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Record W2024832837 · doi:10.3828/tpr.2011.28

Viewpoint: The planning research agenda: after the 'Great Recession' <i>Recalibrating the applications of economic analysis in urban policy</i>

2011· article· en· W2024832837 on OpenAlexaff
David Amborski

Bibliographic record

VenueTown Planning Review · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRecessionGreat recessionPolitical scienceEconomicsUrban policyPublic economicsUrban planningKeynesian economicsEngineering

Abstract

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The recent global economic recession and the subsequent economic aftershock have impacted on urban governments and created the need to recalibrate economic analysis and tools in urban areas. Since economic analysis and economic tools have played a role in shaping both urban planning and policy decisions, it can be argued that effective planning must consider market forces to regulate land market forces and must use incentives to stimulate desirable outcomes. Problems may arise when economic tools are disregarded, when tools are not reconsidered in the face of market force changes, or when the economic impacts of various policy options are not considered. The purpose of this Viewpoint is to identify situations where care and analysis are needed so as to assess either the impacts of existing economic tools, or where economic analysis should be used to assess the impacts of urban policy tools. Planners either need to be adequately educated to undertake basic economic analysis or to have the ability to recognise when it is appropriate to enlist the support of an urban economist or a planner with the requisite expertise. Examples will be provided to demonstrate where economic tools are applied without due consideration of their current impact and where policies are advocated without fully assessing the economic impact. These cases include methods of financing infrastructure, the application of pricing rules for user charges and the impacts of tools for encouraging public benefits when approving new development. Some key planning research issues and priorities for this field are then suggested. Context The need to consider economics in the context of urban policy and planning decisions has been reinforced by what has been termed the 'Great Recession' that affected countries around the world in 2008-2010 (Brookings Institution and LSE Cities, 2010, 4). The recession and its aftermath have had significant impacts on the fiscal health of cities in terms of public finance and the ability of local markets to respond. Although emerging economies in Asia and Latin America may have had different experiences in this period - while numerous economies in Europe and the US have faltered - the negative impact on private sector markets and on urban areas, especially the metropolitan regions that are engines of the economy and recovery, has been self-evident. On the public sector side too, the recession and its consequences have dampened local economies, which are obviously a component of the wider national economy, and decreased municipal governments' own sources of revenue, including local taxes. Local taxes levied by local governments vary according to national circumstances. They could reflect, for instance, local sales taxes, payroll taxes and /or property taxes. Sales taxes are vulnerable to lower sales in a recession. In terms of payroll taxes, recessions lead to higher unemployment rates. And as recessions deter the growth of new business and the housing sector, there will be lower growth in the assessment base to which to apply property taxes. There may also be higher default rates, delinquencies and non-payment of property taxes. Also, the local population has economic pressure and local politicians will be reluctant to increase property tax rates. As central and other upper-tier governments face reductions in their taxes (such as corporate and personal income taxes as well as sales and other taxes), they have less revenue to use for transfer payments to municipal governments. In countries where some taxes are collected locally and sent to the central government and then reallocated to local governments, less revenue is collected for redistribution. This is also reflected in general transfers that may also face reductions for local governments. Revenue from user charges is also likely to suffer. Many permits and fees are related to the vibrancy of the economy for items such as building permits and business licences. …

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.050
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0090.021
Scholarly communication0.0170.018
Open science0.0040.007
Research integrity0.0170.023
Insufficient payload (model declined to judge)0.0180.002

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 head0.131
GPT teacher head0.339
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2011
Admission routes1
Has abstractyes

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