John G. Fernald

Bio: John G. Fernald is an academic researcher from Federal Reserve Bank of San Francisco. The author has contributed to research in topics: Productivity & Total factor productivity. The author has an hindex of 41, co-authored 127 publications receiving 8298 citations. Previous affiliations of John G. Fernald include Federal Reserve System & INSEAD.

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

Abstract: 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 Technology Improvements Contractionary

Abstract: Yes. We construct a measure of aggregate technology change, controlling for varying utilization of capital and labor, non- constant returns and imperfect competition, and aggregation effects. On impact, when technology improves, input use and non- residential investment fall sharply. Output changes little. With a lag of several years, inputs and investment return to normal and output rises strongly. We discuss what models could be consistent with this evidence. For example, standard one-sector real-business-cycle models are not, since they generally predict that technology improvements are expansionary, with inputs and (especially) output rising immediately. However, the evidence is consistent with simple sticky-price models, which predict the results we find: When technology improves, input use and investment demand generally fall in the short run, and output itself may also fall.

Roads to Prosperity? Assessing the Link between Public Capital and Productivity

Abstract: Does the positive correlation between infrastructure and productivity reflect causation? If so, in which direction? The author finds that, when growth in roads (the largest component of infrastructure) changes, productivity growth changes disproportionately in U.S. industries with more vehicles. That vehicle-intensive industries benefit more from road-building suggests that roads are productive. At the margin, however, road investments do not appear unusually productive. Intuitively, the interstate system was highly productive, but a second one would not be. Road-building thus explains much of the productivity slowdown through a one-time, unrepeatable productivity boost in the 1950s and 1960s.

A Quarterly, Utilization-Adjusted Series on Total Factor Productivity

Abstract: This paper describes a real-time, quarterly growth-accounting database for the U.S. business sector. The data on inputs, including capital, are used to produce a quarterly series on total factor productivity (TFP). In addition, the dataset implements an adjustment for variations in factor utilization—labor effort and the workweek of capital. The utilization adjustment follows Basu, Fernald, and Kimball (BFK, 2006) as updated in Basu, Fernald, Fisher, and Kimball (BFFK, 2013). Using relative prices and input-output information, the series are also decomposed into separate TFP and utilization-adjusted TFP series for equipment investment (including consumer durables) and "consumption" (defined as business output less equipment and consumer durables).

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Abstract: 1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

The Science of Monetary Policy: A New Keynesian Perspective

Abstract: This paper reviews the recent literature on monetary policy rules. To organize the discussion, we exposit the monetary policy design problem within a simple baseline theoretical framework. We then consider the implications of adding various real word complications. We concentrate on developing results that are robust across a reasonable variety of competing macroeconomic frameworks. Among other things, we show that the optimal policy implicitly incorporates inflation targeting. We also characterize the gains from making credible commitments to fight inflation and consider the implications of frictions such as imperfect information and model uncertainty. Finally, we assess how proposed simple rules, such as the Taylor rule, square with the principles for optimal policy that we describe. We use this same metric to evaluate the recent course of U.S. monetary policy.

Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy

Abstract: We present a model embodying moderate amounts of nominal rigidities that accounts for the observed inertia in inflation and persistence in output. The key features of our model are those that prevent a sharp rise in marginal costs after an expansionary shock to monetary policy. Of these features, the most important are staggered wage contracts that have an average duration of three quarters and variable capital utilization.

The Science of Monetary Policy: A New Keynesian Perspective

Abstract: The paper reviews the recent literature on monetary policy rules. We exposit the monetary policy design problem within a simple baseline theoretical framework. We then consider the implications of adding various real world complications. Among other things, we show that the optimal policy implicitly incorporates inflation targeting. We also characterize the gains from making a credible commitment to fight inflation. In contrast to conventional wisdom, we show that gains from commitment may emerge even if the central bank is not trying to inadvisedly push output above its natural level. We also consider the implications of frictions such as imperfect information.

Estimating production functions using inputs to control for unobservables

Abstract: Economists began relating output to inputs in the early 1800's. A large literature on estimating production functions has followed, in part because much of economic theory yields testable implications that are related to the technology and optimizing behaviour.1 Since at least as early as Marschak and Andrews (1944), applied researchers have worried about the potential correlation between input levels and the unobserved firm-specific productivity shocks in the estimation of production function parameters. The economics underlying this concern are intuitive. Firms that have a large positive productivity shock may respond by using more inputs. To the extent that this is true, ordinary least squares (OLS) estimates of production functions will yield biased parameter estimates, and, by implication, biased estimates of productivity. Many alternatives to OLS have been proposed, and we add to this set by extending Olley and Pakes (1996). They show the conditions under which an investment proxy controls for correlation between input levels and the unobserved productivity shock. Their approach has the advantage that, for many questions, it is no more difficult to implement than OLS. We show when intermediate inputs (those inputs which are typically subtracted out in a value-added production function) can also solve this simultaneity problem. We discuss some potential benefits of expanding the choice set of proxies to include these inputs.