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Investment and the Dynamic Cost of Income Uncertainty: the Case of Diminishing Expectations in Agriculture
Abstract
This paper studies optimal investment and the dynamic cost of income uncertainty, applying a stochastic programming approach. The motivation is given by a case study in Finnish agriculture. Investment decision is modelled as a Markov decision process, extended to account for risk. A numerical framework for studying the dynamic uncertainty cost is presented, modifying the classical expected value of perfect information to a dynamic setting. The uncertainty cost depends on the volatility of income; e.g. with stationary income, the dynamic uncertainty cost corresponds to a dynamic option value of postponing investment. The numerical investment model also yields the optimal investment behavior of a representative farm. The model can be applied e.g. in planning investment subsidies for maintaining target investments. In the case study, the investment decision is sensitive to risk.
... This is related to the recognition that agricultural production is often a lengthy process requiring ongoing investments that may not produce expected returns for a prolonged period, thereby being highly sensitive to market risks. Research on investment in agrifood businesses shows extensive impacts as a consequence of uncertainties in investment decisions ( Heikkinen & Pietola, 2009 ). Investment in the food supply chain, from farmers to food-providing services, can also be dampened and discouraged by volatile prices. ...
... However, without stable farm revenue, investment cannot be improved. Research on investment in agri-food businesses shows extensive impacts of uncertainties on investment decisions ( Heikkinen & Pietola, 2009 ). A study by Lagerkvist (2005) reports effects from policy uncertainty on investment in the agriculture industry, while according to Bo and Lensink (2005) econometric evidence supports a nonlinear uncertainty-investment relation. ...
This study examines how managing risk by introducing commodity price insurances may improve the likelihood of increased investment in agri-food supply chains. A model is introduced which shows how insurance products on index prices can reduce the uncertainty of the impact of investment, and also how lower investment can generate the same impact as a higher investment. To show our results, we use two different frameworks which include total profit (Pareto optimal) and Stackelberg game setups. The results demonstrate that in both frameworks the investment will have a greater impact when an insurance product is present. By implication, the study presents an encouraging message to the insurance industry to introduce products to secure supply chain actors’ revenue leading to an increase in investment rate. Consequently, the study offers insight into how the role of traditional government subsidies for protecting farmers, particularly the small to medium-sized farms, may be revisited by replacing some of the existing subsidisation policies with revenue insurance.
... Likewise, different programming variations are made to address studied problems better. As presented in(Heikkinen and Pietola, 2009), by modifying the classical expected value of perfect information (a standard metric of the value of information in decision theory) to a dynamic setting, it is possible to study the dynamic uncertain cost for an investment farm decision problem.Janová (2012) proposed a SP optimization model based on mean variance theory for a crop planning optimization problem. 7 ...
... seed harvest activity while minimizing the risk of crop quality degradationBorodina et al. (2012) TSP demand for jobs, level of rural employment, market prices supporting policy-making on sustainable agricultural development in Ukraine under inherent risks, incomplete informationdeveloping a decision support model to help public water agencies allocating surface water among farmers and authorizing the use of groundwater for irrigationBurer et al. evaluating the economic impact of rainfall regime changes and target policy measures that increase farm sector organic dairy farms, which include risk aversion in the objective function of the decision maker with both and embedded and non-embedded risksGolenko-Ginzburg et al. (1996) SP production speed providing a multilevel decision-making system with multiple restricted resources (manpower, harvesters) for controlling cotton harvestingGuan and Philpott (2011) MSP supply addressing a production planning problem by taking into account uncertain milk supply price-demand curves and contractingHeikkinen and Pietola (2009) SP cost studying optimal investment and income uncertain dynamic cost in agri-dairy policy planningHeumesser et al. (2012) SDP weather analysing a farmers optimal investment strategy for either a water saving drip irrigation system or sprinkler irrigation systemHuh and Lall (2013) TSP weather, market price solving a farm-level decision problem aiming at land allocation among different crops under various water requirements an inventory control of a deteriorating product with non-negligible procurement lead-time that perishes after a known number of periodsKampas and White (2003) JCCP nitrate emissions exploring the trade-off between reliability in achieving emission standards and the cost of compliance to agricultureMaatman et al. (2002) TSP weather describing farmers strategies of production, consumption, selling, purchasing and storage from the start of the growing season until one year after the harvest periodMoghaddam and DePuy (2011) SCCP crop yield determining the optimal number of acres of hay a farm should harvest for horses consumption and the quantity to purchase and sell to maximize the total profit of the farm ...
Given the evolution in the agricultural sector and the new challenges it faces, managing agricultural supply chains efficiently has become an attractive topic for researchers and practitioners. Against this background, the integration of uncertain aspects has continuously gained importance for managerial decision making since it can lead to an increase in efficiency, responsiveness, business integration, and ultimately in market competitiveness. In order to capture appropriately the uncertain conjuncture of most agricultural real-life applications, an increasing amount of research effort is specially dedicated to treating uncertainty. In particular, quantitative modelling approaches have found extensive use in agricultural supply chain management. This paper provides an overview of the latest advances and developments in the application of operations research methodologies to handling uncertainty occurring in the agricultural supply chain management problems. It seeks to: (i) offer a representative overview of the predominant research topics, (ii) highlight the most pertinent and widely used frameworks, and (iii) discuss the emergence of new operations research advances in the agricultural sector. The broad spectrum of reviewed contributions is classified and presented with respect to three most relevant discerned features: uncertainty modelling types, programming approaches, and functional application areas. Ultimately, main review findings are pointed out and future research directions which emerge are suggested.
... These risks and uncertainties are the results of fierce business competition with continuously changing market economy and/or unexpected events during project execution. As a result, recent research in investment decision-making is driving a paradigm shift, integrating new techniques with existing methods to develop a robust decision-making process (Heikkinen and Pietola, 2009). ...
... Foulds and Foulds (2006) proposed a probabilistic dynamic programming model for operations scheduling. Heikkinen and Pietola (2009) modelled the investment decision problem as a Markov decision process (MDP) and they used stochastic programming approaches to make optimal decisions. They presented dynamic uncertainty cost with the modification of the classical expected value of perfect information to a dynamic setting. ...
In multi-stage project investment decision-making with uncertainty, risk mitigation plays a vital role. The return on investment (ROI) that will be realised in making a particular decision quite often carries a high degree of uncertainty, with an increased number of competing investors entering to the market every day. In this research, our objective is to develop a technique for a multi-stage project investment decision problem that deals with uncertainty in ROI and complex interrelated state transition dynamics. We do this by formulating our problem as an infinite horizon stochastic dynamic programming (IHSDP) problem and solve it to maximise the total return over an infinite time horizon. We have implemented our solution to the project investment decision problem in a simple case study using three well-known stochastic dynamic programming algorithms. Our simulation results show that the IHSDP algorithms are useful in making optimum investment decisions in an uncertain business environment.
... The dynamic uncertainty cost as formalized in (10) can be decomposed into a quantity cost (lower expected investment) and a value loss (reduced expected value of investment). Following (Heikkinen and Pietola, 2006) let Pr t denote the probability of investment at time t when the state r t is observed, and let Pr 0 t denote the corresponding probability with expected value maximization (Model 2). Assuming Pr t > 0, define the unit value of investment at time t, v 1 (t), in Model 1 as v 1 ðtÞ ¼ E½y t ðI Ã t Þ Pr t I a and assuming Pr 0 t > 0 define the unit value in Model 2 as v 2 ðtÞ ¼ E½y t ðEðr t jr tÀ1 Þ; I ÃÃ t Þ Pr 0 t I a : ...
... Author's personal copy the dynamic EVPI(t) in (10) can be decomposed into a value loss and a quantity loss component (Heikkinen and Pietola, 2006): (11). An aggregate dynamic uncertainty cost EVPI D can be defined as the difference E½f ðfI Ã t g; fr t gÞ À E½f ðfI ÃÃ t g; fr t gÞ. ...
This paper studies optimal investment decision in agriculture under diminishing income expectations. The goal is to study the cost of income uncertainty and its implications to the efficiency of investment subsidies. Investment decision is modelled as a Markov decision process, extended to account for risk. Applying a stochastic programming approach, the cost of imperfect information is evaluated as the difference between the profitability of investment under stable income and under uncertain income. Computational experiments demonstrate that the cost of imperfect information can be high, deteriorating the efficiency of investment subsidies. Also, examples suggest that the optimal timing of the investment can be sensitive to risk.
... Extending the decision-making unit from the farm to the household, including on-and off-farm resource allocation and including investment variables, can also improve the decision-making process (Viaggi et al., 2010b). While many studies ignore transaction costs and investment dynamics, several studies showed their importance for the adoption of alternative activities and resulting impacts of policies (Bartolini et al., 2007a;Bartolini et al., 2007b;Heikkinen and Pietola, 2009;Peerlings and Polman, 2008;Viaggi et al., 2010a) and to convert to alternative types of farming (Acs et al., 2009;Acs et al., 2007). ...
... These risks and uncertainties are the results of huge business competition and vibrant market economy. As a result, recent research in investment decision making is undergoing a paradigm shift with much integration of new techniques with existing methods to develop robust decision making processes [Heikkinen et al. (2009)]. ...
Proper investment decision making is key to success for every investor in their efforts to keep pace with the
competitive business environment. Mitigation of exposure to risk plays a vital role, since investors are now directly exposed to the uncertain decision environment. The uncertainty (and risk) of an investment is increasing with the increased number of competing investors entering to market. As a result, the expected return on investment (ROI) of a decision quite often carries a high degree of uncertainty. Our objective is to formulate a dynamic programming mathematical model for the investment decision with incorporating this uncertainty in a probabilistic manner. Policy iteration algorithm of the dynamic programming is adopted to solve the model. Our simulation result shows that the algorithm is able to help us in taking optimum investment decision.
... -Moghaddam (2010), Albareda-Sambola et al. (2011), Kavadias and Chao (2008), Wang et al. (2010), Triki et al. (2005), Li and Zabinsky (2011), Sodhi and Tang (2009), Geng et al. (2009), Al-Othman et al. (2008), Zhou and Liu (2003), MirHassani et al. (2000), Wang et al. (2008), Ma et al. (2010), Lin et al. (2011), Santoso et al. (2005), Li and Tirupati (1995), Domenica et al. (2007), Guan and Philpott (2011), El-Sayed et al. (2010), Schutz et al. (2009), Peidro et al. (2009), Longinidis and Georgiadis (2011),Louveaux and Thisse (1985),Yang (2009) andList et al. (2003) Health, pharmaceutical research and development, epidemic model; vaccination, emergencyTanner et al. (2008),Tanner and Ntaimo (2010),Colvin and Maravelias (2008), Chang et al. (2007 andMin and Yih (2010) Economic planning, investment, trade, banking, insurance, and financeHagigi et al. (1990),Sengupta (1982),Fang et al. (2008),Topaloglou et al. (2002Topaloglou et al. ( , 2008,Hibiki (2006),Heikkinen and Pietola (2009), Infanger (2008), Hoyland and Wallace(2008), Luo et al. (2007), Bertocchi et al. (2008), de Lange et al. (2004), Tokat et al. (2003), Kouwenberg (2001), Osorio et al. (2004), Mulvey and Shetty (2004), Board and Sutcliffe ...
Transportation and logistics systems are characterised by their highly dynamic structures along with numerous interconnected processes. The natures of these systems involve various levels of resource allocation decisions where usually it is not always possible to execute these decisions in the field on time at the best possible way because of the unpredictable factors in plans. By considering the uncertain operational environment, this paper explores the uncertainty issue within operational systems and deals with the problem of allocating resources to maximise expected total profit and minimise inefficiencies under uncertainty. The aim is to design, develop, visualise, and effectively deal with a more realistic model to satisfy uncertain demand nodes by leaving minimal or no unsatisfied zones within an operational environment at seaports, transportation, logistics and supply chain systems. A representative optimisation model, which is developed to address the uncertainty issue, has been solved by using an optimisation algorithm. The results show that operational plans without the utilisation of uncertainty models could have negative impacts, including increased emissions, negative environmental effects, along with higher costs to organisations.
... -Moghaddam (2010), Albareda-Sambola et al. (2011), Kavadias and Chao (2008), Wang et al. (2010), Triki et al. (2005), Li and Zabinsky (2011), Sodhi and Tang (2009), Geng et al. (2009), Al-Othman et al. (2008), Zhou and Liu (2003), MirHassani et al. (2000), Wang et al. (2008), Ma et al. (2010), Lin et al. (2011), Santoso et al. (2005), Li and Tirupati (1995), Domenica et al. (2007), Guan and Philpott (2011), El-Sayed et al. (2010), Schutz et al. (2009), Peidro et al. (2009), Longinidis and Georgiadis (2011),Louveaux and Thisse (1985),Yang (2009) andList et al. (2003) Health, pharmaceutical research and development, epidemic model; vaccination, emergencyTanner et al. (2008),Tanner and Ntaimo (2010),Colvin and Maravelias (2008), Chang et al. (2007 andMin and Yih (2010) Economic planning, investment, trade, banking, insurance, and financeHagigi et al. (1990),Sengupta (1982),Fang et al. (2008),Topaloglou et al. (2002Topaloglou et al. ( , 2008,Hibiki (2006),Heikkinen and Pietola (2009), Infanger (2008), Hoyland and Wallace(2008), Luo et al. (2007), Bertocchi et al. (2008), de Lange et al. (2004), Tokat et al. (2003), Kouwenberg (2001), Osorio et al. (2004), Mulvey and Shetty (2004), Board and Sutcliffe ...
Transportation and logistics systems are characterised by their highly dynamic structures along with numerous interconnected processes. The natures of these systems involve various levels of resource allocation decisions where usually it is not always possible to execute these decisions in the field on time at the best possible way because of the unpredictable factors in plans. By considering the uncertain operational environment, this paper explores the uncertainty issue within operational systems and deals with the problem of allocating resources to maximise expected total profit and minimise inefficiencies under uncertainty. The aim is to design, develop, visualise, and effectively deal with a more realistic model to satisfy uncertain demand nodes by leaving minimal or no unsatisfied zones within an operational environment at seaports, transportation, logistics and supply chain systems. A representative optimisation model, which is developed to address the uncertainty issue, has been solved by using an optimisation algorithm. The results show that operational plans without the utilisation of uncertainty models could have negative impacts, including increased emissions, negative environmental effects, along with higher costs to organisations.
... Another stream of research develops a refinement of the basic profit-maximising model in the direction of accounting for uncertainty. Examples are provided by the Real Option Approach (Pyndick, 1991), or by Stochastic programming (Heikkinen and Pietola, 2009). ...
We develop a multi-objective farm-household dynamic integer programming model to simulate investment behaviour in different policy and price scenarios, with a particular focus on the decoupling of the Common Agricultural Policy (CAP). The model takes into account the characteristics of individual assets, including ageing and fixity through the explicit consideration of transaction costs. A case study application in the context of arable farming in Northern Italy is provided as an example. The results emphasise different patterns of reaction of different farm-household types over time, as an effect of the varying opportunity costs of resources and initial asset endowments. Overall, this application highlights the potentialities and limits of the methodology. In particular, the approach proved to be effective in providing a variety of results depending on the individual features of each farm-household, such as the differences between: (a) a ‘no reaction’ attitude; (b) an adaptation of farm activity and assets; and (c) a radical reaction pattern guided by high-income alternatives to farming. This highlights the potential of this tool as a generator of ideas and working hypotheses. We argue that, in view of the further developments of the CAP, the use of instruments able to account for multiple objectives, dynamics and investment choices will become even more relevant in the analysis of EU agricultural policy.
We review and analyze the farming (upstream agribusiness supply chain) research literature since 1965 to identify farming research opportunities for operations management (OM) researchers. A majority of reviewed papers in our corpus, until the turn of the 21st century, primarily focus on improving operational efficiency and effectiveness of farming using optimization techniques. However, during the last two decades, farmers’ welfare and the interests of other stakeholders have drawn OM researchers’ attention. This expanded focus on farming research has become possible due to the proliferation of mobile communication devices and the Internet, as well as advancements in information technology platforms and social media. Our review also shows that there is a paucity of OM literature that leverages increased data availability from the emergence of precision agriculture and blockchain to address major challenges for the farming sector emanating from climate change, natural disasters, food security, and sustainable and equitable agriculture, among others. Big data, in conjunction with opportunities for field‐based experimentation, artificial intelligence and machine learning, and integration of predictive and prescriptive analytics, can be leveraged by OM scholars engaged in farming research. We zero in on specific questions, issues, and opportunities for research in farming. This article is protected by copyright. All rights reserved
This study examines whether oil price uncertainty affects the efficiency of firm-level labor investments. Using a sample of 5183 US firms from 1991 to 2019, we find an inverted U-shaped relationship between oil price uncertainty and labor investment inefficiency. More specifically, firms overinvest in their human capital during the volatility of negative oil price changes. Labor overinvestment (staff overhiring) is highest at an oil price uncertainty of about 38% and decreases after that point. Our findings remain robust to several alternative specifications and are restricted to the subsamples of firms that are more likely to overinvest ex-ante, have high labor intensity, and have overinvestment in non-labor investment expenditures. These findings are relevant to managers, shareholders, and policymakers in devising strategies that demonstrate the response of corporate labor investments to oil price uncertainty.
A large amount of greenhouse gases is emitted from the dairy industry in Canada. Actions must be taken to achieve Canada's environmental goals of decreasing greenhouse gas emissions by 30% below 2005 levels by 2030. Dietary management plays an important role in reducing greenhouse gas emissions in dairy cattle production for current and future climate change conditions. In this study, a factorial interval chance-constrained diet model has been developed for Saskatchewan's dairy cow farms to control emissions by incorporating factorial analysis, interval programming, and chance-constrained programming. Multiple uncertainties (e.g., the price of the milk, the price of the feed, and the disease of animals) have been considered in this model. The chance-constrained programming method, which is effective to tackle uncertainties, has been first integrated in the animal nutrition optimization model. This model can provide the schemes of feed selection for diet formulation under trade-offs between farm profit and environmental impact. In addition, it could facilitate decision-makers to identify interactive relationships among various nutrient levels and manage methane emissions and nitrogen excretion from dairy cattle in dairy farms under various risks. Results show that canola meal, rolled barley grain, soybean meal, and whey would be contained in all optimized diets; the forage proportion would have the most significant effect on methane emissions; factors of forage and crude protein would have negative effects on methane emissions, while the neutral detergent fiber (NDF) would have a positive effect on methane emissions. Results indicate that reducing the number of cows is still an important strategy to reduce carbon emissions under the condition of meeting the growing milk demand. Overall, this model could manipulate the formula of different types of dairy cattle to maximise the benefits of farm and reduce its environmental impact.
Investment is an inherent component of business activities. However, what if firms invest more than they should? This study examines this phenomenon, which is dubbed as overinvestment, among resource firms as induced by the business cycle and macroeconomic uncertainties. The analysis is conducted using unbalanced panel data drawn from 584 resource companies across 32 countries covering 1986 to 2017 in four resource sectors: (1) alternative energy, (2) forestry and paper, (3) mining, and (4) oil and gas producers. The results indicate that the forestry and paper sector overinvests relative to the standard investment level predicted by the investment function regardless of the sample period, while the alternative energy sector tends to underinvest. Also, many emerging economies, including Brazil, China, India, Indonesia, Russia, and South Korea, are found to have overinvested over the last three decades or so. In addition, the results suggest that commodity price inflation plays a more important role in inducing firms’ overinvestment than commodity price uncertainty. It is also found that the home country’s business cycle significantly affects overinvestment, with the sign alternating from negative to positive after the global financial crisis. Furthermore, the finding also shows no significant relationship between global geopolitical risk and overinvestment but a significantly positive relationship is found between global economic and country-level governance policy uncertainties and overinvestment. Lastly, the results suggest that the effect of overinvestment on firm performance after three years is positive, especially for firms in the mining sector.
Investment is important at the state and company level. Although various authors examine the determinants of farms’ investment, analysis is often limited to one investment theory. Moreover, investment decisions in agriculture don’t always comply with investment theory. Therefore, a systematic approach for evaluating the determinants of farms’ investment is still missing. The aim of this paper is to assess the determinants of dairy farms’ investment. The analysis of scientific literature, comparative analysis and generalization was employed to reveal and classify factors that affect farms’ investment, whereas logistic regression was used to assess determinants that affect dairy farms’ investment.
Purpose
In present research, the authors conducted the massive literature review and collected the information, in regards to material handling system (MHS) to build a multi criteria MHS hierarchical module consists of ecological cum fiscal criteria. Moreover, similar literature review assisted the authors to resolve and eventually construct the effectual and robust approach. The purpose of this paper is to facilitate the managers for benchmarking the MHS alternatives operating under similar module via robust decision support system (DSS).
Design/methodology/approach
In present research, the proposed module dealt with ecological (subjective) and fiscal (objective) criteria, where subjective criteria associated with incompleteness, vagueness, imprecision, as well as inconsistency, solicited the discrete information in terms of Grey set via linguistic scale from experts panel. The objective information (capital) has been assigned by expert’s panel in terms of Grey set. To robustly evaluate and select the admirable MHS, three approaches named: degree of possibility, technique for order preference similar to ideal solution as well as Grey relational analysis fruitfully applied to connect and unite discrete information.
Findings
The performance evaluation of MHSs has been carried out under concert of individual fiscal criteria excluding ecological criteria in past researches. Moreover the previous developed DSS tackled sole approach under individual fiscal criteria. The authors found the broad applications of fuzzy sets except Grey set theory in the same context for measuring the performance of MHS alternatives. Aforesaid research gaps have been transformed into research objectives by incorporating the module for both fiscal cum ecological criteria. This research embraces a robust DSS, which has been explored to select the admirable MHS alternative.
Originality/value
An empirical case study has been carried out in order to demonstrate the legitimacy of holistic Grey-MCDM method, implemented over multi criteria MHS hierarchical module. Proposed DSS seems to be the best for organisations, which believe to appraise and select the MHS including fiscal as well as ecological criteria excluding individual fiscal criteria. Moreover, subjective cum objective or individual subjective or objective criteria can be extended with respect to varieties of MHSs.
Most valuation methods for real options originated in financial options. In all cases, the volatility parameter is one of the most important variables and yet the most difficult to estimate with any accuracy. This leads to a lack of confidence in the accuracy of real option values. Several authors have stated that the option value is equal to the expected value of perfect information (EVPI). If true, then decision trees and EVPI analysis can be used to determine an option value. This option value can then be used to calculate the implied volatility. When it can be used, EVPI analysis significantly reduces the solution complexity of an option value and may provide greater accuracy. The proposed equality of EVPI to the real option value is investigated theoretically. Analysis of several realistic projects provides supporting evidence that EVPI does for these examples indeed equal the option value. One example is detailed here. This proposed equality also has important direct implications, the most important of which is that current methods of determining option volatility may provide incorrect estimates.
Dans le cadre de la thèse portant sur l’optimisation et simulation de la chaîne logistique agricole, c'est l'activité de collecte qui est concernée, la période de moisson étant primordiale en termes de quantité et qualité de production, i.e. des revenus pour les agriculteurs et des richesses pour le territoire. Plus spécifiquement, celle-ci implique les opérations de récolte, transport et stockage des céréales, réalisées par plusieurs exploitations agricoles, dispersées géographiquement.
En vue d'aborder la complexité et la nature dynamique de la chaîne logistique d’une coopérative agricole française dans son intégralité, nous avons développé un système d'aide à la décision, qui s'inscrit dans la cadre de la recherche opérationnelle (RO) et plus précisément se réfère à l'optimisation linéaire, robuste et stochastique; la simulation de flux à évènements discrets; ainsi qu'à leur couplage. De plus, la synergie créée entre les outils de la RO, le système d’information géographique, la statistique inférentielle et prédictive rend le système d'aide à la décision compétitif et performant, capable de répondre convenablement au besoin de l’industriel.
The Common Agricultural Policy of the European Union is subject to a continuous process of reform. The objective of this paper is to evaluate the effect of decoupling and related policy and market scenarios, as introduced in the 2003 CAP reform, by way of selected agriculture sustainability indicators and through the aggregation of individual farm-household simulated behaviour. The approach is based on the use of a Net Present Value-maximising dynamic farm-household model. The model is implemented on 80 farm-households to simulate the reaction to scenarios of different agricultural systems in 8 EU countries. The results are measured through three main indicators – represented by farm income, labour use and nitrogen use – evaluated over a period of 14 years. The results of individual farm-households are aggregated first using the concept of farming system and then based on a cluster analysis using the results in different scenarios as discriminant variables. The results show that the CAP as a whole is crucial for the sustainability of farming systems in terms of income and employment, but also provides incentives for higher use of inputs, suggesting a trade-off between social and environmental sustainability concerns. In the range of variation considered, nitrogen and labour use appear much more reactive than income and indicate much higher variability across farms and scenarios. The aggregation by agricultural system denotes rather different behaviour among systems. However, the cluster analysis shows that results appear to be better interpreted by patterns of individual characteristics (location in the plain, structure, asset endowment, labour, etc.) than by country, specialisation or technology.
The paper presents a theory of policy timing that relies on uncertainty and transaction costs to explain the optimal timing and length of policy reforms. Delaying reforms resolves some uncertainty by gaining valuable information and saves transaction costs. Implementing reforms without waiting increases welfare by adjusting domestic policies to changed market parameters. Optimal policy timing is found by balancing the trade-off between delaying reforms and implementing reforms without waiting. Our theory offers an explanation of why countries differ with respect to the length of their policy reforms, and why applied studies often judge agricultural policies to be inefficient.
This paper studies the effect of decoupling on optimal investment in agriculture assuming disinvestment flexibility. Agricultural income be-ing determined by policy processes is subject to policy uncertainty. Case study examples suggest that assuming disinvestment flexibility, decou-pling increases income stability, and a higher level of investments can be achieved even with lower subsides. Increased income stability may also diminish the dynamic cost of income uncertainty. When decoupling of in-come from production, the stability of the compensating direct payments should be ensured.
This paper studies the impact of changing national supports on agricultural investments, when taking into account disinvestment flexibility, focusing on a case study in Finnish agriculture. Numerical examples suggest that the reduction and decoupling of national support will cause a significant reduction in investments in the case study. However, assuming disinvestment flexibility, decoupling, if accompanied by fixed compensating payments, increases income stability and a higher level of investments can be achieved even with lower subsides. Increased income stability may also diminish the dynamic cost of income uncertainty. When decoupling the income support from production, it is important to ensure the stability of the compensating direct payments.
Exploring the attitude of farmers toward risk is important in understanding their managerial decisions, especially given the exposure of farmers to risky events such as drought. However, a survey of the literature finds no study of the risk attitudes of farmers in Turkey. Therefore, this study examines the risk attitudes of farmers in the Lower Seyhan Plain of Turkey. While some variation by utility function exists in the classification of the sampled farmers into risk averse and risk preferring categories, the overwhelming evidence is that the sampled farmers are risk averse. One hundred eighty-two out of 200 estimated Arrow-Pratt risk coefficients imply a risk averse attitude. Thus, these farmers are likely to make managerial decisions that reduce risk, even if the decisions translate into lower income. A policy implication of this finding is that producers are likely to be interested in crop insurance.
Markov decision processes are solved recursively, using the Bellman optimality principle,
$$V(s,t): = \mathop {\max }\limits_{a \in A(s)} \left\{ {r(s,a) + \alpha \sum\limits_{j \in S} {{p_{s,j}}(a)} V(j,t + 1)} \right\}$$ (A)
where V(s, t) is the optimal value of state s at stage t, r(s, a) is the instantaneous profit from action a at state s, S is the state space, A(s) the set of feasible actions at state s and p
i,j
(a) the transition probabilities from i to j. This solution maximizes the expected value of the discounted sum of future profits (the right side of (A)), and assumes risk neutrality, i.e. the decision maker is indifferent between a random variable and its expected value.
We propose an alternative solution, with explicit modeling of risk, using the recursion
$$V(s,t): = \mathop {\max }\limits_{a \in A(s)} \left\{ {r(s,a) + \alpha {S_\beta }(V(Z(s,a),t + 1))} \right\}
$$ (B)
where Z(s, a) is the next state, Sβ
is the quadratic certainty equivalent
$${S_\beta }(X): = EX - \frac{\beta }{2}VarX$$
and β is a parameter modeling the attitude of the decision maker towards risk: β > 0 if risk-averse, β < 0 if risk seeking and β = 0 if risk-neutral (in which case (B) reduces to (A)).
We apply our model to solve two problems of maintenance and inventory and compare with the classical solution.
This study analyses the impacts of de-coupling of agricultural support from production in Finland. A dynamic agricultural sector model, which includes 17 production regions and endogenous investments and technical change, is used in the analysis. Investment in different production techniques is dependent on the relative profitability and the spread of each technique in the population of heterogeneous farms. There are relatively few large farms which use efficient production techniques in Finland. De-coupling weakens the incentive for investment in dairy production and causes a temporary but significant slow down in dairy investments and technical change. Consequently, de-coupling is likely to result in a significant drop in milk and beef production in the next 10–20 year period if no corrective measures are taken in agricultural policy in less-favoured areas such as Finland. However, a slow recovery of investment and output levels are expected in the long run.
This paper examines irreversible investment in a project with uncertain returns, when there is an advantage to being the first to invest, and externalities to investing when others also do so. Pre-emption decreases and may even eliminate the option values created by irreversibility and uncertainty. Externalities introduce inefficiencies in in-vestment decisions. Pre-emption and externalities combined can actually hasten, rather than delay investment, contrary to the usual outcome. These facts demonstrate the importance of extending 'real options' analysis to include strategic interactions.
This study develops a real options approach for analyzing the optimal risk adoption policy in an environment where the adoption means a switch from one stochastic flow representation into another. We establish that increased volatility does not necessarily decelerate investment, as predicted by the standard literature on real options, once the underlying volatility of the state variable is made endogenous. We prove that for a decision maker with a convex (concave) objective function, increased post-adoption volatility increases (decreases) the expected cumulative present value of the post-adoption profit flow, which consequently decreases (increases) the option value of waiting and, therefore, accelerates (decelerates) current investment.
Six measures of returns are used to estimate the most "“appropriate"” market index for southeast Kansas farms. Results suggest that localized indices are more appropriate than state indices for use as the market index. The appropriate index was used to estimate systematic and nonsystematic risk and risk costs for farm planning. Estimated risks depend on the choice of market index, whereas risk costs depend on the index choice and the risk aversion are considered. More risk-averse specialized farmers are not completely compensated for risk.
Summary: The traditional discussion about CO2 emissions and greenhouse gases as a source of global warming has been rather static, namely in the sense that innovation dynamics have not been considered much. Given the global nature of the climate problem, it is natural to develop a more dynamic Schumpeterian perspective and to emphasize a broader international analysis, which takes innovation dynamics and green international competitiveness into account: We discuss key issues of developing a consistent global sustainability indicator, which should cover the crucial dimensions of sustainability in a simple and straightforward way. The basic elements presented here concern genuine savings rates – covering not only depreciations on capital, but on the natural capital as well -, the international competitiveness of the respective country in the field of environmental ("green") goods and the share of renewable energy generation. International benchmarking can thus be encouraged and opportunities emphasized - an approach developed here. This new EIIW-vita Global Sustainability Indicator is consistent with the recent OECD requirements on composite indicators and thus, we suggest new options for policymakers. The US and Indonesia have suffered from a decline in their performance in the period 2000-07; Germany has improved its performance as judged by the new composite indicator whose weights are determined from factor analysis. The countries covered stand for roughly 91% of world GDP, 94% of global exports, 82% of global CO2 emissions and 68% of the population.
A dynamic stochastic business-level land valuation model is derived to analyse how agricultural policy uncertainty regarding reform of the CAP area payment system affects farmland investment incentives. Subjective probability beliefs of Swedish farmers were collected in a survey and used to illustrate the implications of the model. The results show the working of adjustments in current farmland investment incentives triggered by the announcement of agricultural policy programmes linked to farmland. Lack of complete information causes inefficiency by inducing farm operators to over-invest before the reform date if they expect a reform that is likely to reduce their area payment. Policy uncertainty has surprisingly volatile and ambiguous effects on farmland investment incentives. Copyright 2005, Oxford University Press.
The Dixit entry/exit real option model was applied to the entry/exit decisions of New York dairy farmers. For the cost structure of a 500-cow farm, the entry milk price is $17.52 per hundredweight (cwt) and the exit milk price is $10.84. For the 50-cow farm cost structure, the entry price is higher at $23.71 per cwt, and the exit price is also higher at $13.48. If infinite numbers of representative farms enter and exit at these prices, the price of milk should range between $13.48 and $17.52 per cwt.
We examine the investment-uncertainty relationship for a panel of Dutch manufacturing firms. The system generalised method of moments (GMM) estimates suggests that the effect of uncertainty on investment is non-linear: for low levels of uncertainty an increase in uncertainty has a positive effect on investment, whereas for high levels of uncertainty an increase in uncertainty lowers investment. This result is in line with a number of theoretical studies, but has never been demonstrated empirically.
> #! t # a:s: t =2;:::;T: In #MRP# ! := f! t g T+1 t=1 is a discrete, autocorrelated random data process, representing #nancial returns and borrowing conditions, de#ned in a canonical probability space## ; F;P#. The time set is #nite with horizon T , and the decision process x := fx t g T t=1 takes values in X # IR n , for X := # T t=1 X t ; X t # IR n t ; n := P T t=1 n t and is adapted or nonanticipative, i.e. x t = fx t jF t g a:s:, with respect to the #ltration F t<F9
Multistage stochastic linear programming has many practical applications for problems whose current decisions have to be made under future uncertainty. There are a variety of methods for solving the deterministic equivalent forms of these dynamic problems, including the simplex and interior point methods and nested Benders decomposition # which decomposes the original problem into a set of smaller linear programming problems and has recently been shown to be superior to the alternatives for large problems. The Benders subproblems can be visualised as being attached to the nodes of a tree which is formed from the realisations of the random data vectors determining the uncertainty in the problem. Parallel versions of the nested Benders algorithm involvetwo obvious techniques for parallelising the associated tree structure for multiprocessors or multicomputers # subtree parallelisation or a nodal parallelisation # both of which utilise a farming approach. The nodal parallelisation techni...
It is well accepted that conventional NPV criterion fails to capture investment flexibility, and the market approach using riskless-arbitrage-pricing is ideally suited to price real options. However, when valuing complex real options, it is difficult to satisfy the restrictive assumptions required for risk-free arbitrage pricing. Using two-action linear payoff analysis, we show that when it is possible to delay and obtain additional information, an irreversible capital investment decision should be valued as an option taking into considering the value of flexibility. This option value is not based on risk-less arbitrage, but on a more fundamental concept in decision theory - the opportunity loss criterion.
This paper studies the effect of policy uncertainty on the formation of new activities in Romer's (1994) type of an economy, where productivity of labor increases with the number of capital goods. Adding a new capital good requires a capital specific set-up cost, invested prior to using the capital good. Agents are disappointment averse, putting greater utility weight on downside risk [as modeled by Gul (1991)]. Policy uncertainty is induced by the Disappointment aversion implies that investment, labor and capitalists' income drop at a rate proportional to the standard deviation of the tax rate. Hence, policy uncertainty induces first-order adverse effects, whereas policy uncertainty leads to second-order effects when consumers maximize the conventional expected utility. The adverse effects of policy uncertainty can be partially overcome by a proper investment policy. The paper interprets the tax concessions granted to multinationals as a commitment device that helps overcoming the adverse implications of policy uncertainty.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
A dynamic stochastic business-level land valuation model is derived to analyse how agricultural policy uncertainty regarding reform of the CAP area payment system affects farmland investment incentives. Subjective probability beliefs of Swedish farmers were collected in a survey and used to illustrate the implications of the model. The results show the working of adjustments in current farmland investment incentives triggered by the announcement of agricultural policy programmes linked to farmland. Lack of complete information causes inefficiency by inducing farm operators to over-invest before the reform date if they expect a reform that is likely to reduce their area payment. Policy uncertainty has surprisingly volatile and ambiguous effects on farmland investment incentives.
We consider resource management with recursive preferences. These generalize expected utility while eliminating some well-known
difficulties. Monotonicity and convergence properties of optimal decision rules are established using lattice programming
methods. Empirical applications are rangeland and groundwater management. Decreasing the intertemporal elasticity of substitution
implies greater (lower) resource usage with limited (abundant) stocks. This moderates stock evolution and stabilizes consumption.
Increasing risk aversion implies the same or reduced usage over the state space. Intertemporal substitution has a substantial
effect on the optimal decision rule and a moderate effect on the limiting distribution, while risk aversion has a very small
effect.
In this article we show how a project’s option value increases with incremental levels of investment and disinvestment flexibility. We do this by presenting two NPV and seven option pricing models in a strict sequence of increasing flexibility. We illustrate each with numerical examples and determine the maximum value that a project option could ever support. We show that managerial consideration of exit options at the time of project initiation can add value.
The current regulation of new pharmaceuticals is inefficient because it demands arbitrary amounts of information, the type of information demanded is not relevant to decision-makers and the same standards of evidence are applied across different technologies. Bayesian decision theory and an analysis of the value of both perfect and sample information is used to consider the efficient regulation of new pharmaceuticals. This type of analysis can be used to decide whether the evidence in an economic study provides 'sufficient substantiation' for an economic claim, and assesses whether evidence can be regarded as 'competent and reliable'.
The European Union is increasingly relying on direct payments to support farm incomes. Recent research has shown that a direct payment may increase production and investment by risk-averse farmers via a link between wealth, risk aversion and decision making. This paper shows that, even in the absence of risk aversion, a direct payment may stimulate farm investment. With lenders using a standard insolvency rule for determining bankruptcy, the direct payment raises the expected value of marginal investment because it reduces the risk of bankruptcy over the farmer's operating time horizon. The investment response to the direct payment is larger for a farmer with an intermediate versus low or high level of equity, and for a farmer with a long versus short-time horizon.
We examine the investment-uncertainty relationship for a panel of Dutch non-financial firms. The system generalized method of moments (GMM) estimates suggest that the effect of uncertainty on investment is nonlinear: for low levels of uncertainty an increase in uncertainty has a positive effect on investment, while for high levels of uncertainty an increase in uncertainty lowers investment. This result is in line with a number of theoretical studies, but has never been documented empirically. Copyright (c) The London School of Economics and Political Science 2005.
Investment and the Dynamic Cost of Income Uncertainty: the Case of Diminishing Expectations in Agri-culture. MTT Discussion Paper 5, 2006 (previous version also in proc
- T Heikkinen
- K Pietola
Heikkinen, T., Pietola, K., 2006. Investment and the Dynamic Cost of Income Uncertainty: the Case of Diminishing Expectations in Agri-culture. MTT Discussion Paper 5, 2006 (previous version also in proc. ERSA 2006, Volos).
A Stochastic Dynamic Programming Model of Direct Subsidy Payments and Agricultural Investment. Presentation at the Joint Annual Meetings of the American Agricultural Economics Association and Canadian Agricultural Economics Society
- J Vercammen
Vercammen, J., 2003. A Stochastic Dynamic Programming Model of Direct Subsidy Payments and Agricultural Investment. Presentation at the Joint Annual Meetings of the American Agricultural Economics Association and Canadian Agricultural Economics Society. Montreal.
Introduction to Stochastic Programming Springer Series in Opereations Research Is the investment-uncertainty relationship nonlinear? an empirical analysis for the netherlands
- J Birge
- F Louveaux
Birge, J., Louveaux, F., 1997. Introduction to Stochastic Programming. Springer Series in Opereations Research. Bo, H., Lensink, R., 2005. Is the investment-uncertainty relationship nonlinear? an empirical analysis for the netherlands. Economica 72, 307–331.
Can Grater Uncertainty Hasten Investment. Working Paper Taloustohtori (in Finnish)
- R Mason
- H Weeds
Mason, R., Weeds, H., 2005. Can Grater Uncertainty Hasten Investment. Working Paper, Department of Economics, University of Southampton. MTT, 2007. Taloustohtori (in Finnish). www.mtt.fi/kannattavuu-skirjanpito.
The Effect of Decoupling CAP-subsidies on Profitability of Livestock Bilding Investments'. In "CAP-uudistus Suomen maataludessa
- P Uusitalo
- A.-M Heikkilä
- K Pietola
[Uusitalo et al.2004] Uusitalo, P., A.-M. Heikkilä, and K. Pietola: 2004, 'The
Effect of Decoupling CAP-subsidies on Profitability of Livestock Bilding Investments'. In "CAP-uudistus Suomen maataludessa", H. Lehtonen ed. (in
Finnish, attachments available upon request).
Compecon Toolbox for Matlab
- P Fackler