Hung-Jen Wang

One-step and two-step estimation of the effects of exogenous variables on technical efficiency levels

Consider a stochastic frontier model with one-sided inefficiency u, and suppose that the scale of u depends on some variables (firm characteristics) z. A “one-step” model specifies both the stochastic frontier and the way in which u depends on z, and can be estimated in a single step, for example by maximum likelihood. This is in contrast to a “two-step” procedure, where the first step is to estimate a standard stochastic frontier model, and the second step is to estimate the relationship between (estimated) u and z.In this paper we propose a class of one-step models based on the “scaling property” that u equals a function of z times a one-sided error u* whose distribution does not depend on z. We explain theoretically why two-step procedures are biased, and we present Monte Carlo evidence showing that the bias can be very severe. This evidence argues strongly for one-step models whenever one is interested in the effects of firm characteristics on efficiency levels.

Heteroscedasticity and non-monotonic efficiency effects of a stochastic frontier model

We consider a model that provides flexible parameterizations of the exogenous influences on inefficiency. In particular, we demonstrate the models unique property of accommodating non-monotonic efficiency effect. With this non-monotonicity, production efficiency no longer increases or decreases monotonically with the exogenous influence; instead, the relationship can shifts within the sample. Our empirical example shows that variables can indeed have non-monotonic effects on efficiency. Furthermore, ignoring non-monotonicity is shown to yield an inferior estimation of the model, which sometimes results in opposite predictions concerning the data.

A Stochastic Frontier Analysis of Financing Constraints on Investment

is shown that investment under financing constraints can be modeled as a one-sided deviation from a frictionless investment level, and that effects of financing constraints can be identified and quantified by imposing a distributional assumption on the effects. Panel data on Taiwanese manufacturing firms between 1989 and 1996 are used in the estimation. It is found that (1) some of the sorting criteria used in the literature do not have significant and monotonic relationships with the degrees of financing constraint, resulting in problematic sample separations, and (2) the effects of financial liberalization in Taiwan are such that the investment efficiency improved over time for a typical firm, and the improvement was particularly large for smaller firms.

Production Smoothing When Bank Loan Supply Shifts: The Role of Variable Capacity Utilization

How do firms smooth production when facing financing uncertainty? By using a model incorporating financing constraints, this paper shows that firms may adjust capacity utilization rates to buffer against financing disturbances. In particular, it emphasizes that variable capacity utilization plays the roles of both inter- and intra-temporal substitution of capital in this context. The paper presents results from the comparative statics and numerical calibrations of the model. These results show that the implied short-run dynamics are consistent with business cycle phenomena. The results also indicate that the long-run average of the capital stock is not likely to be affected by financing uncertainty, so that stabilization policy in the banking sector may only have a second-order welfare gain.

A Method of Moments Estimator for a Stochastic Frontier Model with Errors in Variables

We propose a method of moment estimator for a stochastic frontier model in which one of the independent variables is measured with errors. The estimator corrects for the measurement errors, and it requires only minimal assumption on the error distribution, has no need for additional data, and is computationally inexpensive. A Monte Carlo study shows favorable statistical properties of this estimator. We apply this estimator to an investment model with financing constraint, where a major explanatory variable, Tobins Q, is known to prone to measurement problems. We find that the Qs explanatory powers increase substantially upon correcting for the measurement errors.

Estimation of Technical Inefficiency in Production Frontier Models Using Cross-Sectional Data

In this paper we discuss the specification and estimation of technical efficiency in a variety of stochastic frontier production models. The focus is on cross-sectional models. We start from the basic neoclassical production theory and introduce technical inefficiency in there. Various model specifications with several distributional assumptions on the inefficiency component are explored in detail. Theoretical and empirical issues are illustrated with empirical examples using STATA.

Centered-Residuals-Based Moment Estimator and Test for Stochastic Frontier Models

The composed error of a stochastic frontier (SF) model consists of two random variables, and the identification of the model relies heavily on the distribution assumptions for each of these variables. While the literature has put much effort into applying various SF models to a wide range of empirical problems, little has been done to test the distribution assumptions of these two variables. In this article, by exploiting the specification structures of the SF model, we propose a centered-residuals-based method of moments which can be easily and flexibly applied to testing the distribution assumptions on both of the random variables and to estimating the model parameters. A Monte Carlo simulation is conducted to assess the performance of the proposed method. We also provide two empirical examples to demonstrate the use of the proposed estimator and test using real data.

Nominal data and the production smoothing hypothesis

Abstract This paper shows that when price variations are not properly purged from the data, the estimated ratios of production variances to sales variances are likely to be biased upward, leading to more frequent rejections of the production smoothing hypothesis. The results point to the importance of removing price variations from the data before testing the hypothesis.

Exogenous cash: testing financing constraints on inventory investment using dynamic panels with additional information from annual reports

Abstract In spite of the literature’s great interests in firms’ financing constraints on investment, the endogeneity problem between investment and cash flow have long plagued the empirical endeavor. To avoid the problem, we use firms’ nonoperating income, which is shown to be exogenous to profitability, both as a direct measure of liquidity and as an instrumental variable to control for the endogeneity problem of cash flow. Estimation results do not support the financing constraint hypothesis. We also conduct a narrative analysis on firms’ Annual Reports to identify factors causing inventory reduction. We find that accounting adjustments and decreases in market demand are two important factors in investment disinvestment, and financial difficulty does not play an important role in this regard.

A Stochastic Frontier Model with Endogenous Treatment Status and Mediator

Government policies are frequently used to promote productivity. Some policies are designed to enhance production technology, while others are meant to improve production efficiency. An important issue to consider when designing and evaluating policies is whether a mediator is required or effective in achieving the desired final outcome. To better understand and evaluate the policies, we propose a new stochastic frontier model with a treatment status and a mediator, both of which are allowed to be endogenous. The model allows us to decompose the total program (treatment) effect into technology and efficiency components, and to investigate whether the effect is derived directly from the program or indirectly through a particular mediator. Supplementary materials for this article are available online.