Research

Physical capital takes time to build. Yet, the measurement of time to build and of its response to firm behavior remain scant. We fill this gap using project-level data from India. We first document new facts about time to build. Industry heterogeneity accounts for 30% of its variation; and time to build increases on average by 0.18% for each 1% increase in project cost. We exploit quasi-experimental variation in credit supply to document that firms have control over time to build. When credit dries up, the conditional probability of completing a project over the following quarter rises by 6%, consistent with firms accelerating project development. In turn, new project starts fall by 7.5%. To rationalize our findings, we introduce a model of endogenous time to build. A credit crunch increases firm appetite for immediate cash flows relative to delayed cash flows. Firms then accelerate existing, closer to completion projects and postpone unbegun projects. Such a mechanism is borne out in the data: projects proxied to be more mature are sped up the most. We quantify endogenous time to build by calibrating our model to match our causal estimates, and the joint distribution of project-level costs and gestation lags. Moving from exogenous to endogenous time to build amplifies the response of investment to shocks, increasing investment volatility by up to 30%. Endogenous gestation lags are policy relevant. Monetary policy is more potent when the distribution of projects along their gestation cycle skews towards mature projects. Fiscal policy, in turn, can flexibly reshuffle investment expenditures over time with tax credits.

The business cycles literature has recently embraced heterogeneous-agent (HA) models, which generate large and dispersed marginal propensities to consume (MPCs), in line with the data. In this paper, we focus on the precautionary saving implications of these models, a less-studied feature. We first show that two calibrated versions of the model, one with standard and another with present-biased preferences, can both match MPC profiles as well as a host of other moments, but differ in their predictions for precautionary saving. We then measure the precautionary saving channel in the data by studying the response of asset accumulation to variation in unemployment insurance (UI) schedules across U.S. states as well as over time. We find small, statistically non-significant effects. Reproducing our empirical design using model-simulated data, the empirical estimates reject the standard model but are in line with the present-biased model. To illustrate the implications of this difference, we study the stabilization properties of UI in an estimated HA New Keynesian model. In standard HA models, UI affects aggregate consumption largely by reducing precautionary savings. By weakening this effect, a model with present bias predicts a fiscal multiplier of temporary UI extensions 45% smaller than a standard model. Moreover, it predicts UI to have a smaller effect in reducing aggregate consumption volatility, being, therefore, a less powerful automatic stabilizer as well. 

We show that, in a broad class of menu cost models, the dynamics of aggregate inflation in response to arbitrary shocks to aggregate costs are nearly the same as in Calvo models with suitably chosen Calvo adjustment frequencies. We first prove that the canonical menu cost model is first-order equivalent to a mixture of two time-dependent models, which reflect the extensive and intensive margins of price adjustment. We then show numerically that, in any plausible parameterization, this mixture is well approximated by a single Calvo model. This close numerical fit carries over to other standard specifications of menu cost models. Thus, the Phillips curve for a menu cost model looks like the New Keynesian Phillips curve, but with a higher slope. 

We study a unique dataset with comprehensive coverage of daily prices in large multi-product retailers in Israel. Retail stores synchronize price changes around occasional “peak” days when they reprice around 10% of their products. To assess the aggregate implications of partial price synchronization, we develop a new model in which multi-product firms face economies of scope in price adjustment, and synchronization is endogenous. Synchronization of price changes attenuates the average price response to monetary shocks, but only high degrees of synchronization can substantially strengthen the real effects of monetary non-neutrality. Our calibrated model generates real effects similar in magnitude to those in Golosov and Lucas (2007). 

We propose a model that reconciles microeconomic evidence of frequent and large price changes with sizable monetary non-neutrality. Firms incur separate lump-sum costs to change prices and to gather and process some information about marginal costs. Additional relevant information is continuously available, and can be factored into pricing decisions at no cost. We estimate the model by Simulated Method of Moments, using price-setting statistics for the U.S. economy. The model with free idiosyncratic and costly aggregate information fits well both targeted and untargeted microeconomic moments and generates almost three times as much monetary non-neutrality as the Calvo model.