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.

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.

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.

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.

Previous studies of the U.S. Great Depression find that increased government spending and taxation contributed little to either the dramatic downturn or the slow recovery. These studies include only one type of capital taxation: a business profits tax. The contribution is much greater when the analysis includes other types of capital taxes. A general equilibrium model extended to include taxes on dividends, property, capital stock, and excess and undistributed profits predicts patterns of output, investment, and hours worked that are more like those in the 1930s than found in earlier studies. The greatest effects come from the increased taxes on corporate dividends and undistributed profits.

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.

The central finding of the recent structural vector autoregression (SVAR) literature with a differenced specification of hours is that technology shocks lead to a fall in hours. Researchers have used this finding to argue that real business cycle models are unpromising. We subject this SVAR specification to a natural economic test and show that when applied to data from a multiple-shock business cycle model, the procedure incorrectly concludes that the model could not have generated the data as long as demand shocks play a nontrivial role. We also test another popular specification, which uses the level of hours, and show that with nontrivial demand shocks, it cannot distinguish between real business cycle models and sticky price models. The crux of the problem for both SVAR specifications is that available data require a VAR with a small number of lags and such a VAR is a poor approximation to the model’s VAR.