Input Demand Functions Research Articles

The exposure-response of air pollution and climate change to chronic respiratory diseases: Does residential energy efficiency matter?

Promoting energy efficiency is crucial for reducing energy consumption, yet its impact on human health remains discussed. This study examines the relationship between household energy efficiency, ambient air pollution, climate change, and mortality risk from chronic respiratory diseases. The study collected observational data in six major cities in Taiwan from 2008 to 2020. The energy efficiency level was determined using the input demand function derived from the stochastic frontier analysis (SFA). Subsequently, analysis was conducted employing a dynamic panel data model and a pooled mean group estimator. The study's findings indicate that enhancing household energy efficiency decreases the mortality rate associated with chronic respiratory diseases. Specifically, at the highest level of energy efficiency (99%), the relative risk reaches its lowest value of 0.639 (95% CI: 0.58–0.70). Additionally, a positive exposure-response relationship is observed between degree days and ambient air pollution (PM2.5, PM10, and SO2), associated with an increased risk of death from chronic respiratory diseases. These results underscore the importance of initiatives to enhance energy efficiency programs in households, particularly in metropolitan areas.

Farm‐level responses to weather trends: A structural model

AbstractAssessing the effects of weather and climate on agricultural production is crucial for designing policies related to climate change adaptation and mitigation. A large body of literature has identified the detrimental effects of climate change on crop yields worldwide, and farm‐level adaptation has been shown to mitigate the adverse effects on agricultural production. In this study, we employ a structural model to examine farm production responses to ongoing weather trends. We investigate how farmers adjust output and input decisions by estimating a system of output supply and input demand functions, controlling for nonrandom crop selection. Using panel data with 14,796 observations reflecting 1638 German crop farms (1996–2019), we find that both the expected and realized weather determine farmers' production decisions. In the event of a drought, the supply of most considered crops and the demand for fertilizer decrease. The drought shock has also lasting effects on farmers' production decisions, with a reduced supply of protein crops and an increased level of root crops production in subsequent years. These findings highlight the need to account for farm‐level production responses when assessing weather and climate impacts.

Technical progress and induced innovation in China: a variable profit function approach

We propose a new methodology to estimate empirically the input price-induced technical change and total factor productivity (TFP) growth in China. Our primary goal is to test Hicks’ induced innovation hypothesis by examining whether technical change in China has been induced by sharp increase in input prices that have accompanied its rapid economic growth. Utilizing the idea of a firm’s two-stage optimization problem, we develop a new parametric form of the variable profit function wherein the derived input demand and output supply functions can be easily constrained to be regular, and the functional structure is parsimonious in the number of parameters. Applying this methodology to Chinese time series data for 1986–2017, we find that not only is wage-induced innovation significant and quantitatively important, but also that it substantially buffered a decline in TFP growth before 2006 that would otherwise be substantial. Overall, we conclude that China’s economic growth is predominantly driven by wage-induced innovation along with massive injection of heavily subsidized physical inputs in public works and huge investment in industrial sectors.

Input Demand and Output Supply Elasticities of Coarse Cereals in India: A Translog Profit and Sure Approach

Aims: The aim was to estimate the output supply and input demand elasticities of maize, jowar and bajra production, using the restricted normalised translog profit function, for the major producing states of India (Andhra Pradesh, Maharashtra and Rajasthan). 
 Study Design: A stratified multi-stage random sampling design was adopted for carrying out the sampling.
 Place and Duration of Study: The study pertains to cross sectional plot level data for the period 2013-14 and 2017-18. The study is based on secondary data, collected from Directorate of Economics & Statistics, Ministry of Agriculture, Government of India. 
 Methodology: For the present studied crops (maize, jowar and bajra), those states were selected which covered maximum area, i.e, 85% of the total area under the cultivation. Socio-economic data of farmers such as age, sex, level of education, occupation, size of landholding were collected. The translog profit function approach was used as the econometric technique to estimate output supply, and input demand functions. Labour, fertiliser and seeds are taken as variable inputs. Statistical software STATA version 16 was used for the analysis.
 Results: The results suggest that the changes in market prices of inputs and output significantly affect the farmers’ profits, crop produce supply and the use of resources in the cultivation of these crops. The supply elasticities of maize, jowar and bajra with respect to its own prices are positive and statistically significant indicating that increase in support prices can boost the supply of these nutri-grains and farmers profits. Labour demand for these crops in the country is elastic and significant to its own price.
 Conclusion: During both the periods, 2013-14 (typical monsoon year) and 2017-18 (drought year), the elasticities derived are statistically robust as almost all of them carried compatible signs and in line with the theory. Promoting these crops can contribute to labour absorption.

Rebound effect with energy efficiency determinants: a two-stage analysis of residential electricity consumption in Indonesia

This study aims to estimate the economy-wide rebound effect using the determinants of household energy demand in Indonesia. Identifying the size of the rebound effect is essential for the government's energy efficiency and carbon emission reduction programs. The estimation of the rebound effect uses a two-stage analysis with panel data of every province in Indonesia from 2002 to 2018. We employ the Input Demand Function of the Stochastic Frontier Analysis to measure the energy efficiency of residential aggregate in Indonesia. In the second stage, we adopt the dynamic panel data model to estimate the economy-wide rebound effect. The estimated dynamic panel data model reveals that the magnitudes of the short-run and long-run rebound effects were 87.2% and -45.5%, respectively. In other words, a 1% increase in household energy efficiency results in a reduction in energy consumption of 0.13% in the short term and 1.45% in the long term. Our research also discovers that a backfire rebound effect exists in provinces with high energy efficiency. Therefore, we prove to backfire claims that improving energy efficiency will increase energy use. Henceforth, energy efficiency programs in the household sector still need to be implemented, followed by increasing technological innovation and improving housing policy.

Impacts of energy intensity target constraint on elasticity of substitution between production factors in China

China has implemented the energy intensity target (EIT) constraint policy to improve its energy efficiency for more than three decades. Producers in China need to consider factor prices, outputs, and EIT constraint while they plan the number of input factors. Therefore, this article brings the EIT into the conditional input demand function of an input factor and assesses the impacts of the elasticity of substitution between different production factors. By building two-factor substitution elasticity models with and without EIT constraints, this paper examines the impacts of EIT constraint on the elasticity of substitution between input factors in both the fossil fuel production sector and the non-fossil-fuel production sector. The main conclusions are, firstly, EIT constraint influences both own-price elasticity of an input factor and cross-price elasticity between different input factors. Secondly, EIT constraint hinders the responses of some input factors to the price changes of other input factors, and changes relationships between some input factors from complementary to substitute, or vice versa. Two policy implications are obtained. First, producers should consider the impacts of EIT constraint on their investment, labor input, energy input, and raw materials purchase and bring these impacts into their business strategies. Second, reducing energy input by changing prices of other production factors will be ineffective under EIT constraint.

Estimation of costs of technical and allocative inefficiency

If a firm is both technically and allocatively inefficient its cost will increase. Since allocative inefficiency results from misallocation of inputs, cost of allocative inefficiency (CAI) can be obtained if the input over(under) use can be analytically derived. This is only possible for production functions for which input demand functions can be explicitly derived. Schmidt and Lovell (1979) addressed this problem using a Cobb-Douglas production function with the cost minimizing behavior. In this paper, we propose a mixture of Cobb-Douglas (CD) production functions to get a more flexible system that allows obtaining costs of technical and allocative inefficiency analytically. Inflexibility of the CD function is addressed by making its parameters (input elasticities) functions of environmental/predetermined variables. Our empirical evidence show considerable heterogeneity in parameters and multimodal distributions of many quantities of interest, which support our formulation.

METROPOLITAN EVIDENCE REGARDING SMALL COMMERCIAL AND INDUSTRIAL ELECTRICITY CONSUMPTION

Small commercial and industrial (CIS) electricity demand is an important category of electric energy consumption. Historically, it has received substantially less research attention than residential usage, potentially due to data constraints. This study seeks to partially fill that gap in the energy economics literature by employing a fairly unique data set for the El Paso, Texas, USA metropolitan economy that includes private capital stock estimates from 1978 through2017. The empirical model is specified using a recently developed analytical framework based on duality theory and a derived input demand function. Parameter estimation is completed using an Autoregressive-Distributed Lag model (ARDL) and an Error Correction Model (ECM). In the long-run, CIS customers in El Paso respond only to own-price and the quantity of capital stock per capita. In the short-run, CIS customers adjust their electricity usage in response to changes in all variables except for the price of electricity. The most unexpected result from this analysis is a short-run income elasticity of -0.32, indicating that CIS electricity usage decreases with economic expansion in El Paso, Texas.Keywords: Electricity usage, Metropolitan economic growth, Small commercial and industrial customers, Capital stocks, Duality theory, Derived input demandJEL Classifications: Q41; R11; M21DOI: https://doi.org/10.32479/ijeep.8233

The Impact of Government Expenditure on Agriculture Marketing and Supply Chain Management: An Application of Two-Stage Least Approaches

The purpose of this quantitative study is to investigate the role of government expenditure on food security and its model based on the Input Demand Function (IDF). This study uses secondary data collected from the Bureau of Statistics Centre of Indonesia, which consists of 23 regencies/municipalities in Aceh Province for the 2007-2016 period. The data is analysed using Two-Stage Least Square (2SLS). The analysis shows that food security in terms of availability, accessibility, and food utilisation was influenced by the farmers’ decisions regarding production inputs. In order to increase food availability, the government is partially responsible for the costs incurred by farmers for producing food. Furthermore, to raise food accessibility, utilisation, and farmers’ income, food prices need to be regulated such as the price of grain for farmers or the price of rice for producers. To conclude, the government needs to buffer the stock board and ensure controls are in place to stabilise food prices.

The impact of conservation tillage on maize yield and input demand: the case of smallholder farmers in north‐west Ethiopia

This study analyses the economics of conservation tillage (CT) with respect to its effect on maize yield and chemical fertiliser, herbicide, and female and male labour demand. We estimate production and input demand functions using seemingly unrelated regressions on plot‐level cross‐sectional farm household data collected in the north‐west of Ethiopia. A two‐step control function is applied to address potential endogeneity bias due to the inclusion of the CT adoption decision as an explanatory variable. Our results show that CT increases maize yield and chemical fertiliser demand. Additionally, the results show that the adoption of CT reduces female and male labour required for crop production. However, this is achieved through the increased use of herbicides, which might have an undesirable health and environmental effects.

How do farmers manage crop biodiversity? A dynamic acreage model with productive feedback

Previous studies on the productive value of biodiversity have emphasised that crop diversity increases crop yields. Here, we focus on the management of crop biodiversity for wheat, winter barley and rapeseed productions. We introduce productive capacity of biodiversity into a structural dynamic model with supply, variable input demand and acreage functions. We estimate the model for a sample of French farms between 2007 and 2012. We highlight that biodiversity indicators influence the yield of crops and variable input uses. We find evidence that farmers manage their acreage to benefit from the productive capacity of crop biodiversity.

Land Measurement Bias: Comparisons from Global Positioning System, Self-Reports, and Satellite Data

Agricultural statistics derived from remote sensing data have been used primarily to compare land use information and changes over time. Nonclassical measurement error from farmer self-reports has been well documented in the survey design literature primarily in comparison to plots measured using Global Positioning System (GPS). In this paper, we investigate the reliability of remotely sensed satellite data on nonrandom measurement error and on agricultural relationships such as the inverse land size–productivity relationship and input demand functions. In our comparison of four Asian countries, we find significant differences between GPS and remotely sensed data only in Viet Nam, where plot sizes are small relative to the other countries. The magnitude of farmers’ self-reporting bias relative to GPS measures is nonlinear and varies across countries, with the largest magnitude of self-reporting bias of 130% of a standard deviation (2.2-hectare bias) in the Lao People’s Democratic Republic relative to Viet Nam, which has 13.3% of a standard deviation (.008-hectare bias). In all countries except Viet Nam, the inverse land size–productivity relationship is upwardly biased for lower land area self-reported measures relative to GPS measures. In Viet Nam, the intensive margin of organic fertilizer use is negatively biased by self-reported measurement error by 30.4 percentage points. As remotely sensed data becomes publicly available, it may become a less expensive alternative to link to survey data than rely on GPS measurement.

What Determines the Adoption of Technologies for Renewable Energy Generation Across Countries?

I provide evidence about the effects of induced biased technical change as a main determinant of solar and wind energies adoption. This study presents an empirical framework based on an input demand function for electricity production. Implementing this model helps us to understand what is driving the decision of electricity generators to adopt solar and wind energies. The data used for this study was a cross country panel dataset on 29 OECD countries between 1990 and 2015. I test three mechanisms that may be driving the adoption of these technologies: 1) a pure substitution effect between renewable energies and fuel; 2) induced technological change and innovation; 3) fuel taxes. The results show that induced technical change is the main driver to increase the rate of adoption of solar and wind energies.

The impact of climate on farm inputs in developing countries agriculture

The success of the green revolution has prompted some analysts to suggest it can be extended more broadly to all poor farmers. This paper argues that suitable natural endowments are an important precondition for high input farming. Examining production functions across China, we find that outcomes are very climate sensitive. It follows that we also find that input demand functions are climate sensitive. Efforts to intensify farming in undeveloped regions should focus on places with suitable soils and especially climate. The results also suggest that farmers will partially adapt to climate change by altering their input intensity.

Modelling Supply Response and Investment in Agriculture in Developing Countries - A View from the Cloister

Modeling aggregate supply response of agriculture in developing economies may be effectively pursued both at micro and meso-levels in a profit maximizing framework, choice of technique and household decision theoretical system of neo-classical tradition. The system of equations consisting of aggregate output supply, input demand, and private investment functions can be estimated taking into account of imperfections in land, labor, capital and public infrastructure and market access across ago-ecosystems and production systems.

Testing competing destinations gravity models – evidence from BRIC International

ABSTRACTThis paper derives and compares two competing-destinations formulation of the gravity model applied to trade in intermediates. We build a probabilistic input demand function from entropy maximization in which firms may purchase some of their inputs from other firms paying the required transport costs and accounting for the spatial structure of trading partners in a geographical system. In the second model, firms trade inputs to reduce the overall cost of production and intermediate sales are encouraged by low distance costs and low competition from alternative input sources. Even if the gravity equations look similar, we show that their underlying structures are different, and that the type of gravity equation has significant implications for the estimation technique adopted.

On the nature and magnitude of cost economies in hog production

AbstractIn this article, we assess the impact of farm size on production cost and evaluate the marginal costs and margins by considering that input prices may change with the scale of production. By using French hog farm data, we estimate a system of equations including a feed price function, input demand functions, and an output supply function based on a technology approximated by a combined generalized Leontief‐Quadratic form. Our results suggest that the marginal costs are over‐estimated when the adjustment of the feed unit prices to a change in farm size is not controlled for. More specifically, the cost economies for large farms (enjoying the highest profits) arise primarily from lower feed prices, with technological scale economies having little impact. In contrast, farms with no hired labor exhibit technological scale economies and reach higher price‐cost margins compared to larger farms.

Land Measurement Bias and Its Empirical Implications: Evidence from a Validation Exercise

This paper investigates how land size measurements vary across three common land measurement methods (farmer estimated, Global Positioning System (GPS), and compass and rope), and the effect of land size measurement error on the inverse farm size relationship and input demand functions. The analysis utilizes plot-level data from the second wave of the Nigeria General Household Survey Panel, as well as a supplementary land validation survey covering a subsample of General Household Survey Panel plots. Using this data, both GPS and self-reported farmer estimates can be compared with the gold standard compass and rope measurements on the same plots. The findings indicate that GPS measurements are more reliable than farmer estimates, where self-reported measurement bias leads to over-reporting land sizes of small plots and under-reporting of large plots. The error observed across land measurement methods is nonlinear and results in biased estimates of the inverse land size relationship. Input demand functions that rely on self-reported land measures significantly underestimate the effect of land on input utilization, including fertilizer and household labor.

Off‐farm Employment and Input Intensification among Smallholder Maize Farmers in Kenya

AbstractWe derive input demand functions for fertiliser and hybrid seed, testing for the combined and separate effects of income from non‐farm sources and agricultural wage labour among smallholder maize farmers in Kenya. More income from off‐farm sources, and specifically non‐farm sources, competes with maize intensification, particularly in more productive areas where use rates are higher. In less productive areas, where households rely more on off‐farm income and input use in maize is extremely low, agricultural wage labour reduces the likelihood that fertiliser is applied, but when used, has a positive effect on quantities purchased of both seed and fertiliser.

On the Joint Estimation of Heterogeneous Technologies, Technical, and Allocative Inefficiency

In this article, we provide a semiparametric approach to the joint measurement of technical and allocative inefficiency in a way that the internal consistency of the specification of allocative errors in the objective function (e.g., cost function) and the derivative equations (e.g., share or input demand functions) is assured. We start from the Cobb–Douglas production and shadow cost system. We show that the shadow cost system has a closed-form likelihood function contrary to what was previously thought. In turn, we use the method of local maximum likelihood applied to a system of equations to obtain firm-specific parameter estimates (which reveal heterogeneity in production) as well as measures of technical and allocative inefficiency and its cost. We illustrate its practical application using data on U.S. electric utilities.