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,

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

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

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

MODELLING FARMS' PRODUCTION DECISIONS UNDER EXPENDITURE CONSTRAINTS

Limited budget for the purchase of variable inputs might adversely affect producer's input use decisions and might result in a non-optimal input usage. If expenditure constrains are present and binding, unconstrained profit-maximization is not valid for modelling producers' input use decisions. In this paper we apply the indirect production function approach which describes output maximization subject to a given technology, a set of quasi-fixed inputs and a given budget for the purchase of variable inputs. By employing the indirect production function in the stochastic frontier framework we can estimate producer's output loss due to both expenditure constraints and technical inefficiency. Our estimation results show that most of the study farms were expenditure constrained during the considered period. Expenditure constraints have caused on average a potential output loss of 11 percent. Output loss due to technical inefficiency is quite moderate and averages 18 percent.Indirect production function, SFA, expenditure constraints, technical efficiency, Russian agriculture, Farm Management, Research Methods/ Statistical Methods,

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

Biases in approximating log production

Most empirical work in economic growth assumes either a Cobb-Douglas production function expressed in logs or a log-approximated constant elasticity of substitution specification. Estimates from each are likely biased due to logging the model and the latter can also suffer from approximation bias. We illustrate this with a successful replication of Masanjala and Papagerogiou (2004) and then estimate both models in levels to avoid these biases. Our estimation in levels gives results in line with conventional wisdom.Nonparametric, Kmenta Approximation, Levels, Logs, PPML

Which banks choose deposit insurance? Evidence of adverse and moral hazard in a voluntary insurance system

The sharp increase in depository institution failures in recent years has drawn attention to the moral hazard created by under-priced deposit insurance. To identify possible reforms, researchers have begun to consider alternative deposit insurance arrangements. This paper contributes to that literature by examining the deposit insurance system of Kansas, which operated from 1909 to 1929. The Kansas system had a number of regulations that were intended to limit risk-taking, and membership was made voluntary to assuage objections that insurance forces conservative banks to protect depositors of high-risk institutions. Using individual bank data, we test explicitly whether adverse selection and moral hazard characterized the Kansas system. We find that risk-prone banks were the most likely to join the system at its inception. And, using a simultaneous equation model, we find that both adverse selection and moral hazard behavior were present throughout the system's first ten years.Deposit insurance

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 andWilson (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.Bootstrap; Nonparametric kernel; Technical efficiency