Log-linear Production Function Research Articles

Implications of Farmer’s Adaptation Strategies to Climate Change in Agricultural Sector of Gujarat: Experience from Farm Level Data

This study examined the farmer’s perception on climate change and adaptation strategies to mitigate the adverse effect of climate change in the agricultural sector of Gujarat. It used farm level information of 400 farmers who were purposely selected from 8 districts. Thereupon, linear, non-linear and log-linear production function models were used to examine the impact of climate change, farmer’s adaptation strategies and technological change on agricultural production in Gujarat. The descriptive and empirical results specify that adaptation strategies (i.e., change in showing time of crops, mixed cropping pattern, irrigation facilities, application of green and organic fertilizer, hybrid varieties of seeds, dampening of seed before planting, climate tolerate crops, organic farming and technology) have a positive impact on agricultural production. Thus, farmer’s adaptation strategies are useful to mitigate the negative impact of climate change in the agricultural sector. Maximum temperature and minimum temperature, precipitation and rainfall have a negative impact on agricultural production. However, the impact of these factors seemed positive in the agricultural sector when farmers apply aforementioned adaptation strategies in cultivation. Family size, education level of farmers, annual income of farmers, arable land, irrigated area, cost of technology, appropriate technology and financial support from government have a positive contribution to increase agricultural production in Gujarat.

Resource use efficiency in sugarcane production in Konkan region (M.S.)

The functional analysis was carried out to know the contribution of independent variables in yield of sugarcane. From the estimated Cobb-Douglas production function (log linear production function), it was observed that, in suru sugarcane co-efficient of determination (R 2 ) was 0.9113 indicating 91 per cent of variation and in ratoon sugarcane co-efficient of determination (R 2 ) was 0.9344 indicating that, 93 per cent of the variation in the yield explained by identified input variables included in the function. The expenditure on manures, plant protection, potassium and nitrogen in suru sugarcane cultivation and plant protection and manures to be curtailed considering their excess utilization in ratoon sugarcane cultivation. Whereas, MVP to FC ratios was more than unity for phosphorus and nitrogen, human labour, irrigation indicated under utilization of these resources in sugarcane cultivation which underlines scope of expanding the use of these inputs.

Water storage for dry season vegetable farming as an adaptation to climate change in the upper east region of Ghana

The climate is changing in Ghana and farmers in the upper east region are adapting to the situation. The study analyzed determinants of farmers’ adaptive capacity in dry season vegetable farming using an Ordered Logit Model. The effects of adaptive capacities on farm income were also analyzed using log-linear production function. The empirical results reveal that sex, access to land near a reservoir site, engaging in off-farm business activity, access to credit and number of years of formal education determines farmers’ decision to venture into dry season vegetable farming. Besides, about 31.3% of farmers are of low adaptive capacity, 26.0% are of moderate adaptive capacity and 42.7% being high adaptive capacity. Further, adaptive capacity is explained by sex of respondent, number of years of formal education, household size, off-farm business activity and access to credit. Finally, the study also indicates that high adaptive capacity increases income from vegetable farming, and the results have implications for dry season food crop farming in developing countries. Key words: Dry season vegetable farming, climate change, adaptive capacity, ordered logit model, Ghana.

Probability distributions for carbon emissions and atmospheric response

Probability distributions for carbon burning, atmospheric CO2, and global average temperature are produced by time series calibration of models of utility optimization and carbon and heat balance using log-linear production functions. Population growth is used to calibrate a logistically evolving index of development that influences production efficiency. Energy production efficiency also includes a coefficient that decreases linearly with decreasing carbon intensity of energy production. This carbon intensity is a piecewise linear function of fossil carbon depletion. That function is calibrated against historical data and extrapolated by sampling a set of hypotheses about the impact on the carbon intensity of energy production of depleting fluid fossil fuel resources and increasing cumulative carbon emissions. Atmospheric carbon balance is determined by a first order differential equation with carbon use rates and cumulative carbon use as drivers. Atmospheric CO2 is a driver in a similar heat balance. Periodic corrections are included where required to make residuals between data and model results indistinguishable from independently and identically distributed normal distributions according to statistical tests on finite Fourier power spectrum amplitudes and nearest neighbor correlations. Asymptotic approach to a sustainable non-fossil energy production is followed for a global disaggregation into a tropical/developing and temperate/more-developed region. The increase in the uncertainty of global average temperature increases nearly quadratically with the increase in the temperature from the present through the next two centuries.

Cross-sectional aggregation of non-linear models

This paper considers the problem of cross-sectional aggregation when the underlying micro behavioural relations are characterized by general non-linear specifications. It focuses on forecasting the aggregates, and shows how an optimal aggregate model can be derived by minimizing the mean squared prediction errors conditional on the aggregate information. The paper also derives model selection criteria for distinguishing between aggregate and disaggregate models when the primary object of the analysis is forecasting the aggregates, and establishes the consistency of the model selection criteria in large samples. In the case of standard non-linear micro relations with additive errors it also provides suitable small sample corrections. For more general non-linear specifications we consider bootstrap techniques to correct for small sample bias of the proposed model selection criteria. Some of the ideas in the paper are illustrated using log-linear micro relations, often employed in applied research. The paper also contains an empirical application where log-linear production functions are estimated for the UK economy disaggregated by eight industrial sectors and at the aggregate level over the period 1954–1995.

Economy-Wide and Industry-Level Impact of Information Technology

Efforts by economists to measure the contribution to output growth of investment in information technology (IT) equipment have proven inconclusive, and studies of the productivity of IT workers are relatively scarce. In the present study, we attempt to fill this gap in our understanding of the impact of IT capital and labor in the production process. We use industry data to extend the previous studies in three ways: 1) by using more recent data (1983 to 1993); 2) by covering manufacturing and non-manufacturing industries, including services; and 3) by treating IT workers and non-IT workers as two distinct categories of labor. We adopt a log-linear production function approach, usually used in conjunction with company data, but apply the model to industries. We first estimate the model for the aggregate economy by pooling eleven cross-sections for 58 industries after establishing that elasticities are invariant over 1983 to 1993. We find that all the economy-wide elasticities are positive and lie between zero and one, consistent with a priori expectations. Also, the sum of the elasticities is about 0.9, implying decreasing returns to scale. In the time-series analysis, the results focus on each of the 58 industries separately. For two-thirds of these industries, the estimated elasticities are non-negative with respect to IT equipment; for ten of them, marginal returns to IT equipment are statistically significant and quite high, with nine exhibiting annual rates of return in excess of 100 percent. Not surprisingly, average returns to IT investment for the entire economy and most of the major industry sectors are not only lower, but also more plausible, a consequence of averaging the diverse results from component industries. We obtain similar results in the case of IT labor. Two-thirds of all industries show non-negative elasticities, with ten showing statistically significant positive coefficients. In five of these industries, both the IT capital and the IT labor coefficients are positive and significant. Though the marginal products derived from the estimated coefficients seem inexplicably high, especially for IT workers, they suggest that the returns to IT labor are quite large in some industries. Marginal products estimated for the entire economy and for major industrial sectors, such as manufacturing and non-manufacturing, however, fall within plausible ranges.

Alternative Approaches to the Estimation of Production Functions and of Technical Change

THE AGGREGATE PRODUCTION FUNCTION is one of the basic tools in the modern theory of economic growth. In recent years the increased interest in this subject has brought forth two types of improvement in the specification of the aggregate production function. First, the form of the production function has been noticeably extended from the constant elasticity of substitution functions (CES [1]) to variable elasticity of substitution functions, such as the linear elasticity of substitution function [17], the production function which combines CES and Cobb-Douglas functions [9] and [4], the production function in which the elasticity of derived demand is constant (CEDD) [14] and [15], etc. Secondly, the problems of specifying and measuring the types of technical progress that are consistent with a given form of the production function have been elucidated. For instance, in a theoretical paper [16] the present authors have studied extensively the implications of various types of neutral technical progress that result from the invariant relationships between pairs of important economic variables. Subsequently we have estimated production functions and technical progress on the basis of linear and invariant relationships between pairs of these variables [3]. The purpose of the present paper is to extend the scope of the above mentioned papers. We proceed by dropping the two-variable restriction and allowing general multi-variate analysis among subsets of production variables. In particular, alternative specifications of production functions and of technical progress will be obtained by specifying linear relationships between rates of change of such production variables as labor-capital ratio, output-capital ratio, wage rate, interest rate, marginal rate of substitution, and profit share. A general differential equation is derived from the linear combination of all these variables. Its solution yields a new class of production functions which includes the CES, Cobb-Douglas and their combination as special cases and for which technical change is factor augmenting. This new class of production functions may appropriately be called the log-linear production functions. The model is tested against time series data for two digit industries in the United States 1949-65. The question asked is this: what production functions and what types of technical progress are we testing against each other when we