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Record W3123209743

Time Invariant Decision Rules and Land Development: A Dynamic and Stochastic Analysis *

2003· article· en· W3123209743 on OpenAlexvenueno aff
Amitrajeet A. Batabyal

Bibliographic record

VenueCanadian Journal of Regional Science · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCapital Investment and Risk Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyTerrainWelfare economicsForestryEconomicsCartography
DOInot available

Abstract

fetched live from OpenAlex

The Arrow-Fisher-Henry (AFH) analysis of land development under uncertainty has been conducted in a two period model. In contemporary times, Capozza and Helsley (1990), Batabyal (1996, 1997, 2000), and others have analyzed the question of land development under uncertainty in a multi-period setting. We extend aspects of this literature by examining the role that bid values play in the decision to develop land over time and under uncertainty. We first delineate a simple decision rule for a landowner who wishes to obtain, at the very least, a minimal level of revenue from land development. Next, we compute the expected value of the bid that results in land development when this decision rule is followed by the landowner. L'analyse Arrow-Fisher-Henry (AFH) du developpement foncier sous des conditions de l'incertitude fut poursuivie en utilisant un modele base sur deux periodes temporelles. Recemment, Capozza et Helsley (1990), et Batabyal (1996, 1997, 2000) entre autres ont analyse la question du developpement foncier sous des conditions d'incertitude dans un contexte multi-temporel. Nous poursuivons quelques aspects de cette litterature en analysant le role que joue les valeurs offertes dans la decision a developper le terrain dans le temps et sous des conditions de l'incertitude. D'abord, nous definissons une regle decisionnelle simple pour un proprietaire de terrain qui voudrait obtenir, au moins, un revenu minimum du developpement de son terrain. Puis, nous calculons la valeur attendue de l'offre qui donne lieu au developpement du terrain quand cette regle est suivie par le proprietaire. Introduction Natural resource and environmental economists have been interested in the question of (potentially irreversible) land development under uncertainty at least since Weisbrod (1964). Since then, Arrow and Fisher (1974) and Henry (1974) have shed considerable light on this development question. Specifically, these researchers have identified a notion known as option value. The so called Arrow-Fisher-Henry (AFH) notion of option value--sometimes called quasi-option value (QOV)--tells us that when development is both indivisible and irreversible, a landowner who disregards the possibility of procuring new information about the effects of such development will invariably underestimate the benefits of preservation and hence skew the binary choice develop/preserve decision in favour of development. Does this AFH result hold when the development decision is divisible? Epstein (1980), Hanemann (1989), and Batabyal (1999) have studied various aspects of this question and have shown that when the development decision is divisible, the AFH result will not hold in general. One can also inquire about the nature of the development decision when this decision is made in a multi-period setting. Because the AFH analysis is conducted in a two period model, the pertinent development question is Do I develop today or tomorrow? In contrast, in a multi-period setting, the pertinent question is When do I develop? This follows from the fact that a landowner's decision problem now is not over two periods but over n > 2 periods. Markusen and Scheffman (1978), Arnott and Lewis (1979), and Capozza and Helsley (1989) have all studied this question in a deterministic environment. However, when the pertinent development decision is irreversible, the use of a certainty framework will bias results about when land ought to be developed. In fact, as we have learned from the investment under uncertainty literature, (1) uncertainty will typically impart an option value to undeveloped land and delay the development of land from, say, agricultural to urban use. Therefore, if we are to comprehend when land ought to be developed in the presence of an irreversibility, it is essential that we explicitly account for uncertainty. Recently, Titman (1985), Capozza and Helsley (1990), and Batabyal (1996, 1997, 2000) have examined the question of land development under uncertainty. …

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.526
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.200
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2003
Admission routes1
Has abstractyes

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