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Record W92638495

Households' Attitudes to Saving, Investment and Wealth

2007· article· en· W92638495 on OpenAlexaboutno aff
J Burns, Máire Dwyer

Bibliographic record

VenueReserve Bank of New Zealand Bulletin · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsInvestment (military)Consumption (sociology)Equity (law)Position (finance)National wealthWork (physics)Labour economicsHousehold incomeDemographic economicsFinanceGeographySociologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Household saving – the difference between household disposable income and household consumption – has declined over the last two decades and now appears to be negative. On the other hand, household wealth has risen. This has been due to rises in house prices, which have pushed up the equity held by households in residential property. While a downward trend in household saving is evident across many developed countries, New Zealand’s household saving rate has been among the lowest, or the lowest, for much of the last 20 years. Also, New Zealand households have lower levels of wealth than households in Australia, Canada, the UK and the US. While New Zealanders’ wealth in housing, as a proportion of disposable income, is around the same as for these other countries, it seems that on average New Zealand households own less financial wealth (e.g. shares and bonds). In view of this pattern, the Economics Department of the Reserve Bank decided to undertake a small-scale exploratory study of households’ attitudes to various forms of investment. The idea behind this work was to get a view, from a sociological perspective rather than an economic perspective, on why wealth in New Zealand is held in the way it is. This perspective contributes to the Economic Department’s ongoing programme of work on the financial position of households.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.022
GPT teacher head0.259
Teacher spread0.237 · 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

Citations8
Published2007
Admission routes1
Has abstractyes

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