Search results

The purpose of this paper is to analyze the repayment records of Farm Service Agency (FSA) borrowers in two distinct US farming regions that have been experienced serious drought conditions even as the US economy was going through a recession. The analysis will identify factors that significantly influence both the probability of FSA borrowers’ survival (capability to remain in good credit standing) and temporal endurance (or length of period of good standing with creditor).

This analysis utilizes a data set of farm borrowers of the Farm Service Agency that regular farm lenders have classified as “marginal” relative to other borrowers. The research goal is addressed by confining this study’s regional focus to the Southeast and Midwest that have both dealt with financial stress arising from abnormal natural and economic conditions prevailing during the same time period. A split population duration model is employed to separately identify determinants of the probability and duration of survival (condition of good credit standing).

This study’s results indicate that larger loan balances, declining commodity prices, and the severity of drought conditions have adversely affected both the borrowing farms’ probability of survival and temporal endurance in terms of maintaining non-delinquent borrower standing. Notably, Midwestern farms have been relatively less affected by drought conditions compared to Southeastern farms. This study’s results validate the contention that the farms’ capability to survive and the duration of their survival can be attributed to differences in regional resource endowments, farming activities, and business structures.

This study’s analytical framework departs from the basic duration model approach by considering temporal endurance, in addition to survival probability analysis. This study’s original contributions are enhanced by its specific focus on the contrasting farm business structures and operating environments in the Midwest and Southeast regions.

One of the key elements of survival models is that they enable the researcher to determine whether the length of time an individual (or economic entity) spends in a particular state affects the probability of exiting that state. Natural applications in economics and finance include the analysis of unemployment spells, corporate bankruptcies and mortgage pre‐payments. The distinguishing feature of most applications is the definitive event that marks the transition from the origin to the transition state. We believe that limiting the use of survival analysis to applications in which the event duration appears to be ‘naturally’ available is an unnecessary constraint. For example, the date of emergence from Chapter 11 bankruptcy protection is a subjective management decision and the true event duration, though treated as definitive, is in reality quite ambiguous. We propose that survival models can and should be extended to analyze researcher‐defined events such as the length of time a stock takes to reach a preset price target. We illustrate our point with an examination of IPO aftermarket behavior.

This special issue of the Review of Finance and Accounting presents six papers which use survival analysis as a research method to examine a wide range of research questions in accounting, economics, and finance. Although researchers have increased their use of survival analysis as a research method in recent years, its presence in the methods ‘toolbox’ of these disciplines is not comparable to the physical sciences or other social sciences. Whereas survival analysis is routinely used in biomedicine, sociology, and engineering to study questions related to patient survival, marriage, and equipment failure, the use of survival analysis in economic‐based disciplines is not comparable to other disciplines. This issue was assembled in order to highlight the use of survival analysis in economics‐based disciplines with the express purpose of encouraging its use as a research method to examine a wider range of research issues. The papers assembled in this issue are written by authors who have previously demonstrated an interest in survival analysis.

This paper aims to present an integrated method for optimized project duration and costs, considering the size and cost of crews assigned to project activities' execution modes.

The proposed method utilizes fuzzy set theory (FSs) for modeling uncertainties associated with activities' duration and cost and genetic algorithm (GA) for optimizing project schedule. The method has four main modules that support two optimization methods: modeling uncertainty and defuzzification module; scheduling module; cost calculations module; and decision-support module. The first optimization method uses the elitist non-dominated sorting genetic algorithm (NSGA-II), while the second uses a dynamic weighted optimization genetic algorithm. The developed scheduling and optimization methods are coded in python as a stand-alone automated computerized tool to facilitate the developed method's application.

The developed method is applied to a numerical example to demonstrate its use and illustrate its capabilities. The method was validated using a multi-layered comparative analysis that involves performance evaluation, statistical comparisons and stability evaluation. Results indicated that NSGA-II outperformed the weighted optimization method, resulting in a better global optimum solution, which avoided local minima entrapment. Moreover, the developed method was constructed under a deterministic scenario to evaluate its performance in finding optimal solutions against the previously developed literature methods. Results showed the developed method's superiority in finding a better optimal set of solutions in a reasonable processing time.

The novelty of the proposed method lies in its capacity to consider resource planning and project scheduling under uncertainty simultaneously while accounting for activity splitting.

The purpose of this paper is to demonstrate that the externality that arises from environmental tobacco smoke damage is no ordinary externality. Apart from acting to the detriment of passive smokers, tobacco smoking also affects the survival probabilities of smokers. Incorporating this into its analysis, this paper evaluates the damage caused by smoking, the appropriate design of public health policies and tax policies targeted at reducing tobacco‐related externalities.

By mathematically characterising how smoking impacts smokers and non‐smokers differently, the paper determines smokers' and non‐smokers' lifetime utility, enabling one to evaluate the impact of both health interventions and tax policies.

The paper shows that treatment as well as research and development leading to life‐prolonging health outcomes for smokers are generally oversupplied. The tax recommendations, however, are far from straightforward. Indeed, although not universally the case, it may be optimal to subsidise tobacco usage. The paper also discusses the separating conditions necessary for cigarette taxation to fall or rise with time.

It follows from the paper that ignoring the effect that smoking has on smokers' own life expectancy may lead to erroneous theoretical results and misguided policy recommendations.

The paper seeks to rectify the omission that smoking is somewhat different from other externalities. It develops a model where smoking results in both self‐harm and harm to others, enabling one to demonstrate that there is more to the theoretical study of this externality than is currently acknowledged in the literature.

This paper proposes an integrated approach that seeks to jointly optimize project scheduling and material lot sizing decisions for time-constrained project scheduling problems.

A mixed integer linear programming model is devised, which utilizes the splitting of noncritical activities as a mean toward leveling the renewable resources. The developed model minimizes renewable resources leveling costs along with consumable resources related costs, and it is solved using IBM ILOG CPLEX optimization package. A hybrid metaheuristic procedure is also proposed to efficiently solve the model for larger projects with complex networks structure.

The results confirmed the significance of the integrated approach as both the project schedule and the material ordering policy turned out to be different once compared to the sequential approach under same parameter settings. Furthermore, the integrated approach resulted in substantial total costs reduction for low values of the acquiring and releasing costs of the renewable resources. Computational experiments conducted over 240 test instances of various sizes, and complexities illustrate the efficiency of the proposed metaheuristic approach as it yields solutions that are on average 1.14% away from the optimal ones.

This work highlights the necessity of having project managers address project scheduling and materials lot sizing decisions concurrently, rather than sequentially, to better level resources and minimize materials related costs. Significant cost savings were generated through the developed model despite the use of a small-scale example which illustrates the great potential that the integrated approach has in real life projects. For real life projects with complex network topology, practitioners are advised to make use of the developed metaheuristic procedure due to its superior time efficiency as compared to exact solution methods.

The sequential approach, wherein a project schedule is established first followed by allocating the needed resources, is proven to yield a nonoptimized project schedule and materials ordering policy, leading to an increase in the project's total cost. The integrated approach proposed hereafter optimizes both decisions at once ensuring the timely completion of the project at the least possible cost. The proposed metaheuristic approach provides a viable alternative to exact solution methods especially for larger projects.

Nowadays, building information modeling (BIM) represents an evolution in the architecture, engineering and construction (AEC) industries with its various applications. BIM is capable to store huge amounts of information related to buildings which can be leveraged in several areas such as quantity takeoff, scheduling, sustainability and facility management. The main objective of this research is to establish a model for automated schedule generation using BIM and to solve the time–cost trade-off problem (TCTP) resulting from the various scenarios offered to the user.

A model is developed to use the quantities exported from a BIM platform, then generate construction activities, calculate the duration of each activity and finally the logic/sequence is applied in order to link the activities together. Then, multiobjective optimization is performed using nondominated sorting genetic algorithm (NSGA-II) in order to provide the most feasible solutions considering project duration and cost. The researchers opted NSGA-II because it is one of the well-known and credible algorithms that have been used in many applications, and its performances were tested in several comparative studies.

The proposed model is capable to select the near-optimum scenario for the project and export it to Primavera software. A case study is worked to demonstrate the use of the proposed model and illustrate its main features.

The proposed model can provide a simple and user-friendly model for automated schedule generation of construction projects. In addition, opportunities related to the interface between an automated schedule generation model and Primavera software are enabled as Primavera is one of the most popular and common schedule software solutions in the construction industry. Furthermore, it allows importing data from MS Excel, which is used to store activities data in the different scenarios. In addition, there are numerous solutions, each one corresponds to a certain duration and cost according to the performance factor which often reflects the number of crews assigned to the activity and/or construction method.

This study aims to develop a mathematical programming model for preemptive multi-mode resource-constrained project scheduling problems in construction with the objective of levelling resources considering renewable and non-renewable resources.

The proposed model was solved by the exact method and the genetic algorithm integrated with the solution modification procedure coded with MATLAB software. The Taguchi method was applied for setting the parameters of the genetic algorithm. Different numerical examples were used to show the validation of the proposed model and the capability of the genetic algorithm in solving large-sized problems. In addition, the sensitivity analysis of two parameters, including resource factor and order strength, was conducted to investigate their impact on computational time.

The results showed that preemptive activities obtained better results than non-preemptive activities. In addition, the validity of the genetic algorithm was evaluated by comparing its solutions to the ones of the exact methods. Although the exact method could not find the optimal solution for large-scale problems, the genetic algorithm obtained close to optimal solutions within a short computational time. Moreover, the findings demonstrated that the genetic algorithm was capable of achieving optimal solutions for small-sized problems. The proposed model assists construction project practitioners with developing a realistic project schedule to better estimate the project completion time and minimize fluctuations in resource usage during the entire project horizon.

There has been no study considering the interruption of multi-mode activities with fluctuations in resource usage over an entire project horizon. In this regard, fluctuations in resource consumption are an important issue that needs the attention of project planners.

There is a long period from completing studies to finding a permanent or temporary (but at least satisfactory) job in all European countries, especially in Mediterranean countries, including Italy. This paper aims to study the determinants of this duration and measure them, for the first time in a systematic way, in the case of Italy.

This paper provides several measures of duration, including education level and other criteria. Furthermore, it attempts to identify the main determinants of the long Italian transition, both at a macroeconomic and an individual level. It tests for omitted heterogeneity of those who are stuck at this important crossroads in their life within the context of parametric survival models.

The average duration of the school-to-work transition for young people aged 18–34 years was 2.88 years (or 34.56 months) in 2017. A shorter duration was found for the highly educated; they found a job on average 46 months earlier than those with compulsory education. At a macroeconomic level, the duration over the years 2004–2017 was inversely related to spending in the labour market policy and in education, gross domestic product growth and the degree of trade union density; however, it was directly related to the proportion of temporary contracts. At the individual level, being a woman, a migrant or living in a densely populated area in the South are the risk factors for remaining stuck in the transition. After correcting for omitted heterogeneity, there is clear evidence of positive duration dependence.

Positive duration dependence suggests that focusing on education and labour policy, rather than labour flexibility, is the best way to smooth the transition.

This study develops our understanding of the Italian school-to-work transition regime by providing new and detailed evidence of its duration and by studying its determinants.