Because firms invest heavily in R&D, software, brands, and other intangible assets—at a rate close to that of tangible assets—changes in measured GDP, which does not include all intangible investments, understate the actual changes in total output. If changes in the labor input are more precisely measured, then it is possible to observe little change in measured total factor productivity (TFP) coincidentally with large changes in hours and investment. This mismeasurement leaves business cycle modelers with large and unexplained labor wedges accounting for most of the fluctuations in aggregate data. To address this issue, I incorporate intangible investments into a multi-sector general equilibrium model and parameterize income and cost shares using data from an updated U.S. input and output table, with intangible investments reassigned from intermediate to final uses. I employ maximum likelihood methods and quarterly observations on sectoral gross outputs for the United States over the period 1985–2014 to estimate processes for latent sectoral TFPs—that have common and sector-specific components. Aggregate hours are not used to estimate TFPs, but the model predicts changes in hours that compare well with the actual hours series and account for roughly two-thirds of its standard deviation. I find that sector-specific shocks and industry linkages play an important role in accounting for fluctuations and comovements in aggregate and industry-level U.S. data, and I find that the model’s common component of TFP is not correlated at business cycle frequencies with the standard measures of aggregate TFP used in the macroeconomic literature.

Manufacturing plants have a clear life cycle: they are born small, grow substantially as they age, and eventually die. Economists have long thought that this life cycle is driven by the accumulation of plant-specific knowledge, here called organization capital. Theory suggests that where plants are in the life cycle determines the size of the payments, or dividends, plant owners receive from organization capital. These payments are compensation for the interest cost to plant owners of waiting for their plants to grow. We build a quantitative growth model of the life cycle of plants and use it, along with U.S. data, to infer the overall size of these payments. They turn out to be quite large—more than one-third the size of the payments plant owners receive from physical capital, net of new investment, and more than 40% of payments from all forms of intangible capital.

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Starting in the early 1990s, countries in southern Europe experienced low productivity growth alongside declining real interest rates. We use data for manufacturing firms in Spain between 1999 and 2012 to document a significant increase in the dispersion of the return to capital across firms, a stable dispersion of the return to labor, and a significant increase in productivity losses from capital misallocation over time. We develop a model with size-dependent financial frictions that is consistent with important aspects of firms’ behavior in production and balance sheet data. We illustrate how the decline in the real interest rate, often attributed to the euro convergence process, leads to a significant decline in sectoral total factor productivity as capital inflows are misallocated toward firms that have higher net worth but are not necessarily more productive. We show that similar trends in dispersion and productivity losses are observed in Italy and Portugal but not in Germany, France, and Norway.

The U.S. Bureau of Economic Analysis (BEA) estimates the return on investments of foreign subsidiaries of U.S. multinational companies over the period 1982–2006 averaged 9.4 percent annually after taxes; U.S. subsidiaries of foreign multinationals averaged only 3.2 percent. Two factors distort BEA returns: technology capital and plant-specific intangible capital. Technology capital is accumulated know-how from intangible investments in R&D, brands, and organizations that can be used in foreign and domestic locations. Used abroad, it generates profits for foreign subsidiaries with no foreign direct investment (FDI). Plant-specific intangible capital in foreign subsidiaries is expensed abroad, lowering current profits on FDI and increasing future profits. We develop a multicountry general equilibrium model with an essential role for FDI and apply the BEA’s methodology to construct economic statistics for the model economy. We estimate that mismeasurement of intangible investments accounts for over 60 percent of the difference in BEA returns.

Over the period 1982–2006, the U.S. Bureau of Economic Analysis (BEA) estimates the return on investments of foreign subsidiaries of U.S. multinational companies averaged 9.4 percent per year after taxes while U.S. subsidiaries of foreign multinationals earned on average only 3.2 percent. We estimate the importance of two factors that distort BEA returns: technology capital and plant-specific intangible capital. Technology capital is accumulated know-how from intangible investments in R&D, brands, and organizations that can be used in foreign and domestic locations. Technology capital used abroad generates profits for foreign subsidiaries with no foreign direct investment. Plant-specific intangible capital in foreign subsidiaries is expensed abroad, lowering current profits on foreign direct investment (FDI) and increasing future profits. We develop a multicountry general equilibrium model with an essential role for FDI and apply the same methodology as the BEA to construct economic statistics for the model economy. We estimate that mismeasurement of intangible investments accounts for over 60 percent of the difference in BEA returns.

This paper was published with no issue number.

Appendix A provides firm-level and industry-level evidence that is consistent with several key features of our model, including the predictions that rates of return increase with a firm’s intangible investments and foreign affiliate rates of return increase with age and with their parents’ R&D intensity. Appendix B provides details for the computation of our model’s equilibrium paths, the construction of model national and international accounts, and the sensitivity of our main findings to alternative parameterizations of the model. We demonstrate that the main finding of our paper—namely, that the mismeasurement of capital accounts for roughly 60 percent of the gap in FDI returns—is robust to alternative choices of income shares, depreciation rates, and tax rates, assuming the same procedure is followed in setting exogenous parameters governing the model’s current account. Appendix C demonstrates that adding technology capital and locations to an otherwise standard two-country general equilibrium model has a large impact on the predicted behavior of labor productivity and net exports.

This paper characterizes the behavior of investment expenditures, optimal capital stocks, and real interest rates in the time-to-build model of investment. These results are used to show that the delivery lag model of investment fails to account for time lags in investment when constructing the cost of capital variable and hence, misspecifies the effects of interest rates on investment expenditures. Second, this paper derives equilibrium pricing relationships involving the prices of existing capital and uses these relationships to obtain simple tests of the underlying investment technology. Despite the widespread use of 'q' in the empirical investment literature, it is shown that the relationship between current investment and an appropriately defined measure of Tobin's 'q' contains no such testable implications. Finally, it is shown that the practice of using stock market data to measure the price of existing capital is invalid when time lags exist in the investment process.