This comment points out mismeasurement of three of the variables in the DSGE model in Smets and Wouters (2007) and in models that use the Smets-Wouters model as a benchmark. The mismeasurement appears serious enough to call into question the reliability of empirical results using these variables.
This paper lists 19 points that follow from results I have obtained using a structural macroeconomic model (SEM). Such models are more closely tied to the aggregate data than are DSGE models, and I argue that DSGE models and similar models should have properties that are consistent with these points. The aim is to try to bring macro back to its empirical roots.
Weak contract enforcement may reduce the efficiency of investment in developing countries. I study how contract enforcement affects efficiency in procurement auctions for the largest power projects in India. I gather data on bidding and ex post contract renegotiation and find that the renegotiation of contracts in response to cost shocks is widespread, despite that bidders are allowed to index their bids to future costs like the price of coal. Connected firms choose to index less of the value of their bids to coal prices and, through this strategy, expose themselves to cost shocks to induce renegotiation. I use a structural model of bidding in a scoring auction to characterize equilibrium bidding when bidders are heterogeneous both in cost and in the payments they expect after renegotiation. The model estimates show that bidders offer power below cost due to the expected value of later renegotiation. The model is used to simulate bidding and efficiency with strict contract enforcement. Contract enforcement is found to be pro-competitive. With no renegotiation, equilibrium bids would rise to cover cost, but markups relative to total contract value fall sharply. Production costs decline, due to projects being allocated to lower-cost bidders over those who expect larger payments in renegotiation.
We use insurance claims data covering 28% of individuals with employer-sponsored health insurance in the United States to study the variation in health spending on the privately insured, examine the structure of insurer-hospital contracts, and analyze the variation in hospital prices across the nation. Health spending per privately insured beneficiary differs by a factor of three across geographic areas and has a very low correlation with Medicare spending. For the privately insured, half of the spending variation is driven by price variation across regions, and half is driven by quantity variation. Prices vary substantially across regions, across hospitals within regions, and even within hospitals. For example, even for a nearly homogeneous service such as lower-limb magnetic resonance imaging, about a fifth of the total case-level price variation occurs within a hospital in the cross section. Hospital market structure is strongly associated with price levels and contract structure. Prices at monopoly hospitals are 12% higher than those in markets with four or more rivals. Monopoly hospitals also have contracts that load more risk on insurers (e.g., they have more cases with prices set as a share of their charges). In concentrated insurer markets the opposite occurs—hospitals have lower prices and bear more financial risk. Examining the 366 mergers and acquisitions that occurred between 2007 and 2011, we find that prices increased by over 6% when the merging hospitals were geographically close (e.g., 5 miles or less apart), but not when the hospitals were geographically distant (e.g., over 25 miles apart). JEL Codes: I11, L10, L11.
This comment points out mismeasurement of variables in the DSGE model in Smets and Wouters (2007) and in models that follow the Smets-Wouters measurement procedures. The mismeasurement errors appear to be large.
This paper lists 19 points that follow from results I have obtained using a structural macroeconomic model (SEM). Such models are more closely tied to the aggregate data than are DSGE models, and I argue that DSGE models and similar models should have properties that are consistent with these points. The aim is to try to bring macro back to its empirical roots.
Weak contract enforcement may reduce the efficiency of production in developing countries. I study how contract enforcement affects efficiency in procurement auctions for the largest power projects in India. I gather data on bidding and ex post contract renegotiation and find that the renegotiation of contracts in response to cost shocks is widespread, despite that bidders are allowed to index their bids to future costs like the price of coal. Connected firms choose to index less of the value of their bids to coal prices and, through this strategy, expose themselves to cost shocks to induce renegotiation. I use a structural model of bidding in a scoring auction to characterize equilibrium bidding when bidders are heterogeneous both in cost and in the payments they expect after renegotiation. The model estimates show that bidders offer power below cost due to the expected value of later renegotiation. The model is used to simulate bidding and efficiency with strict contract enforcement. Contract enforcement is found to be pro-competitive. With no renegotiation, equilibrium bids would rise to cover cost, but markups relative to total contract value fall sharply. Production costs decline, due to projects being allocated to lower-cost bidders over those who expect larger payments in renegotiation.
Social sciences start by looking at the social-psychological attributes of humans to model and explain their observed behavior. However, we suggest starting the study of observed human behavior with the universal laws of physics, e.g., the principle of minimum action. In our proposed three-tier framework, behavior is a manifestation of action driven by physical, biological, and social-psychological principles at the core, intermediate, and top tier, respectively. More broadly, this reordering is an initial step towards building a platform for reorganizing the research methods used for theorizing and modeling behavior. This perspective outlines and illustrates how a physical law can account for observed human behavior and sketches the elements of a broader agenda.
Cross-border financial flows arise when (otherwise identical) countries differ in their abilities to use assets as collateral to back financial contracts. Financially integrated countries have access to the same set of financial instruments, and yet there is no price convergence of assets with identical payoffs, due to a gap in collateral values. Home (financially advanced) runs a current account deficit. Financial flows amplify asset price volatility in both countries, and gross flows driven by collateral differences collapse following bad news about fundamentals. Our results can explain financial flows among rich, similarly-developed countries, and why these flows increase volatility.