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Record W1531283556

Sind Haushalte mit Wohneigentum sparsamer als Mieterhaushalte

2003· preprint· de· W1531283556 on OpenAlexaboutno aff
Ruth Grunert

Bibliographic record

VenueEconstor (Econstor) · 2003
Typepreprint
Languagede
FieldEconomics, Econometrics and Finance
TopicGerman Economic Analysis & Policies
Canadian institutionsnot available
Fundersnot available
KeywordsRentingQuarter (Canadian coin)Consumption (sociology)GermanService (business)BusinessDemographic economicsEconomicsLabour economicsAgricultural economicsGeographyEconomyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

In this paper examines the data of the income and consumption survey of 1998 with regard to savings and wealth. Households owning their property (owner households) and households renting the property (tenant households) are analysed seperatly and then compared. For further insight these two groups are also devided into East and West German households. In 1998, almost half of the West German and almost a quarter of the East German private households owned property they occupied themselves. The average owner household saves a monthly amount three times as large as the tenant household. The decisive economizing motive for the owner households is servicing its mortgages and loans. However, at the same time, there is the necessity to form reserves for the property maintainance as well as the renovation. In comparison to tenant households, owner households which no longer have to service mortgages or loans, have a higher average rate of saving. The estimate of the saving by means of regression analysis confirms the significant positive influence of the 'owner' status on savings. However, in every estimate the houshold's income proves to be the main influence on savings activity.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.022
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0220.004

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 head0.032
GPT teacher head0.234
Teacher spread0.202 · 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 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
Published2003
Admission routes1
Has abstractyes

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