Knowledge that Transforms

This review provides a critical survey of psychology-and-economics (“behavioral-economics”) research in contract theory. First, I introduce the theories of individual decision making most frequently used in behavioral contract theory, and formally illustrate some of their implications in contracting settings. Second, I provide a more comprehensive (but informal) survey of the psychology-and-economics work on classical contract-theoretic topics: moral hazard, screening, mechanism design, and incomplete contracts. I also summarize research on a new topic spawned by psychology and economics, exploitative contracting, that studies contracts designed primarily to take advantage of agent mistakes. (JEL A12, D03, D82, D86)

We review the main identification strategies and empirical evidence on the role of expectations in the New Keynesian Phillips curve, paying particular attention to the issue of weak identification. Our goal is to provide a clear understanding of the role of expectations that integrates across the different papers and specifications in the literature. We discuss the properties of the various limited-information econometric methods used in the literature and provide explanations of why they produce conflicting results. Using a common dataset and a flexible empirical approach, we find that researchers are faced with substantial specification uncertainty, as different combinations of various a priori reasonable specification choices give rise to a vast set of point estimates. Moreover, given a specification, estimation is subject to considerable sampling uncertainty due to weak identification. We highlight the assumptions that seem to matter most for identification and the configuration of point estimates. We conclude that the literature has reached a limit on how much can be learned about the New Keynesian Phillips curve from aggregate macroeconomic time series. New identification approaches and new datasets are needed to reach an empirical consensus. (JEL C51, D84, E12, E24, E31)

This paper uses firm-level survey data matched with official tax records to estimate the unobserved true sales of formal firms in Mongolia. Taking into account firm-level incentives to comply with taxes and a production function technology linking unobserved true sales with observable firm-level production characteristics, we derive a multiple-indicators, multiple-causes model predicting true sales. We find that firms underreport sales to the tax office by 38.6%, but firm-level survey data also suffer from significant underreporting. Finally, we compare our approach with two alternative approaches of measuring underreporting and discuss the practical implications of the findings for firm-level analyses of underreporting.

Physician-induced demand, whereby physicians alter patient treatment for personal gain, lies at the heart of concerns about publicly provided health care. However, little is known about how payment systems affect the ultimate outcome of patient health. Exploiting a unique policy induced variation in Denmark, I investigate the impact of physician payment contracts on infant health. In a difference-in-differences framework, I find that firstborn infants exposed in the womb to the care of general practitioners with capitation contracts have poorer infant health outcomes than infants exposed to fee-for-service contracts. The firstborn children of younger women primarily drive the effects.

In this paper, I construct product-level U.S.-manufacturing-imports data for new products. I show that consistent with product cycles, the North's new-products exports to the United States, relative to its old-products exports, grow faster than the South's for over a decade; then the South catches up with the North, and this pattern is reversed. This finding holds up in parametric, nonparametric, and semiparametric estimations, and only when new products are properly identified and old products within the same industries are used as controls. There is also evidence that product cycles become shorter over time and they are technology related.

In this paper, we explore whether information from firm-level data can improve forecasts of aggregate productivity growth. We generate firm-level productivity measures and aggregate them into time-series components that capture within-firm productivity and the productivity contribution of reallocation. We show that these components improve aggregate total factor productivity forecasts in a simple univariate setting, even when firm-level data are available with a time lag. Lagged firm-level information also improves aggregate productivity forecasts when we combine results from a variety of different multivariate forecasting models using Bayesian model averaging techniques.

This paper introduces a new confidence interval (CI) for the autoregressive parameter (AR) in an AR(1) model that allows for conditional heteroskedasticity of a general form and AR parameters that are less than or equal to unity. The CI is a modification of Mikusheva's (2007a) modification of Stock's (1991) CI that employs the least squares estimator and a heteroskedasticity-robust variance estimator. The CI is shown to have correct asymptotic size and to be asymptotically similar (in a uniform sense). It does not require any tuning parameters. No existing procedures have these properties. Monte Carlo simulations show that the CI performs well in finite samples in terms of coverage probability and average length, for innovations with and without conditional heteroskedasticity.

We propose a random coefficients nested logit (RCNL) model to compare the tractable nested logit (NL) model with the more complex random coefficients logit (RC) model. After a simulation study, we use data on the European automobile market. Both the NL and RC models are rejected against the RCNL model. The RC model results in different substitution patterns and a wider market definition than the NL and RCNL models. Nevertheless, the predicted price effects from mergers are robust across models. Our findings stress the importance of accounting for discrete sources of market segmentation not captured by continuous product characteristics.

Firms may adjust capital and labor sequentially or simultaneously. In this paper, we develop a structural model of interrelated factor demand subject to nonconvex adjustment costs and estimated by simulated method of moments. Based on Norwegian manufacturing industry plant-level data, parameter estimates reveal cost advantages for adjusting capital and making net changes in labor simultaneously. Factor demand models with fully specified interrelated adjustment costs structures perform best to describe the dynamic panel data.

We use the unique regulatory environment of the pharmaceutical industry to examine how potential competition affects generic drug pricing. Our identification strategy exploits a provision of the Hatch-Waxman Act that awards 180 days of marketing exclusivity to the first valid generic drug applicant against the holder of a branded drug patent. In smaller drug markets, we find that price falls in response to an increase in potential competition. We also find that few manufacturers enter these markets, indicating this price reduction is an effective deterrent. In contrast, we find that generic incumbents accommodate entry in larger drug markets.