Technical Efficiency of Maize Production in Fluoride Affected Locales, Tamil Nadu: A Stochastic Frontier Approach

To estimate the technical efficiency of maize production among fluoride affected and non affected locales of Tamil Nadu. A multi-stage sampling method involving a combination of purposive and random sampling procedures was employed in drawing up the samples for collecting primary data. The sample size is about 120. Stochastic frontier production function is used to estimate technical efficiency of maize. The result of stochastic frontier production function indicated that FYM, Potassium, machine power, irrigation and management index have significant influence on yield of maize in less fluoride affected locale, while, seed rate, nitrogen, phosphorous, machine power and irrigation are significantly influence the yield of maize in moderately fluoride affected locale, in case of highly fluoride affected locale, seed rate, nitrogen, phosphorous, potassium and irrigation are significantly influencing the yield of maize, while, nitrogen, potassium, irrigation and management index are significantly influences the yield of maize in non affected locale. The study suggests that awareness of fluoride contamination and averting measures must be disseminated to the farmers

TECHNICAL EFFICIENCY IN RUSSIAN AGRICULTURE

For decades, Russian agriculture had had little technological progress and virtually no foreign investment, which resulted in a stable production possibilities frontier and made the sector ideally suited to production function analysis. The production function estimations reported in Chapters 10-13 add to a series of previous studies of the input/output relationship in Russian agriculture (e.g., Clayton, 1980, 1984; Gray, 1981; Johnson and Brooks, 1983), which generally followed the same methodology. In the late 1970s and the 1980s, however, the average response production functions gave way in the economics literature to more sophisticated production analysis techniques that measured not only productivity but technical efficiency as well (Aigner, et al., 1977; Bauer, 1990). Some of the major methodological advances in applying technical efficiency analysis to individual firms were made by a joint Russian-American team in Moscow in the early 1980s (Jondrow, et al., 1982; Danlin et al., 1985), but lack of data for many sectors of the Russian economy precluded the application of this technique until the end of the decade. When the Soviet Union collapsed, the initial optimistic expectation was that many sectors of the new Russian economy could rapidly achieve both higher productivity and higher technical efficiency once market forces prevailed. Our research attempts to understand why this has not happened in Russian agriculture in terms of technical efficiency.Research and Development/Tech Change/Emerging Technologies,

Technical Efficiency Evaluation: Naturally Dual!

We provide a dual perspective on technical efficiency evaluation, in two respects. First, we build on the price assumptions implicitly associated with the notion of technical efficiency in a general equilibrium framework to characterize a set of appropriate references to be used in the technical efficiency evaluation of an input-output vector. Some existing evaluation methods always select an element of this set, but other methods fail to do so. Second, the above framework leads us to assert that a well-grounded measure of technical efficiency is naturally decomposable. One part refers to technical efficiency as captured by the classical Debreu-Farrell measure. The other part refers to technical efficiency resulting from the “implicit allocative efficiency” or “mix efficiency” of the evaluated vector. We present both a quantity-based distance measure and its price-based equivalent to evaluate this complementary dimension of technical efficiency. This generalized perspective encompasses the standard Debreu-Farrell framework for technical efficiency evaluation, and makes it fully consistent with the well-established Koopmans efficiency notion.

Farm Technical Efficiency and Extension

This paper presents a methodology for estimating technical efficiency levels for individual farms using both a fixed effects panel model and a stochastic production frontier approach. It tests whether the estimated technical efficiency levels are associated with measures of contact with the advisory service. The approach is applied to a panel of 307 farms drawn from the Irish National Farm Survey over the period 1984 to 1994. The results show evidence that extension contact has had a positive impact on agricultural output.

The fixed effects estimator of technical efficiency

Firms and organizations, public or private, often operate on markets characterized by non-competitiveness. For example agricultural activities in the western world are heavily subsidized and electricity is supplied by firms with market power. In general it is probably more difficult to find firms that act on highly competitive markets, than firms that are not. To measure different types of inefficiencies, due to this lack of competitiveness, has been an ongoing issue, since at least the 1950s when several definitions of inefficiency was proposed and since the late 1970s as stochastic frontier analysis. In all three articles presented in this thesis the stochastic frontier analysis approach is considered. Furthermore, in all three articles focus is on technical inefficiency. The ways to estimate technical inefficiency, based on stochastic frontier models, are numerous. However, focus in this thesis is on fixed effects panel data estimators. This is mainly for two reasons. First, the fixed effects analysis does not demand explicit distributional assumptions of the inefficiency and the random error of the model. Secondly, the analysis does not require the random effects assumption of independence between the firm specific inefficiency and the inputs selected by the very same firm. These two properties are exclusive for fixed effects estimation, compared to other stochastic frontier estimators. There are of course flaws attached to fixed effects analysis as well, and the contribution of this thesis is to probe some of these flaws, and to propose improvements and tools to identify the worst case scenarios. For example the fixed effects estimator is seriously upward biased in some cases, i.e. inefficiency is overestimated. This could lead to false conclusions, like e.g. that subsidies in agriculture lead to severely inefficient farmers even if these farmers in reality are quite homogenous. In this thesis estimators to reduce bias as well as mean square error are proposed and statistical diagnostics are designed to identify worst case scenarios for the fixed effects estimator as well as for other estimators. The findings can serve as important tools for the applied researcher, to obtain better approximations of technical inefficiency

STOCHASTIC FRONTIER ANALYSIS OF NEW ZEALAND'S MANUFACTURING INDUSTRIES: SOME EMPIRICAL RESULTS

This paper examines the sources of total factor productivity growth (TFP) in New Zealand's manufacturing industries over the period 1978-98 and over various sub-periods. Examination of the data adopts two stages using a stochastic frontier approach. The first stage involves the specification and estimation of the stochastic frontier production function and the prediction of technical efficiency effects. The second stage involves the specification of a regression model for the predicted technical efficiency effects. The sources of TFP growth have been decomposed into four components; i.e. technical progress, changes in technical efficiency, scale effects, and change in allocative efficiency. The empirical results show that productivity has been largely due to changes in technical progress, technical efficiency and resource allocation effect. The changes in technical progress and resource allocation have improved in the post-reform period, i.e. 1984-98, while technical efficiency has declined in the post-reform period. With respect to scale effect its contribution to productivity growth is quite small.New Zealand Manufacturing Sector, Total Productivity Growth, Technical Progress, Technical Efficiency, Scale Components, Allocative Efficiency, Industrial Organization, D24, C23, O47,

Lumpy capital adjustment and technical efficiency

This paper investigates the impact of lumpy capital adjustment on productivity at the firm level using data on Japanese manufacturing industries. We estimate stochastic production frontiers, taking firm heterogeneity into account. We find that investment spikes are negatively related to technical efficiency. Furtermore, we find a negative relationship between machinery capital age and measured efficiency.

Technical Efficiency of Nigerian Insurance Companies

This paper uses data envelopment analysis (DEA) to evaluate the performance of Nigerian insurance companies, from 2001 to 2005, combining operational and financial variables. The paper also analyses the situations of these companies in relation to the frontier of best practices. In addition, it tests for the roles played by dimension, bank network and market share in the efficiency of the Nigerian insurance companies. The implications of this research for managerial purposes are then drawn.Nigerian insurance companies; Data Envelopment Analysis; Efficiency.

Technical Efficiency in Portuguese Dairy Farms

This paper addresses issues related to efficiency measurement from an empirical point of view. A stochastic frontier production model using a translog is estimated for a group of Portuguese dairy farms. Farm level survey data for the period 1988 -2005 is used. Stochastic Frontier estimates address behaviour across all periods so the empirical model allows for time varying efficiency as well as technical change. Previous empirical studies use either the value of production or the quantity of milk produced in a single output framework. The value of production approach bundles together decision regarding the quantity and quality components as well as CAP subsidies. Using both approaches for the same data set produce substantial differences in efficiency measurement. The analysis shows that while farmers are quite efficient maximizing quantity produced they are much less efficient when allowing for quality. Sensitivity of estimates to the heterogeneity of the panel data sample as well as to the specification of the dependent variable is discussed. Average efficiency is 84% which indicates a close proximity to the production frontier for 71% of dairy farms but one can not reject the hypothesis that efficiency is decreasing over time and the rate of technical change is negative and close to 2%.