Susanto Basu

Returns to Scale in U.S. Production: Estimates and Implications

A typical (roughly) two‐digit industry in the United States appears to have constant or slightly decreasing returns to scale. Three puzzles emerge, however. First, estimates often rise at higher levels of aggregation. Second, apparent decreasing returns contradicts evidence of only small economic profits. Third, estimates with value added differ substantially from those with gross output. A representative‐firm paradigm cannot explain these puzzles, but a simple story of aggregation over heterogeneous units can. Theory and evidence on aggregation invalidate the common use of demand‐side instruments. Finally, we discuss implications of heterogencity for macroeconomic modeling: A one‐sector macroeconomic model that ignores heterogeneity may sometimes require firm‐level parameters, but at other times the model may require the “biased” aggregate parameters.

Are apparent productive spillovers a figment of specification error

Using data on gross output for two-digit manufacturing industries, we find that an increase in the output of one manufacturing sector has little or no significant effect on the productivity of other sectors. Using value-added data, however, we confirm the results of previous studies which find that output spillovers instead appear large. We provide an explanation for these differences, showing why, with imperfect competition, the use of value-added data leads to a spurious finding of large apparent external effects.

Aggregate Productivity and Aggregate Technology

Aggregate productivity and aggregate technology are meaningful but distinct concepts. We show that a slightly modified Solow productivity residual measures changes in economic welfare, even when productivity and technology differ because of distortions such as imperfect competition. We then present a general accounting framework that identifies several new non-technological gaps between productivity and technology, gaps reflecting imperfections and frictions in output and factor markets. Empirically, we find that these gaps are important, even though we abstract from variations in factor utilization and estimate only small average sectoral markups. Compared with productivity growth, our measured technology shocks are significantly less correlated with output and are essentially uncorrelated with inputs. Our results imply that calibrating dynamic general equilibrium models as if Solow residuals were technology shocks confuses impulses and propagation mechanisms.

THE VALUE OF RISK : MEASURING THE SERVICE OUTPUT OF U.S. COMMERCIAL BANKS

Rather than charging direct fees, banks often charge implicitly for their services via interest spreads. As a result, much of bank output has to be estimated indirectly. In contrast to current statistical practice, dynamic optimizing models of banks argue that compensation for bearing systematic risk is not part of bank output. We apply these models and find that between 1997 and 2007, in the U.S. National Accounts, on average, bank output is overestimated by 21 percent and GDP is overestimated by 0.3 percent. Moreover, compared with current methods, our new estimates imply more plausible estimates of the share of capital in income and the return on fixed capital.(This abstract was borrowed from another version of this item.)

Productivity, welfare and reallocation : theory and firm-level evidence

A considerable literature has focused on the determinants of total factor productivity (TFP), prompted by the empirical finding that TFP accounts for the bulk of long-term growth. This paper offers a deeper reason for such focus: the welfare of a representative consumer is summarized by current and anticipated future Solow productivity residuals. The equivalence holds for any specification of technology and market structure, as long as the representative household maximizes utility while taking prices parametrically. This result justifies total factor productivity as the right summary measure of welfare, even in situations where it does not properly measure technology, and makes it possible to calculate the contributions of disaggregated units (industries or firms) to aggregate welfare using readily available data. Based on this finding, the authors compute firm and industry contributions to welfare for a set of European countries (Belgium, France, Great Britain, Italy, Spain) using industry-level and firm-level data. With additional assumptions about technology and market structure (specifically, that firms minimize costs and face common factor prices), the authors show that welfare change can be further decomposed into three components that reflect, respectively, technical change, aggregate distortions, and allocative efficiency. Then, using the appropriate firm-level data, they assess the importance of each of these components as sources of welfare improvement in the same set of European countries.

Productivity and the Welfare of Nations

This paper shows that the welfare of a countrys representative consumer can be measured using just two variables: current and future total factor productivity and the capital stock per capita. These variables suffice to calculate welfare changes within a country, as well as welfare differences across countries. The result holds regardless of the type of production technology and the degree of market competition. It applies to open economies as well, if total factor productivity is constructed using domestic absorption, instead of gross domestic product, as the measure of output. It also requires that total factor productivity be constructed with prices and quantities as perceived by consumers, not firms. Thus, factor shares need to be calculated using after-tax wages and rental rates and they will typically sum to less than one. These results are used to calculate welfare gaps and growth rates in a sample of developed countries with high-quality total factor productivity and capital data. Under realistic scenarios, the U.K. and Spain had the highest growth rates of welfare during the sample period 1985-2005, but the U.S. had the highest level of welfare.

Whither News Shocks

Does news about future productivity cause business-cycle fluctuations? What other effects might it have? We explore the answer to this question using semi-structural VARs, where “news” is defined as the innovation in the expectation of TFP at a fixed horizon in the future. We find that systems incorporating a number of forward-looking variables, including stock prices, consumption, consumer confidence and inflation, robustly predict three outcomes. First, following a news shock, TFP rises for several years. Second, inflation falls immediately and substantially, and stays low, often for 10 quarters or more. Third, there is a sharp increase in a forward-looking measure of consumer confidence. Consumption typically rises following good news, but investment, consumer durables purchases and hours worked typically fall on impact. All the quantity variables subsequently rise, as does TFP. Depending on the specification of the reduced form VAR, the activity variables may lead TFP to some extent – possibly lending some support to the hypothesis of news-driven business cycles – or they may move in lockstep with productivity. For the most part, the quantity and inflation responses are quite consistent with the predictions of a standard New Keynesian model augmented with real wage inertia.

Endogenous Volatility at the Zero Lower Bound: Implications for Stabilization Policy

At the zero lower bound, the central banks inability to offset shocks endogenously generates volatility. In this setting, an increase in uncertainty about future shocks causes significant contractions in the economy and may lead to non-existence of an equilibrium. The form of the monetary policy rule is crucial for avoiding catastrophic outcomes. State-contingent optimal monetary and fiscal policies can attenuate this endogenous volatility by stabilizing the distribution of future outcomes. Fluctuations in uncertainty and the zero lower bound help our model match the unconditional and stochastic volatility in the recent macroeconomic data.

The Value of Risk: Measuring the Service Output of U.S. Commercial Banks

Rather than charging direct fees, banks often charge implicitly for their services via interest spreads. As a result, much of bank output has to be estimated indirectly. In contrast to current statistical practice, dynamic optimizing models of banks argue that compensation for bearing systematic risk is not part of bank output. We apply these models and find that between 1997 and 2007, in the U.S. National Accounts, on average, bank output is overestimated by 21 percent and GDP is overestimated by 0.3 percent. Moreover, compared with current methods, our new estimates imply more plausible estimates of the share of capital in income and the return on fixed capital.

Uncertainty Shocks in a Model of Effective Demand: Reply

de Groot, Richter, and Throckmorton (2018) argue that the model in Basu and Bundick (2017) can match the empirical evidence only because the model assumes an asymptote in the economy’s response to an uncertainty shock. In this Reply, we provide new results showing that our model’s ability to match the data does not rely either on assuming preferences that imply an asymptote nor on a particular value of the intertemporal elasticity of substitution. We demonstrate that shifting to preferences that are not vulnerable to the Comment’s critique does not change our previous conclusions about the propagation of uncertainty shocks to macroeconomic outcomes. JEL Classification: E32, E52