Econometric Analysis Suggests Possible Crowding Out of Public Libraries by Book Superstores among Middle Income Families in the 1990s
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».