filling the gap.