Search results

In order to improve access to resources for smallholder farmers, efforts are being made to promote contract farming in Ghana. This is seen as a strategy to increase agricultural productivity of farmers, give better market access and guarantee adequate supply of raw materials to agro-based industries. However, the challenge is whether contract farming leads to improvement in food security status of farmers. The study therefore seeks to explore to what extent farmers' food security status is influenced by their participation in contract farming activities.

Using Cragg's double-hurdle model to analyse participation in contract farming, the authors control for selection bias using propensity score matching applied to a data set of 336 observations to examine the impact of contract farming on the food security levels of rice farmers in Ghana.

The results of this study show that yield of paddy and the wealth of the farmer are the main factors that influence the quantity of paddy rice to be contracted in contract farming arrangements. This study also finds that participation in contract farming will increase food security by 109%. In conclusion, contract farming has a significant positive impact on the farmers' food security status.

Agricultural policies and rural development initiatives supporting the promotion and expansion of contract farming should be pursued to persuade more farmers to produce under contract farming agreements.

Grocery retailers have access to detailed data on consumer purchases within their own chains. Previous research has used across-chain scanner panel data to develop optimal price cuts targeted to individual households but whether such a targeting strategy will work with only within-chain data is unknown. The purpose of this research is to address this specific question.

The authors use scanner panel data from multiple categories to create across-chain and within-chain purchase histories for the same consumers. They then estimate models of purchase decisions on the two datasets and compare their performance.

Within-chain data fares significantly worse on both fit and prediction criteria. Retailers' upside to customizing is minimal compared to those reported for manufacturers. Finally, customized prices based on the within-chain model significantly underperform the promise of across-chain data.

Store choice is not modelled. Research also needs to be replicated in other contexts. The authors conclude that limited purchase histories may not yield accurate enough estimates of marketing mix responsiveness, and that across-chain purchase histories are essential for effective targeted price cuts.

Loyalty card data may be useful for other purposes, like experimenting with segment-specific discounts, but its value in customizing prices at individual level is limited without adding other sources of information.

Previous research on price customization has been based almost exclusively on across-store data. However, retailers only have access to their own chain-specific data. This is the first study to comprehensively compare price customization based on within- and across-chain purchase data and show that the upside potential for price customization based on the former information set is quite limited.

Branching is one of the crucial strategic non-price actions for banks. Previous studies on the impact of state ownership upon banks focus on bank lending behavior. This paper aims to offer a novel investigation of how state ownership affects bank branching behavior by examining state-controlled commercial banks (SCCBs) in the context of the largest developing and transitional country China.

The two-part model (TPM) is applied to analyze the branching decision process. In the first stage, the dependent variable is the choice of bank branching dynamics and in the second stage the dependent variable is the number of new branches or the number of closed branches. For robustness check, the ordered probit selection model allowing for interdependence of the two stage decisions is also employed.

Using a unique dataset of bank branches in China, this paper finds that the branching decisions of Chinese SCCBs are driven by both profit motivated factors including population size, population density, income level, financial development and banking competition and politically motivated factors as represented with the proportion of SOEs. As a comparison, branching decisions of joint-stock banks in China are fully determined by profit motivated factors.

First, this study is the first to explore the effect of state ownership on bank branching decisions, providing a new insight on the literature regarding to the impact of state ownership on bank decisions. Second, this study explores the potential effect of politically motivated factors on bank branching decisions, filling the gap in bank branching literature. Third, this study can contribute to bank branching literature by enriching the limited understanding of how SCCBs make branching decisions. Lastly, this study applies novel empirical strategies to analyze bank branching decisions, including the TPM and the ordered probit selection model.

While some studies have found that cognitive biases are detrimental to entrepreneurial performance, others have conjectured that cognitive biases may stimulate entrepreneurial action. This study uses a typology of availability and representative heuristics to examine how two patterns of biases affect entrepreneurial performance. Drawing on ideas from cognitive science, this study predicts that various levels of biases in each pattern stimulate entrepreneurial behavior and performance.

A profile-deviation approach was employed to analyze data from 253 entrepreneurs and zero-truncated Poisson regression and the zero-truncated negative binomial regression to test hypotheses.

This study finds some positive associations between a particular level of cognitive biases in each of the two patterns and entrepreneurial behavior and performance. Results show that the patterns of biases often stimulate and never hurt entrepreneurial behavior and performance. The opposite holds for a lack of cognitive biases, which hurts and never stimulates entrepreneurial behavior and performance.

This study examines patterns of cognitive biases of entrepreneurs instead of single biases. The study broadens the perspective on the heuristics and cognitive biases of entrepreneurs by examining patterns of biases emanating from the availability and the representativeness heuristic that make a difference for entrepreneurial behavior and performance. The study also brings the “great rationality debate” closer to the entrepreneurship field by showing that a normative rule based on statistics and probability theory does not benefit entrepreneurial behavior and performance.

The purpose of this paper is to investigate the determinants of pork consumption in urban western China and the different consumption patterns across income strata with respect to income elasticity and price elasticity of demand.

The double-hurdle model is fit to the household data of Sichuan and Xinjiang provinces which is from the National Bureau of Statistics urban household surveys.

The paper finds that consumers’ purchasing decisions regarding pork are related to both non-economic and economic factors. The results also indicate large differences among the determinants for decision of how much pork to buy across the three income strata. Low-income households have higher income elasticity than middle-income and high-income households. High-income and middle-income households’ level of pork consumption is more sensitive to pork price. High-income households have greater cross-price elasticity.

In the previous studies, the non-economic determinants of pork consumption have not been addressed, and neither does the issue of difference pork purchasing behavior across income strata for urban households in western China. So this study uses the double-hurdle model to investigate the determinants of pork consumption in urban western China.

Modeling of material behavior by physically or microstructure-based models helps in understanding the relationships between its properties and microstructure. However, the majority of the numerical investigations on the prediction of the deformation behavior of AA2024 alloy are limited to the use of phenomenological or empirical constitutive models, which fail to take into account the actual microscopic-level mechanisms (i.e. crystallographic slip) causing plastic deformation. In order to achieve accurate predictions, the microstructure-based constitutive models involving the underlying physical deformation mechanisms are more reliable. Therefore, the aim of this work is to predict the mechanical response of AA2024-T3 alloy subjected to uniaxial tension at different strain rates, using a dislocation density-based crystal plasticity model in conjunction with computational homogenization.

A dislocation density-based crystal plasticity (CP) model along with computational homogenization is presented here for predicting the mechanical behavior of aluminium alloy AA2024-T3 under uniaxial tension at different strain rates. A representative volume element (RVE) containing 400 grains subjected to periodic boundary conditions has been used for simulations. The effect of mesh discretization on the mechanical response is investigated by considering different meshing resolutions for the RVE. Material parameters of the CP model have been calibrated by fitting the experimental data. Along with the CP model, Johnson–Cook (JC) model is also used for examining the stress-strain behavior of the alloy at various strain rates. Validation of the predictions of CP and JC models is done with the experimental results where the CP model has more accurately captured the deformation behavior of the aluminium alloy.

The CP model is able to predict the mechanical response of AA2024-T3 alloy over a wide range of strain rates with a single set of material parameters. Furthermore, it is observed that the inhomogeneity in stress-strain fields at the grain level is linked to both the orientation of the grains as well as their interactions with one another. The flow and hardening rule parameters influencing the stress-strain curve and capturing the strain rate dependency are also identified.

Computational homogenization-based CP modeling and simulation of deformation behavior of polycrystalline alloy AA2024-T3 alloy at various strain rates is not available in the literature. Therefore, the present computational homogenization-based CP model can be used for predicting the deformation behavior of AA2024-T3 alloy more accurately at both micro and macro scales, under different strain rates.

Long-run changes in living standards occupy an important place in development and growth economics, as well as in economic history. An extensive literature uses heights to study historical living standards. Most historical heights data, however, come from selected subpopulations such as volunteer soldiers, raising concerns about the role of selection bias in these results. Variations in sample mean heights can reflect selection rather than changes in population heights. A Roy-style model of the decision to join the military formalizes the selection problem. Simulations show that even modest differential rewards to the civilian sector produce a military heights sample that is significantly shorter than the cohort from which it is drawn. Monte Carlos show that diagnostics based on departure from the normal distribution have little power to detect selection. To detect height-related selection, we develop a simple, robust diagnostic based on differential selection by age at recruitment. A companion paper (H. Bodenhorn, T. Guinnane, and T. Mroz, 2017) uses this diagnostic to show that the selection problems affect important results in the historical heights literature.

The World Health Organization issued its global action plan on physical activities 2018–2030, emphasizing the importance of context-specific evidence on the subject. Accordingly, this study aims to provide unique and important policy insights on trends and drivers of participation in physical exercises by academic community in Sri Lankan universities.

For this purpose, we collected cross-sectional data (n = 456) in 2020 using a survey, and first, estimated a double-hurdle model to uncover covariates influencing likelihood and intensity of physical exercises overall. Second, count-data models are estimated to capture regularity of key exercises.

The results reveal that about 50% of members do not participate in any general physical exercise. Older members (marginal effect (ME) = 3.764, p < 0.01), non-Buddhists (ME = 54.889, p < 0.01) and alcohol consumers (ME = 32.178, p < 0.05) exhibit a higher intensity of participating in exercises overall. The intensity is lower for rural members (ME = −63.807, p < 0.01) and those with health insurance covers (ME = −31.447, p < 0.05). Individuals diagnosed for chronic illnesses show a higher likelihood of exercising but, their time devotion is limited. The number of children the academic staff members have as parents reduces the likelihood, but for those who choose to exercise have higher time devotion with increased number of children. The covariates play a similar role in determining regularity of key exercises: walking, jogging and exercising on workout machines.

The results imply a need to promote exercising in general and particularly among younger, healthy, insured and female individuals living in rural sector.

The study covers an under-researched professional sub-group in an under-researched developing context, examining both the likelihood and regularity of exercising as both dimensions are equally important for individuals to maintain healthy lives.

The purpose of this paper is to evaluate the fire resistance of an innovative carbon-reinforced PEKK composite for aeronautical applications. To this end, thermal degradation analysis under inert and oxidative atmosphere is carried out. Moreover, a linear model fitting approach is compared to a generally used isoconversional method to validate its reliability for kinetic triplet estimation.

Thermogravimetric analysis carried out under inert and oxidative atmospheres, between 25 and 1000°C for three different heating rates (5, 15, 25°C/min), followed by a qualitative SEM observation of the samples before and after thermal treatment. After the reaction identification by TG/DTG curves, an isoconversional analysis is carried out to estimate the activation energy as a function of the reaction conversion rate. For the identified reactions, the kinetic triplet is estimated by different methods and the results are compared to evaluate their reliability.

In inert case, one global reaction, observed between 500-700°C, seems able to describe the degradation of carbon-PEKK resin. Under oxidative atmosphere, three main reactions are identified, besides the resin degradation, the other two are attributed to char and fiber oxidation. Good agreement achieved between isoconversional and linear model fitting methods in activation energy calculation. The achieved results demonstrate the high thermal resistance of PEKK associated with the ether and ketone bonds between the three aromatic groups of its monomer.

This paper provides a possible degradation model useful for numerical implementation in CFD calculations for aircraft components design, when exposed to high temperatures and fire conditions.

The consistency of stochastic frontier analysis (SFA) and data envelopment analysis (DEA) cost efficiency measures using a sample of 650 commercial and domestic banks in the United States is investigated based on cluster analysis while accounting for the yearly variation in banks.

Due to the importance of efficiency measures for policy and managerial decision-making, the cost efficiency measures of SFA and DEA estimators are examined according to four criteria: levels, rankings, stability over time and stability over clustering groups. In this paper, we present two clustering methods, Gap Statistic and Dindex, that involve SFA and DEA cost efficiency measures. The clustering approach creates homogeneous groups of banks offering a similar mix of efficiency levels. Hence, each evaluated bank knows the cluster to which it belongs. Furthermore, this paper provides nonparametric statistical tests of SFA and DEA cost efficiency measures estimated with and without a clustering approach.

The results suggest that the clustering approach plays a considerable role in the rankings of US banks. Furthermore, the average SFA and DEA cost efficiency measures over time of the homogeneous US banks are substantially higher than those of the heterogeneous US banks.

This research is the first to provide comparative efficiency measures needed for desirable policy conclusions of heterogeneous and homogeneous US banks.