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Record W1704534335 · doi:10.18438/b8488s

Econometric Analysis Suggests Possible Crowding Out of Public Libraries by Book Superstores among Middle Income Families in the 1990s

2007· article· en· W1704534335 on OpenAlexvenueno aff
Stephanie Hall

Bibliographic record

VenueEvidence Based Library and Information Practice · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsMultinomial logistic regressionPopulationCensusHousehold incomeLogistic regressionEconometric modelLogitDemographic economicsCrowdingDemographyGeographyEconomicsStatisticsEconometricsPsychologySociologyMathematics

Abstract

fetched live from OpenAlex

Objective – To determine the effect of large bookstores (defined as those having 20 or more employees) on household library use. Design – Econometric analysis using cross-sectional data sets. Setting – The United States of America. Subjects – People in over 55,000 households across the U.S.A. Methods – Data from 3 1996 studies were examined using logit and multinomial logit estimation procedures: the National Center for Education Statistics’ National Household Education Survey (NHES) and Public Library Survey (PLS), and the U.S. Census Bureau’s County Business Patterns (CBP). The county level results of the NHES telephone survey were merged with the county level data from the PLS and the CBP. Additionally, data on Internet use at the state level from the Statistical Abstract of the United States were incorporated into the data set. A logit regression model was used to estimate probability of library use based on several independent variables, evaluated at the mean. Main results – In general, Hemmeter found that "with regard to the impact of large bookstores on household library use, large bookstores do not appear to have an effect on overall library use among the general population” (613). While no significant changes in general library use were found among high and low income households where more large bookstores were present, nor in the population taken as a whole, middle income households (between $25,000 and $50,000 in annual income) showed notable declines in library use in these situations. These effects were strongest in the areas of borrowing (200% less likely) and recreational purposes (161%), but were also present in work-related use and job searching. Hemmeter also writes that “poorer households use the library more often for job search purposes. The probability of library use for recreation, work, and consumer information increases as income increases. This effect diminishes as households get richer” (611). Finally, home ownership was also correlated with higher library use. Households with children were more than 20% more likely to use the library (610). Their use of the library for school-related purposes, general borrowing, program activities, and so on was not affected by the presence of book superstores. White families with children were somewhat less likely to use the library, while families with higher earning and education levels were more likely to use the library. Library use also increased with the number of children in the family. Shorter distances to the nearest branch and a higher proportion of AV materials were also predictive of higher library use. Educational level was another important factor, with those having less than high school completion being significantly less likely to use the library than those with higher levels of educational attainment. Conclusions – The notable decline in public library use among middle income households where more large bookstores are present is seen as an important threat to libraries, as it may result in a decline in general support and support for funding among an important voting block. More current data are needed in this area. In addition to the type of information examined in this study, the author recommends the inclusion of information on funding, support for library referenda, and library quality as they relate to the presence of large bookstores.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.090
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.033
GPT teacher head0.285
Teacher spread0.252 · 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
Published2007
Admission routes1
Has abstractyes

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