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Record W2125766366 · doi:10.5539/res.v6n2p110

Distribution Impact of the Mortgage Interest Deduction in the Czech Republic

2014· article· en· W2125766366 on OpenAlexvenueno aff
Robert Jahoda, Jana Godarovo

Bibliographic record

VenueReview of European Studies · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
FundersMasarykova UniverzitaGrantová Agentura České Republiky
KeywordsCzechDecileDistribution (mathematics)EconomicsInterest rateLoanTax deductionValue (mathematics)LiabilityPersonal income taxGross incomeTax reformPublic economicsState income taxMonetary economicsMacroeconomicsFinance

Abstract

fetched live from OpenAlex

This paper focuses on the mortgage interest deduction for owner-occupied housing in the Czech Republic. The main research question concerns the distribution of personal income tax liability given the rather generous interest deduction for owner-occupied housing loans and changes to it when restrictions are placed on the interest deduction in 2014. We used data for the Czech Republic from the EU-SILC surveys for our analysis. We estimated the value of this tax expenditure at approximately CZK 4.1 billion in 2011, with more than half the amount spent by the highest two deciles in income distribution. Personal income tax reform is legislated to begin in 2015 one part of which will be a cap on loan interest. This reform will lead to a decrease in the yearly value of the tax expenditure but will be followed by an increase in the PIT rate. Taken together, this will generate greater tax expenditures. Our computations show that the impact will be negative on households in the highest decile, while other groups will feel some benefit.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.055
GPT teacher head0.314
Teacher spread0.259 · 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

Citations7
Published2014
Admission routes1
Has abstractyes

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