Technical appendix for Working Paper 619, https://doi.org/10.21034/wp.619

Prior to the mid-1980s, labor productivity growth was a useful barometer of the U.S. economy’s performance: it was low when the economy was depressed and high when it was booming. Since then, labor productivity has become significantly less procyclical. In the recent downturn of 2008–2009, labor productivity actually rose as GDP plummeted. These facts have motivated the development of new business cycle theories because the conventional view is that they are inconsistent with existing business cycle theory. In this paper, we analyze recent events with existing theory and find that the labor productivity puzzle is much less of a puzzle than previously thought. In light of these findings, we argue that policy agendas arising from new untested theories should be disregarded.

It is widely believed that an important factor underlying the rapid growth in China is increased foreign direct investment (FDI) and the transfer of foreign technology capital, which is accumulated know-how from investment in research and development (R&D), brands, and organizations that is not specific to a plant. In this paper, we study two channels through which FDI can contribute to upgrading of the stock of technology capital: knowledge spillovers and appropriation. Knowledge spillovers lead to new ideas that do not directly compete or devalue the foreign affiliate's stock. Appropriation, on the other hand, implies a redistribution of property rights over patents and trademarks; the gain to domestic companies comes at a loss to the multinational company (MNC). In this paper we build these sources of technology capital transfer into the framework developed by McGrattan and Prescott (2009, 2010) and introduce an endogenously-chosen intensity margin for operating technology capital in order to capture the trade-offs MNCs face when expanding their markets internationally. We first demonstrate that abstracting from technology capital transfers results in predicted bilateral FDI inflows to China that are grossly at odds with the data. We then use the bilateral inflows to parameterize the model with technology capital transfers and compute the global economic impact of Chinese policies that encouraged greater inflows of FDI and technology capital transfers. Microevidence on automobile patents is used to support our parameter choices and main findings.

Empirical studies quantifying the benefits of increased foreign direct investment (FDI) have been unable to provide conclusive evidence of a positive impact on the host country’s economic performance. I show that the lack of robust evidence is not inconsistent with theory, even if the gains to FDI openness are large. Anticipated welfare gains to increased inward FDI should lead to immediate declines in domestic investment and employment and eventual increases. Furthermore, since part of FDI is intangible investment that is expensed from company profits, gross domestic product (GDP) and gross national product (GNP) should decline during periods of abnormally high FDI investment. Using the model of McGrattan and Prescott (2009) and data from the IMF Balance of Payments to parameterize the time paths of FDI openness for each country in the sample, I do not find an economically significant relationship between the amount of inward FDI a country did over the period 1980—2005 and the growth in real GDP predicted by the model. This finding rests crucially on the fact that most of these countries are still in transition to FDI openness.

Previous studies quantifying the effects of increased taxation during the U.S. Great Depression find that its contribution is small, in accounting for both the downturn in the early 1930s and the slow recovery after 1934. This paper shows that this conclusion rests critically on the assumption that the only taxable capital income is business profits. Effects of capital taxation are much larger when taxes on property, capital stock, excess profits, undistributed profits, and dividends are included in the analysis. When fed into a general equilibrium model, the increased taxes imply significant declines in investment and equity values and nontrivial declines in gross domestic product (GDP) and hours of work. Of particular importance during the Great Depression was the dramatic rise in the effective tax rate on corporate dividends.

In the 1970s macroeconomists often disagreed bitterly. Macroeconomists have now largely converged on method, model design, and macroeconomic policy advice. The disagreements that remain all stem from the practical implementation of the methodology. Some macroeconomists think that New Keynesian models are on the verge of being useful for quarter-to-quarter quantitative policy advice. We do not. We argue that the shocks in these models are dubiously structural and show that many of the features of the model as well as the implications due to these features are inconsistent with microeconomic evidence. These arguments lead us to conclude that New Keynesian models are not yet useful for policy analysis.

A framework is developed with what we call technology capital. A country is a measure of locations. Absent policy constraints, a firm owning a unit of technology capital can produce the composite output good using the unit of technology capital at as many locations as it chooses. But it can operate only one operation at a given location, so the number of locations is what constrains the number of units it operates using this unit of technology capital. If it has two units of technology capital, it can operate twice as many operations at every location. In this paper, aggregation is carried out and the aggregate production functions for the countries are derived. Our framework interacts well with the national accounts in the same way as does the neoclassical growth model. It also interacts well with the international accounts. There are constant returns to scale, and therefore no monopoly rents. Yet there are gains to being economically integrated. In the framework, a country’s openness is measured by the effect of its policies on the productivity of foreign operations. Our analysis indicates that there are large gains to this openness.

We make three comparisons relevant for the business cycle accounting approach. We show that in theory representing the investment wedge as a tax on investment is equivalent to representing this wedge as a tax on capital income as long as the probability distributions over this wedge in the two representations are the same. In practice, convenience dictates that the underlying probability distributions over the investment wedge are different in the two representations. Even so, the quantitative results under the two representations are essentially identical. We also compare our methodology, the CKM methodology, to an alternative one used in Christiano and Davis (2006) as well as by us in early incarnations of the business cycle accounting approach. We argue that the CKM methodology rests on more secure theoretical foundations. Finally, we show that the results from the VAR-style decomposition of Christiano and Davis reinforce the results of the business cycle decomposition of CKM.

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.

Expensed investments are expenditures financed by the owners of capital that increase future profits but, by national accounting rules, are treated as an operating expense rather than as a capital expenditure. Sweat investment is financed by worker-owners who allocate time to their business and receive compensation at less than their market rate. Such investments are made with the expectation of realizing capital gains when the business goes public or is sold. But these investments are not included in GDP. Taking into account hours spent building equity while ignoring the output introduces an error in measured productivity and distorts the picture of what is happening in the economy. In this paper, we incorporate expensed and sweat equity in an otherwise standard business cycle model. We use the model to analyze productivity in the United States during the 1990s boom. We find that expensed plus sweat investment was large during this period and critical for understanding the dramatic rise in hours and the modest growth in measured productivity.

The main substantive finding of the recent structural vector autoregression literature with a differenced specification of hours (DSVAR) is that technology shocks lead to a fall in hours. Researchers have used these results to argue that business cycle models in which technology shocks lead to a rise in hours should be discarded. We evaluate the DSVAR approach by asking, is the specification derived from this approach misspecified when the data are generated by the very model the literature is trying to discard? We find that it is misspecified. Moreover, this misspecification is so great that it leads to mistaken inferences that are quantitatively large. We show that the other popular specification that uses the level of hours (LSVAR) is also misspecified. We argue that alternative state space approaches, including the business cycle accounting approach, are more fruitful techniques for guiding the development of business cycle theory.

This paper proposes a simple method for guiding researchers in developing quantitative models of economic fluctuations. We show that a large class of models, including models with various frictions, are equivalent to a prototype growth model with time-varying wedges that, at least at face value, look like time-varying productivity, labor taxes, and capital income taxes. We label the time-varying wedges as efficiency wedges, labor wedges, and investment wedges. We use data to measure these wedges and then feed them back into the prototype growth model. We then assess the fraction of fluctuations accounted for by these wedges during the great depressions of the 1930s in the United States, Germany, and Canada. We find that the efficiency and labor wedges in combination account for essentially all of the declines and subsequent recoveries. Investment wedges play, at best, a minor role.

U.S. stock prices have increased much faster than gross domestic product GDP) in the postwar period. Between 1962 and 2000, corporate equity value relative to GDP nearly doubled. In this paper, we determine what standard growth theory says the equity value should be in 1962 and 2000, the two years for which our steady-state assumption is a reasonable one. We find that the actual valuations were close to the theoretical predictions in both years. The reason for the large run-up in equity value relative to GDP is that the average tax rate on dividends fell dramatically between 1962 and 2000. We also find that, given legal constraints that effectively prohibited the holding of stocks as reserves for pension plans, there is no equity premium puzzle in the postwar period. The average returns on debt and equity are as theory predicts.

In this paper, I characterize equilibria for a sticky-price model in which Federal Reserve policy is an interest-rate rule similar to that described in Taylor (1993). For standard preferences and technologies used in the literature, the model predicts that the nominal interest rate is negatively serially correlated, and that shocks to interest rates imply a potentially large but short-lived response in output. Shocks to government spending and technology lead to persistent changes in output but the percentage change in output is predicted to be smaller than the percentage changes in spending or technology. I compare the model’s predictions to data using innovations backed out from estimated processes for interest rates, government spending, and technology shocks. These comparisons confirm the theoretical findings. In response to observed changes in government spending and technology, the model predicts a path for output that is much smoother than the data and much smoother than that predicted by non-sticky price models.

We describe a model for calculating the optimal quantity of debt and then apply it to the U.S. economy. The model consists of a large number of infinitely-lived households whose saving behavior is influenced by precautionary saving motives and borrowing constraints. This model incorporates a different role for government debt than the standard representative agent growth model and captures different trade-offs between the benefits and costs of varying its level. Government debt enhances the liquidity of households by providing additional assets for smoothing consumption (in addition to claims to capital) and effectively loosening borrowing constraints. By raising the interest rate, government debt makes assets less costly to hold and more effective in smoothing consumption. However, the implied taxes have wealth distribution, incentive, and insurance effects. Further, government debt crowds out capital (via higher interest rates) and lowers per capita consumption. Our quantitative analysis suggests that the crowding out effect is decisive for welfare. We also describe variations of the model which permit endogenous growth. It turns out that even with lump sum taxes and inelastic labor, government debt as well as government consumption have growth rate effects, thereby implying large welfare gains from reducing the level of debt.

A central debate in applied macroeconomics is whether statistical tools that use minimal identifying assumptions are useful for isolating promising models within a broad class. In this paper, I compare three statistical models—a vector autoregressive moving average (VARMA) model, an unrestricted state space model, and a restricted state space model—that are all consistent with the same prototype business cycle model. The business cycle model is a prototype in the sense that many models, with various frictions and shocks, are observationally equivalent to it. The statistical models I consider differ in the amount of a priori theory that is imposed, with VARMAs imposing minimal assumptions and restricted state space models imposing the maximal. The objective is to determine if it is possible to successfully uncover statistics of interest for business cycle theorists with sample sizes used in practice and only minimal identifying assumptions imposed. I find that the identifying assumptions of VARMAs and unrestricted state space models are too minimal: The range of estimates are so large as to be uninformative for most statistics that business cycle researchers need to distinguish alternative theories.

During the downturn of 2008–2009, output and hours fell significantly while labor productivity rose. These facts have led many to conclude that there is a significant deviation between observations and current macrotheories that assume business cycles are driven, at least in part, by fluctuations in total factor productivities of firms. We show that once investment in intangible capital is included in the analysis, there is no inconsistency. Measured labor productivity rises if the fall in output is underestimated; this occurs when there are large unmeasured intangible investments. Microevidence suggests that these investments are large and cyclically important.

Michael Christian's paper presents a human capital account for the United States for the period 1994 to 2006. The main findings are twofold. First, the total human capital stock is about three-quarters of a quadrillion dollars in 2006. This estimate is roughly 55 times gross domestic product (GDP) and 16 times the net stock of fixed assets plus consumer durables. His second finding is that the measures of gross investment in human capital are sensitive to alternative assumptions about enrollment patterns. In my comments, I emphasize the need for greater interaction between human capital accountants and applied economists. To date, there remains a disconnect between those measuring human wealth and those investigating its economic impact.

Macroeconomists have largely converged on method, model design, reduced-form shocks, and principles of policy advice. Our main disagreements today are about implementing the methodology. Some think New Keynesian models are ready to be used for quarter-to-quarter quantitative policy advice; we do not. Focusing on the state-of-the-art version of these models, we argue that some of its shocks and other features are not structural or consistent with microeconomic evidence. Since an accurate structural model is essential to reliably evaluate the effects of policies, we conclude that New Keynesian models are not yet useful for policy analysis.

This paper describes trends in average weekly hours of market work per person and per family in the United States between 1950 and 2005. We disaggregate married couple households by skill level to determine if there is a pattern in the hours of work by wives and husbands conditional on either husband’s wages or husband’s educational attainment. The wage measure of skill allows us to compare our findings to those of Juhn and Murphy (1997), who report on trends in family labor using a different data set. The educational measure of skill allows us to construct a longer time series. We find several interesting patterns. The married women with the largest increase in market hours are those with high-skilled husbands. When we compare households with different skill mixes, we also find dramatic differences in the time paths, with higher skill households having the largest increase in average hours over time.

In this paper, we extend the growth model to include firm-specific technology capital and use it to assess the gains from opening to foreign direct investment. A firm’s technology capital is its unique know-how from investing in research and development, brands, and organization capital. Technology capital is distinguished from other forms of capital in that a firm can use it simultaneously in multiple domestic and foreign locations. A country can exploit foreign technology capital by permitting direct investment by foreign multinationals. In both steady-state and transitional analyses, the extended growth model predicts large gains to being open.

Using simulations from a multicountry neoclassical growth model, we analyze several post-Brexit scenarios. First, the United Kingdom unilaterally imposes tighter restrictions on FDI and trade from other EU nations. Second, the European Union retaliates and imposes the same restrictions on the UK. Finally, the United Kingdom reduces restrictions on other nations during the post-Brexit transition. Model predictions depend crucially on the policy response of multinationals’ investment in technology capital, accumulated know-how from investments in R&D, brands, and organizations used simultaneously in their domestic and foreign operations.

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.

We elaborate on the business cycle accounting method proposed by Chari, Kehoe, and McGrattan (2007), clear up some misconceptions about the method, and then apply it to compare the Great Recession across OECD countries as well as to the recessions of the 1980s in these countries. We have four main …findings. First, with the notable exception of the United States, Spain, Ireland, and Iceland, the Great Recession was driven primarily by the efficiency wedge. Second, in the Great Recession, the labor wedge plays a dominant role only in the United States, and the investment wedge plays a dominant role in Spain, Ireland, and Iceland. Third, in the recessions of the 1980s, the labor wedge played a dominant role only in France, the United Kingdom, Belgium, and New Zealand. Finally, overall in the Great Recession the efficiency wedge played a more important role and the investment wedge played a less important role than they did in the recessions of the 1980s.

We make three comparisons relevant for the business cycle accounting approach. We show that in theory, representing the investment wedge as a tax on investment is equivalent to representing this wedge as a tax on capital income as long as the probability distributions over this wedge in the two representations are the same. In practice, convenience dictates that the underlying probability distributions over the investment wedge are different in the two representations. Even so, the quantitative results under the two representations are essentially identical. We also compare our methodology, the CKM methodology, to an alternative one used in Christiano and Davis (2006) and by us in early incarnations of the business cycle accounting approach. We argue that the CKM methodology rests on more secure theoretical foundations. Finally, we show that the results from the VAR-style decomposition of Christiano and Davis reinforce the results of the business cycle decomposition of CKM.

Real business cycles are recurrent fluctuations in an economy’s incomes, products, and factor inputs—especially labor—that are due to nonmonetary sources. These sources include changes in technology, tax rates and government spending, tastes, government regulation, terms of trade, and energy prices. Most real business cycle (RBC) models are variants or extensions of a neoclassical growth model. One such prototype is introduced. It is then shown how RBC theorists, applying the methodology of Kydland and Prescott (Econometrica 1982), use theory to make predictions about actual time series. Extensions of the prototype model, current issues, and open questions are also discussed.