Estimation and Decomposition of TFP Growth in the Presence of Inefficiency and Production Risk

This paper addresses estimation and decomposition of total factor productivity (TFP) change. Usually TFP change is decomposed into technical change and scale effects. If inefficiency exists, it also contributes to productivity change. Here we decompose productivity change into efficiency change (both technical and allocative), technical change, and scale effects. Three alternative approaches using parametric production, cost, and profit functions, which differ in terms of behavioral assumptions on the producers and data requirements, are considered. Finally, we consider TFP growth when output is risky.factor productivity; inefficiency

RISK PREFERENCES AND TECHNOLOGY: A JOINT ANALYSIS

This paper deals with derivation and estimation of the risk preference function in the presence of output price uncertainty. The derivation depends neither on a specific parametric form of the utility function nor on any distribution of output price. The risk preference function is flexible enough to test different types of risk behavior (e.g., increasing, constant, and decreasing absolute risk aversion). We also test for asymmetry in the distribution of output price, which appears in the risk preference function. Moreover, we allow heterogeneity in production technology. Parameters of production technology and risk preference function are jointly estimated using the system of equations derived from the first-order conditions of expected utility of profit maximization and the production function. The estimated parameters of the risk preference function are used to calculate absolute, relative, and downside risks for each producer. A panel data on salmon farming from Norway is used as an application.Resource /Energy Economics and Policy,

Estimation of Technical and Allocative Inefficiencies in a Cost System: An Exact Maximum Likelihood Approach

Estimation and decomposition of overall (economic) efficiency into technical and allocative components goes back to Farrell (1957). However, in a cross-sectional framework joint econometric estimation of efficiency components has been mostly confined to restrictive production function models (such as the Cobb-Douglas). In this paper we implement a maximum likelihood (ML) procedure to estimate technical and allocative inefficiency using the dual cost system (cost function and the derivative conditions) in the presence of cross-sectional data. Specifically, the ML procedure is used to estimate simultaneously the translog cost system and cost increase due to both technical and allocative inefficiency. This solves the so-called ‘Greene problem’ in the efficiency literature. The proposed technique is applied to the Christensen and Greene (1976) data on U.S. electric utilities, and a cross-section of the Brynjolfsson and Hitt (2003) data on large U.S. firms.Technical inefficiency, allocative inefficiency, the Greene problem, translog cost function

A General Model of Technical Change with an Application to the OECD Countries

In the neoclassical production functions model technical change (TC) is assumed to be exogenous and it is specified as a function of time. However, some exogenous external factors other than time can also affect the rate of TC. In this paper we model TC via a combination of time trend (purely non-economic) and other observable exogenous factors, which we call technology shifters (economic factors). We use several composite technology indices based on appropriate combinations of the external economic factors which are indicators of different aspects of technology. These technology indices are embedded into the production function in such a way that they can complement to different inputs. By estimating the generalized production function, we get estimates of TC which is decomposed TC into a pure time component as well as several producer specific external economic factors. Furthermore, the technology shifters allow for non-neutral and biased shifts in TC. We also consider a simple model in which the technology shifters are aggregated into one single index. The empirical model uses panel data on OECD, accession and enhanced engagement countries observed during 1980-2006.technical change, total factor productivity growth, technology indicator, technology shifter, OECD countries

Financial Sector Development and Productivity Growth

productivity growth, financial sector development

The slack banker dances: deposit insurance and risk-taking in the banking collapse of the 1920s

This paper studies the effects of deposit insurance on bank behavior using individual bank data from Kansas in the 1920s. Kansas banks were severely stressed by the collapse of agricultural prices in 1920 and resulting increase in farm mortgage defaults. Because membership in the state deposit insurance system was voluntary, it is possible to compare the behavior of insured and non-insured banks facing similar exogenous circumstances. We find that deposit insurance encouraged excessive risk-taking, which helps to explain the comparatively high failure rate of insured banks. The deposit insurance fund ultimately failed to reimburse many depositors of failed banks. We find, however, no evidence of a decline in the credibility of insurance, and hence in the ability of insured banks to take excessive risks, before the system’s collapse in 1926.Bank failures ; Deposit insurance ; Banks and banking - History

Does Deregulation Change Economic Behavior of Firms?

Cost minimization and profit maximization behavioral assumptions are most widely used in microeconomic theory to analyze firm behavior. However, in practice researchers do not know whether every firm in the sample maximizes profit or minimizes cost. In this paper we address this problem via a latent class modeling approach in which we first consider the cost minimization problem (first class) and then the profit maximization problem (second class). The two problems are then mixed and the probabilities of class membership are made functions of covariates. This approach does not require researchers to know which firms maximize profit and which ones minimize cost. On the contrary, it helps us to determine not only which firms behave like profit maximizers but also why and what differentiates them from firms that failed to maximize profit. The new technique is illustrated using a panel data for the US airlines. The empirical findings suggest that very few airlines maximize profit consistently (if at all) and that deregulation had a positive impact on the chances of behaving like profit maximizers, although very few airlines continue to maximize profit even after the deregulation.

Impact of Reforms on Plant-Level Productivity and Technical Efficiency: Evidence from the Indian Manufacturing Sector

It is generally believed that the structural reforms that usher in competition and force companies to become more efficient were introduced later in India following the macroeconomic crisis in 1991. However, whether the post-1991 growth is an outcome of more efficient use of resources or greater use of factor inputs, especially capital, remains an open empirical question. In this paper, we use plant-level data from 1989-90 and 2000-01 to address this question. Our results indicate that while there was an increase in the productivity of factor inputs during the 1990s, most of the growth in value added is explained by growth in the use of factor inputs. We also find that median technical efficiency declined in all but one of the industries between the two years, and change in technical efficiency explains a very small proportion in the change in gross value added.efficiency, growth decomposition, productivity, manufacturing

Firm-Heterogeneity, Persistent and Transient Technical Inefficiency

This paper provides a new model that disentangles firm effects from persistent (time-invariant/long-term) and transient (time-varying/short-term) technical inefficiency.Bayesian analysis; Markov Chain Monte Carlo; Technical efficiency.

When, Where and How to Perform Efficiency Estimation

In this paper we compare two flexible estimators of technical efficiency in a cross-sectional setting: the nonparametric kernel SFA estimator of Fan, Li and Weersink (1996) to the nonparametric bias corrected DEA estimator of Kneip, Simar and Wilson (2008). We assess the finite sample performance of each estimator via Monte Carlo simulations and empirical examples. We find that the reliability of efficiency scores critically hinges upon the ratio of the variation in efficiency to the variation in noise. These results should be a valuable resource to both academic researchers and practitioners.nonparametric kernel, technical efficiency, bootstrap