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The purpose of this paper is to reveal the dynamics of the Greek energy sector. As energy sectors contribute substantially to a national economy and stimulate national output and employment, it is important to identify their upward and downward linkages and interrelations with the other sectors of the economy.

To do this and capture such relations in the economy, a general equilibrium model is used. In specific, input–output (I–O) analysis is used and a model is specifically built for the Greek economy to examine in detail the energy sectors. Multiplier and linkage analysis is performed to assess their dynamics in terms of output, household income and employment.

Results indicate that the three energy sectors’ multipliers and elasticities, though are not ranking in the first places, are enough high indicating their strong linkages in the economy and their potentials to enhance the economy’s total output, employment and household income.

Further disaggregation of the economy’s energy sectors is needed to make clearer the separation among renewable and non-renewable sector, to identify and compare the dynamics and contribution of each category in the economy. Additionally, an environmental I–O model would indicate consequences on the environment and not just pure economic benefits.

Through the analysis, it can be seen that energy sectors and secondary energy products have the ability to drive a country’s economic activity through exports and intersectoral linkages, even if it is not a crude petroleum producing economy. Thus, knowledge of the economic impacts of such sectors is a valuable information.

The current study provides significant information of an economy’s energy sectors regarding their ability to support economic activity and employment. A general equilibrium model is used, examining the whole economy, to assess direct and indirect interrelationships.

This chapter examines the main methodological issues involved in the comprehension of the cost structure of the airport industry and suggests considerations for future airport cost analyses. Such understanding has become a crucial concern for policy makers, regional planners, and managers in order to deal with optimal market design (e.g., regulation and market configuration) and airport strategies (e.g., pricing, investments, and alliances). An in-depth analysis of the economics of cost functions is presented, together with a description of the relevant multi-output cost economies measures (average incremental costs, scale and scope economies, and cost complementarities). We also discuss the assumptions underlying estimates of total versus variable cost functions and the importance of estimating a sufficiently flexible functional form. Moreover, we provide a critical survey of the international empirical literature on the cost structure of the airport industry, which highlights how econometric estimates strongly depend on the sample choice and the empirical model considered. Indeed, while econometric studies on international samples based on long-run cost function estimates show that long-run scale economies are never exhausted, single country studies mostly estimate variable cost functions and find lower values for scale economies at median sample points that tend to decrease with size. We discuss why we believe that studies based on the estimation of short-run variable cost functions offer more reliable results, given the reasonable assumption of airport overcapitalization in the short run. We conclude our work by noting that underlying policy issues related to planning and regulation, as well as to the optimal market structure of the airport sector, need to take into account the role played by vertical relationships between airports and airlines.

Academic research in the USA and more recently in the UK and Sweden, has highlighted public capital as a significant growth determinant. Public capital, it is argued, has a positive effect on private sector output, productivity and capital formation. However, controversy surrounds the empirical results emerging from this literature. Much of the controversy rests on research methods employed. Adds to this body of literature in two ways. First, estimates aggregate production functions for private sector output using Irish data. The stock of public capital is included as an input to investigate the effects of government investment on private sector productivity. Second, uses modern time‐series techniques to test the hypothesis. Employs the Johansen (1988) cointegration testing procedure and error correction modelling on annual data for the period 1958‐1990. These modern techniques produce empirical results which do not support the public capital hypothesis. Suggests several reasons to explain this outcome, and outlines possible policy implications.

The purpose of this paper is to investigate Chinese farmers’ adaptation behavior in the context of the rising cost of labor in agriculture. As the cost of labor increases, farmers will either reallocate their budget to different inputs or change the structure of agricultural production to maximize profit.

The Rural Fixed Point Observation data set between 2004 and 2010 is employed in the empirical analysis of this study. Both the compensated and uncompensated demand elasticities with respect to wages are estimated by adopting the translog cost function and the profit function.

The results show that labor input will drop down significantly as a response to rising wages. Land, fertilizer and intermediate inputs are net complements of labor, whereas machinery appears to be net substitute for labor. In addition, the authors also separate the expansion effect from the substitution effect and find that farmers will shift to grain production with intensive use of fertilizer and from wheat and corn to rice as a response to the rising cost of labor.

This study adopts the classical household model to incorporate various adaptation behaviors of farmers into one framework and decomposes the total effect of the rising cost of labor on input demand into an expansion effect and a substitution effect, which provides a better understanding of farmers’ adaptation behavior.

The purpose of this paper is to investigate the productivity of rice production by decomposing the growth of total factor productivity (TFP) into four components: technological change, scale effects, technical and allocative efficiencies.

This study employed an econometric approach to decompose TFP growth into four components: technological change, technical efficiency, allocative efficiency and scale effect. Unbalanced panel data used in this study were surveyed in 1994, 2004 and 2014 from 360 rice farming operations. The model used the stochastic frontier transcendental logarithm production technology to estimate the technology parameters.

The results indicate that the primary sources of TFP growth were technological change and allocative efficiency effects. The contribution of technical efficiency was low because it grew sluggishly.

This study has several shortcomings, such as very low R² and the insignificant elasticity of labour presented in the findings. Another limitation is the limited time period panel covering long interval, which resulted in unbalanced data.

The government should improve productivity growth by allocating more areas for rice production, which enhances the scale and efficiency effects and adjusting the use of capital and material inputs. Extension services should be strengthened to provide farmers with training on improved agronomic technologies. This action will enhance technical efficiency performance and lead to technological progress.

As Indonesian population is still growing at a significant rate and the fact that rice is the primary staple food for Indonesian people, the productivity of rice production should increase continually to ensure social security at a national level.

The productivity growth is decomposed into four components using the transcendental logarithm production technology based on farm-level data. The measure has not been conducted previously in Indonesia, even in rice-producing countries.

This paper deals with estimation of risk and the risk preference function when producers face uncertainties in production (usually labeled as production risk) and output price. These uncertainties are modeled in the context of production theory where the objective of the producers is to maximize expected utility of normalized anticipated profit. Models are proposed to estimate risk preference of individual producers under (i) only production risk, (ii) only price risk, (iii) both production and price risks, (iv) production risk with technical inefficiency, (v) price risk with technical inefficiency, and (vi) both production and price risks with technical inefficiency. We discuss estimation of the production function, the output risk function, and the risk preference functions in some of these cases. Norwegian salmon farming data is used for an empirical application of some of the proposed models. We find that salmon farmers are, in general, risk averse. Labor is found to be risk decreasing while capital and feed are found to be risk increasing.

The purpose of this paper is to provide an empirical evaluation of the impact of infrastructure development on industry‐level productivity, output and efficiency in India over the period 1994‐2006.

The first stage, estimated total factor productivity (TFP) and technical efficiency of eight important industries. In the next stage, the effects of infrastructure were estimated on TFP, output, labor productivity and technical efficiency. Fully modified ordinary least squares procedure was utilized to generate consistent estimates of the relevant panel variables in the cointegrated frameworks.

The results of this study are mixed. On the one hand, TFP, output and technical efficiency appear to be positively and largely affected by infrastructure. On the other hand, the effect of infrastructure on the labor productivity is somewhat negligible. In addition, the effects of information and communication technology on the industrial performance are found to be very weak.

This is the first study of its kind in the related literature which attempts to investigate the role of infrastructure in industrial performance, using alternative frameworks, namely, growth accounting and production function approach. The paper uses appropriate techniques to account for the potential endogeneity of regressors as well as for multicollinearity among infrastructure variables.