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,

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,

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

Deregulation and Productivity: The Case of Spanish Banks

This paper deals with measuring total factor productivity (TFP) growth of financial institutions incorporating different types of deregulatory measures. TFP growth is decomposed into external, scale, and markup (in output prices) components. The contribution of the external component is further dissected into several types of deregulation and technical change components. We include the TFP growth relationship as an additional equation in estimating the cost system. The empirical model uses panel data on Spanish banks (savings and commercial), primarily because the Spanish banking sector went through rapid deregulatory changes. We find that deregulations, in general, contributed positively to TFP growth for both savings and commercial banks. Furthermore, domestic (European) deregulations had a greater effect on TFP growth of savings (commercial) banks.Total factor productivity, markup, deregulation, and technical change.

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

Technical change and total factor productivity growth: the case of Chinese provinces

In the literature technical change is mostly assumed to be exogenous and specified as a function of time. However, some exogenous external factors other than time can also affect technical change. In this paper we model technical change via time trend (purely external non-economic) as well as other exogenous (external economic) factors (technology shifters). We define technology index based on the external economic factors which are indicators of 'technology'. Thus our definition of production function is amended to accommodate several technology shifters which are not separable from the traditional inputs. That is, these technology shifters allow for non-neutral shift in the production function. In doing so we are able to decompose technical change (a component of TFP change) into two parts. One part is driven by time (manna from heaven) and the other part is related to producer specific external economic factors. These exogenous technology shifters are aggregated (via hedonic aggregator functions) into several groups (technology indices) for parsimonious parametric specification. The empirical model uses panel data on Chinese provinces. We identify a number of key technology shifters and their effect on technical change and TFP growth of provinces

The effect of environmental cross compliance regulations on Swiss farm productivity

This paper analyzes the evolution of Swiss farm productivity during the implementation of environmental policy reforms. We employ a production model formulation with technology parameters defined as the functions of subsidies, as well as individual farm characteristics. Our estimates for two groups of farms – milk-producing and crop farms – show that introducing environmental regulations induced serious changes in the production technology and productivity of inputs, especially of land, labor and fertilizer. The overall effect of the subsidies on the production output has been found negative. At the same time, we find that farms do not use their resources optimally, which indicates some deficiencies in structural adjustments, primarily in the land and labor markets.environmental regulations, productivity analysis, Swiss agriculture., Environmental Economics and Policy, Q120, D240,

Measuring productivity differentials – An application to milk production in Nordic countries

The aim of this paper is to analyse the regional productivity differentials on dairy farms in Denmark, Finland and Sweden. Several methods have been suggested for analysing productivity differentials in agriculture between groups of farms or countries. Hayami [5] and Hayami and Ruttan [7] suggested the meta-production function approach. This idea has been further developed by Lau and Yotopoulos [9] and Fulginity and Perrin [13]. Battese and Rao [2] suggested the meta-frontier analysis for these comparisons. One of the advantages of meta-frontiers with respect to metaproduction functions is that they are able to separate technological differences from the differences in technical efficiency. Battese et al. [5] and O’Donnell et al. [16] have extended this idea and developed both parametric and nonparametric approaches. In this paper, we extend the metafrontier analysis to the concave nonparametric least squares estimation of the production function suggested by Kuosmanen [18,19]. In addition, we compare the results with the approach where the estimation of meta-frontier can be avoided. The reference can also be the maximum output providing technology that is the one that yields the maximum estimated output, given inputs [21]. In this case the estimation can be based either on average or frontier production functions. The farm level data is obtained from the EU’s Farm Accountancy Data Network data set for Denmark, Finland and Sweden. They cover 954 dairy farms in 2003. The results suggest that different method provide slightly different results but in all approaches productivity differentials are considerable in favour of Danish farms. In addition, the Danish technology is not only dominating at the mean but also at most of the data points.productivity, technical efficiency, meta-frontier, Productivity Analysis,

Financial sector development and productivity growth

Recent years have witnessed important structural changes around the world as a result of the globalization process, the creation of new economic blocks and the liberalization of financial sector in many countries. Responding to these changes many sectors of the industrialized countries have gone through major deregulatory changes to acclimate themselves to new environments. At the same time, many countries have undertaken institutional reforms to build a market-orientated financial system in the hope that transition towards market economy will improve productivity. In the face of uncertainty resulting from changes in regulatory structure and the development of financial institutions to foster market economy, many countries may not be able to achieve their maximum growth potential. In other words, productivity growth is likely to depend on the development of financial institutions and the stage of economic development. That is, a less developed country is likely to benefit more (in terms of output growth rate) from the development of financial institutions than a developed economy with well-developed financial system. In this paper we document this by using data covering 65 countries, varying substantially in terms of level of development and geographic location, and spanning the period 1960-1999. Empirical results obtained from the estimation of two different empirical models regarding the measurement of total factor productivity growth seem to confirm a priori expectations about the overall positive influence of financial systems on productivity in line with previous work on this front. Our results remain robust with respect to alternative definitions of financial sector development we tried