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Record W2293397077

Conversion of customary land tenure to statutory land tenure by foreign investors as a land value capture tool to finance infrastructure services

2014· article· en· W2293397077 on OpenAlexaboutno aff
E.C.C. Zambia

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLand Rights and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsLand tenureStatutory lawValue captureCustomary landBusinessPublic infrastructurePopulationFinanceGovernment (linguistics)Value (mathematics)Real propertyLand registrationLand lawPublic economicsEconomicsLawGeographyPolitical scienceBusiness model
DOInot available

Abstract

fetched live from OpenAlex

Following the Vancouver Declaration in 1976, there has been a lot of discussion on the concept of land value capture (LVC) worldwide. There is overwhelming consensus that governments have right to capture at least a portion of the value increments arising from their actions or the general public. Government actions like changes in planning regulations, investments in infrastructure and the general population increase all result in value increments to private land affected by such changes. There is almost a general acceptance that such value increments are unearned by the private and the public is justified to capture part or whole of the increments and redistribute for the benefit of the general public. Such land based tools have been effectively used to improve the provision of infrastructure by a number of countries. Conversion of tenure from customary to statutory was identified as one such government action which results in land value increments capable of being captured for purposes of providing infrastructure services. To capture such increments in Zambia, two instruments were identified - property tax and public land leasing. The study looked at property tax as one instrument and considered public land leasing under three sub instruments – ground rent, premium, and gains sharing agreements. The aim of the study was to compare the four instruments and establish which one captured more value increments on land converted from customary to statutory tenure by foreign investors in Lusaka to finance urban infrastructure. In order to achieve this objective, the study was driven by trying to answer the main question was to establish if conversion of tenure from customary resulted in value increments and if so which of the four instruments was capturing more and establish how w the captured values supporting the provision. Through the use of literature, ideal practices around the world were identified. The study also brought out the relationship between infrastructure, investments in land and land based value capture instruments. This resulted in the construction of a conceptual framework. In answering the research question, multiple strategies were adopted. Firstly, to establish if conversion of tenure resulted in value increments, a quasi experiment was used which studied the values of land under customary tenure and land converted to statutory tenure. The research revealed that land under both tenure types was experiencing value increments. However it was also revealed that land under statutory tenure increased at a faster rate and therefore it was concluded that tenure change from customary to statutory resulted in value increment. To answer the other parts of the research question, a combination of interviews and analysis of secondary data was used. The study finally concluded as follows: i) that conversion of tenure from customary to statutory resulted in value increments; ii) that property tax was capturing more values than ground rent and premiums, and that gains sharing agreements have great potential of making conversion tenure an effective land value capture tool; and iii) That the levels of infrastructure financing from these land based tools were very minimal. After the above conclusions, the study gives recommendations on how the utilisation of the four instruments can be improved and also the need for future research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.000
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 score0.581
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.003
GPT teacher head0.173
Teacher spread0.170 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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