Microeconometric models of investment and employment
We survey recent microeconometric research on investment and employment that has used panel data on individual firms or plants. We focus on model specification and econometric estimation issues, but we also review some of the main empirical findings. We discuss advantages and limitations of microeconomic data in this context. We briefly review the neoclassical theory of the demand for capital and labour, on which most of the econometric models of investment and employment that we consider are based. We pay particular attention to dynamic factor demand models, based on the assumption that there are costs of adjustment, which have played a prominent role especially in the microeconometric literature on investment. With adjustment costs, current choices depend on expectations of future conditions. We discuss the challenges that this raises for econometric model specification, and some of the solutions that have been adopted. We also discuss estimation issues that arise for dynamic factor demand equations in the context of micro panel data for firms or plants. We then discuss a number of topics that have been the focus of recent microeconometric research on investment and employment. In particular, we review the literatures on investment and financing constraints, relative price effects on investment and employment, investment and uncertainty, investment in research and development (R&D), elasticities of substitution and complementarity between technology, capital and skilled and unskilled labour, and recent work on models with non-convex adjustment costs.
Spatial competition in retail markets: movie theaters
Retail markets are extremely important, but economists have few practical tools for analyzing the way dispersed buyers and sellers affect the properties of markets. I develop an econometric model of retail demand in which products are location specific and consumers have preferences over both geographic proximity and other store and product characteristics. The model uses data on the observed geographic distribution of consumers within a market to (1) help explain observed variation in market shares and (2) affect predicted substitution patterns between stores. Using data from the U.S. cinema industry, I use the estimated model to evaluate the form of consumer transport costs, the effect of a theater's price and quality choices on rivals, the effects of geographic differentiation, and the nature and extent of market power.
Selection Bias and Econometric Remedies in Accounting and Finance Research
While managers’ accounting and financial decisions are, for many, fascinating topics, selection bias poses a serious challenge to researchers estimating the decisions’ effects using non-experimental data. Selection bias potentially occurs because managers’ decisions are non-random and the outcomes of choices not made are never observable. “Selection bias due to observables” arises from sample differences that researchers can observe but fail to control. “Selection bias due to unobservables” arises from the unobservable and thus uncontrolled sample differences that affect managers’ decisions and their consequences. In this article I review two econometric tools developed to mitigate these biases – the propensity score matching (PSM) method to mitigate selection bias due to observables and the Heckman inverse-Mills-ratio (IMR) method to address selection bias due to unobservables – and discuss their applications in accounting and finance research. The article has four takeaways. First, researchers should select the correct method to alleviate potential selection bias: the PSM method mitigates selection bias due to observables, but does not alleviate selection bias due to unobservables. Second, in applying PSM researchers are advised to restrict their inferences to firms whose characteristics can be found in both the sample and control groups. Third, the IMR method, though popular, is limited to situations in which the choices are binary, the outcomes of choices are modeled in a linear regression, and the unobservables in the choice and outcome models follow a multivariate normal distribution. Researchers can overcome these constraints by using full information maximum likelihood estimation. Last, when the IMR method is used, special attention should be paid to the formulas in calculating IMRs. The article also calls for researchers’ attention to other approaches to evaluating the effects of managers’ decisions.
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