Project evaluation and organizational form

In situations of imperfect testing and communication, as suggested by Sah and Stiglitz (AER, 1986), organizational forms can be identified with different rules of aggregating evaluations of individual screening units. In this paper, we discuss the relative merits of polyarchical organizations versus hierarchical organizations in evaluating cost-reducing R&D projects when individual units' decision thresholds are fully endogenous. Contrary to the results of Sah and Stiglitz, we find that the relative merit of an organizational form depends on the curvature of the screening functions of the individual evaluation units. We find that for certain parameters organizations would want to implement asymmetric decision rules across screening units. This allows us to derive sufficient conditions for a polyarchy to dominate a hierarchy. We also find conditions for which the cost curves associated with the two organizational forms cross each other. In this case the optimal organizational form will depend on product market conditions and on the “lumpiness” of cost-reducing R&D.     

The competitive firm under price uncertainty: the role of information and hedging

We study the impact of transparency in a commodity market on the decision problem of a competitive firm under price uncertainty and hedging opportunities. Market transparency is modeled by means of the informational content of publicly observable signals which are correlated with the random price. We find that the impact of more transparency on labor employment and production depends on the firm’s technology. In particular, more transparency may result in lower average output even though on average more labor has been used in the production process. We also analyze the link between market transparency and the welfare of the firm. We are grateful to two anonymous referees who made extremely useful comments and suggestions.     

Maternal employment,migration, and child development

We analyze the roles of and interrelationships among school inputs and parental inputs in affecting child development through the specification and estimation of a behavioral model of household migration and maternal employment decisions. We integrate information on these decisions with observations on child outcomes over a 13-year period from the National Longitudinal Study of Youth (NLSY). We find that the impact of our school quality measures diminishes by factors of 2 to 4 after accounting for the fact that families may choose where to live in part based on school characteristics and labor market opportunities. The positive statistical relationship between child outcomes and maternal employment reverses sign and remains statistically significant after controlling for its possible endogeneity. Our estimates imply that when parental responses are taken into account, policy changes in school quality end up having only minor impacts on child test scores.     

Corporate financial determinants of foreign direct investment

Financial market incompleteness and (partial) segmentation of financial markets internationally may endow some firms with a financial advantage which can be exploited through foreign direct investment. We argue that this advantage appears as a distinct cost-of-capital effect on FDI, and identify possible channels for such an effect. Using a sample of European firms’ cross-border acquisitions, and controlling for traditional firm-level determinants of FDI, we find strong support for a cost-of-equity effect, whereas the effect of debt costs is indeterminate. Moreover, financial FDI determinants are more important for firms with high knowledge intensity and for firms resident in relatively less financially developed countries.     

A spatio-temporal model of house prices in the USA

This paper provides an empirical analysis of changes in real house prices in the USA using State level data. It examines the extent to which real house prices at the State level are driven by fundamentals such as real per capita disposable income, as well as by common shocks, and determines the speed of adjustment of real house prices to macroeconomic and local disturbances. We take explicit account of both cross-sectional dependence and heterogeneity. This allows us to find a cointegrating relationship between real house prices and real per capita incomes with coefficients (1,−1)$(1,-1)$, as predicted by the theory. We are also able to identify a significant negative effect for a net borrowing cost variable, and a significant positive effect for the State level population growth on changes in real house prices. Using this model we then examine the role of spatial factors, in particular, the effect of contiguous states by use of a weighting matrix. We are able to identify a significant spatial effect, even after controlling for State specific real incomes, and allowing for a number of unobserved common factors. We do, however, find evidence of departures from long run equilibrium in the housing markets in a number of States notably California, New York, Massachusetts, and to a lesser extent Connecticut, Rhode Island, Oregon and Washington State.     

“Waiting for Life to Arrive”: A history of the regression-discontinuity design in Psychology,Statistics and Economics

This paper reviews the history of the regression discontinuity design in three academic disciplines. It describes the design's birth and subsequent demise in Psychology even though most problems with it had been solved there. It further describes the scant interest shown in the design by scholars formally trained in Statistics, and the design's poor reception in Economics from 1972 until about 1995, when its profile and acceptance changed. Reasons are given for this checkered history that is characterized as waiting for life to arrive.     

Identification in a model of sorting with social externalities and the causes of urban segregation

This paper discusses nonparametric identification in a model of sorting in which location choices depend on the location choices of other agents as well as prices and exogenous location characteristics. In this model, demand slopes and hence preferences are not identifiable without further restrictions because of the absence of independent variation of endogenous composition and exogenous location characteristics. Several solutions of this problem are presented and applied to data on neighborhoods in US cities. These solutions use exclusion restrictions, based on either subgroup demand shifters, the spatial structure of externalities, or the dynamics of prices and composition in response to an amenity shock. The empirical results consistently suggest the presence of strong social externalities, that is a dependence of location choices on neighborhood composition.     

Organizational design with a budget constraint

In this paper we point out that the presence of the budget constraint substantially affects the relative performance of organizational decision rules. For instance, if there are only two types of projects to decide upon, the hierarchy always performs better than the polyarchy. The same result holds for any distribution of the quality of projects. We also illustrate that optimal mechanisms for variable evaluation costs involve stopping rules which look hierarchical, but avoid costly duplication as in hierarchies or polyarchies. Received: 5 November 1995 / Accepted: 18 September 1997     

Multiplicative-error models with sample selection

This paper presents a simple approach to deal with sample selection in models with multiplicative errors. Models for non-negative limited dependent variables such as counts fit this framework. The approach builds on a specification of the conditional mean of the outcome only and is, therefore, semiparametric in nature. GMM estimators are constructed for both cross-section data and for panel data. We derive distribution theory and present Monte Carlo evidence on the finite-sample performance of the estimators.     

Inference with dependent data using cluster covariance estimators

This paper presents an inference approach for dependent data in time series, spatial, and panel data applications. The method involves constructing t and Wald statistics using a cluster covariance matrix estimator (CCE). We use an approximation that takes the number of clusters/groups as fixed and the number of observations per group to be large. The resulting limiting distributions of the t and Wald statistics are standard t and F distributions where the number of groups plays the role of sample size. Using a small number of groups is analogous to ‘fixed-b’ asymptotics of [Kiefer and Vogelsang, 2002] and [Kiefer and Vogelsang, 2005] (KV) for heteroskedasticity and autocorrelation consistent inference. We provide simulation evidence that demonstrates that the procedure substantially outperforms conventional inference procedures.     

Identification of binary choice models with social interactions

This paper provides a set of results on the econometric identifiability of binary choice models with social interactions. Our analysis moves beyond parametric identification results that have been obtained in the literature to consider the identifiability of model parameters when the distribution of random payoff terms is unknown. Further, we consider how identification is affected by the presence of unobservable payoff terms of various types as well as identification in the presence of certain forms of endogenous group membership. Our results suggest that at least partial identification may be achieved under assumptions that in certain contexts may be plausible.     

Panel data models with spatially correlated error components

In this paper we consider a panel data model with error components that are both spatially and time-wise correlated. The model blends specifications typically considered in the spatial literature with those considered in the error components literature. We introduce generalizations of the generalized moments estimators suggested in Kelejian and Prucha (1999. A generalized moments estimator for the autoregressive parameter in a spatial model. International Economic Review 40, 509–533) for estimating the spatial autoregressive parameter and the variance components of the disturbance process. We then use those estimators to define a feasible generalized least squares procedure for the regression parameters. We give formal large sample results for the proposed estimators. We emphasize that our estimators remain computationally feasible even in large samples.     

First difference maximum likelihood and dynamic panel estimation

First difference maximum likelihood (FDML) seems an attractive estimation methodology in dynamic panel data modeling because differencing eliminates fixed effects and, in the case of a unit root, differencing transforms the data to stationarity, thereby addressing both incidental parameter problems and the possible effects of nonstationarity. This paper draws attention to certain pathologies that arise in the use of FDML that have gone unnoticed in the literature and that affect both finite sample performance and asymptotics. FDML uses the Gaussian likelihood function for first differenced data and parameter estimation is based on the whole domain over which the log-likelihood is defined. However, extending the domain of the likelihood beyond the stationary region has certain consequences that have a major effect on finite sample and asymptotic performance. First, the extended likelihood is not the true likelihood even in the Gaussian case and it has a finite upper bound of definition. Second, it is often bimodal, and one of its peaks can be so peculiar that numerical maximization of the extended likelihood frequently fails to locate the global maximum. As a result of these pathologies, the FDML estimator is a restricted estimator, numerical implementation is not straightforward and asymptotics are hard to derive in cases where the peculiarity occurs with non-negligible probabilities. The peculiarities in the likelihood are found to be particularly marked in time series with a unit root. In this case, the asymptotic distribution of the FDMLE has bounded support and its density is infinite at the upper bound when the time series sample size T→∞$T\to \infty$. As the panel width n→∞$n\to \infty$ the pathology is removed and the limit theory is normal. This result applies even for T$T$ fixed and we present an expression for the asymptotic distribution which does not depend on the time dimension. We also show how this limit theory depends on the form of the extended likelihood.     

How to get firms to invest: A simple solution to the hold-up problem in regulation

Abstract. Many governmental programs are effective only if firms make costly investments. The inability of authorities to precommit to a regulatory scheme creates incentives for firms not to invest and to hold-up the regulator. This paper describes a simple subsidy/tax scheme embedded in a four-stage mechanism that solves the hold-up problem. We design a self-financing subsidy/tax scheme which benefits a complying firm at the expense of a non-complying firm. In order to be credible, the subsidy and tax rates must maximize social welfare for any combination of investment decisions. We show that there exists a unique subgame perfect equilibrium in which all firms invest and no actual implementation with subsidies and taxes is required. We discuss in which cases the mechanism can work under incomplete information. Received: 30 December 1998 / Accepted: 12 October 2001     

Contractual solutions to hold-up problems with quality uncertainty and unobservable investments

Consider a seller and a buyer who write a contract. After that, the seller produces a good. She can influence the expected quality of the good by making unobservable investments. Only the seller learns the realized quality. Finally, trade can occur. It is always ex post efficient to trade. Yet, it may be impossible to achieve the first best, even though the risk-neutral parties are symmetrically informed at the contracting stage and complete contracts can be written. The second best is characterized by distortions that are reminiscent of adverse selection models (i.e., models with precontractual private information but without hidden actions).