Dataset Information

Proximity and Economic Activity: An Analysis of Vendor-University Transactions.

ABSTRACT: This paper using transaction based data to provide new insights into the link between the geographic proximity of businesses and associated economic activity. It contributes to the literature by developing both two new measures of distance and a set of stylized facts on the distances between observed transactions between vendors and customers for a research intensive sector - universities. We show that spending on research inputs is more likely to be expended at businesses physically closer to universities than those farther away. That relationship is stronger for High Tech and R&D performing businesses than businesses in general, which is consistent with theories emphasizing the role of tacit knowledge. We find that firms behave in a way that is consistent with the notion that propinquity is good for business: if a firm supplies a project at a university in a given year, it is more likely to open an establishment near that university in subsequent years than other firms. We also investigate the link between transactional distance and economic activity and show that when a vendor has been a supplier to a project at least one time, that vendor is subsequently more likely to be a vendor on the same or related project.

SUBMITTER: Goldschlag N

PROVIDER: S-EPMC6364856 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Proximity and Economic Activity: An Analysis of Vendor-University Transactions.

Goldschlag Nathan N Lane Julia J Weinberg Bruce B Zolas Nikolas N

Journal of regional science 20180622 1

This paper using transaction based data to provide new insights into the link between the geographic proximity of businesses and associated economic activity. It contributes to the literature by developing both two new measures of distance and a set of stylized facts on the distances between observed transactions between vendors and customers for a research intensive sector - universities. We show that spending on research inputs is more likely to be expended at businesses physically closer to u ...[more]

PMID: 30739957

Similar Datasets

Project description:BackgroundGastroesophageal reflux disease (GERD) is one of the most common gastrointestinal disorders worldwide, with relevant impact on the quality of life and health care costs.The aim of our study is to assess the prevalence of GERD based on self-reported symptoms among university students in central Italy. The secondary aim is to evaluate lifestyle correlates, particularly eating habits, in GERD students using automatically recorded transactions through cashiers at university canteen.MethodsA web-survey was created and launched through an app, ad-hoc developed for an interactive exchange of information with students, including anthropometric data and lifestyle habits. Moreover, the web-survey allowed users a self-diagnosis of GERD through a simple questionnaire. As regard eating habits, detailed collection of meals consumed, including number and type of dishes, were automatically recorded through cashiers at the university canteen equipped with an automatic registration system.ResultsWe collected 3012 questionnaires. A total of 792 students (26.2% of the respondents) reported typical GERD symptoms occurring at least weekly. Female sex was more prevalent than male sex. In the set of students with GERD, the percentage of smokers was higher, and our results showed that when BMI tends to higher values the percentage of students with GERD tends to increase. When evaluating correlates with diet, we found, among all users, a lower frequency of legumes choice in GERD students and, among frequent users, a lower frequency of choice of pasta and rice in GERD students.DiscussionThe results of our study are in line with the values reported in the literature. Nowadays, GERD is a common problem in our communities, and can potentially lead to serious medical complications; the economic burden involved in the diagnostic and therapeutic management of the disease has a relevant impact on healthcare costs.ConclusionsTo our knowledge, this is the first study evaluating the prevalence of typical GERD-related symptoms in a young population of University students in Italy. Considering the young age of enrolled subjects, our prevalence rate, relatively high compared to the usual estimates, could represent a further negative factor for the future economic sustainability of the healthcare system.

Project description:BackgroundTwo-dimensional speckle-tracking echocardiography (2D-STE) enables objective assessment of left atrial (LA) deformation through the analysis of myocardial strain, which can be measured by different speckle-tracking software. The aim of this study was to compare the consistency of 3 different commercially available software, which include vendor-specific software for measuring left ventricle (VSSLV), vendor-independent software packages for measuring LV strain (VISLV) and vendor-independent software packages for measuring LA strain (VISLA).MethodsSixty-four subjects (mean age: 44 ± 16 years, 50% males) underwent conventional echocardiograms using a GE Vivid 9 (GE Ultrasound, Horten, Norway) cardiac ultrasound system. Standard apical 4 and 2 chamber views of the left atrium were obtained in each subject with a frame-rate range of 40-71 frames/s. LA strain during the contraction phase (Sct), conduit phase (Scd), reservoir phase (Sr = Sct + Scd) were analyzed by 2 independent observers and 3 different software.ResultsSct, Scd, Sr were, respectively, - 11.26 ± 2.45%, - 16.77 ± 7.06%, and 28.03 ± 7.58% with VSSLV, - 14.77 ± 3.59%, - 23.17 ± 10.33%, and 38.23 ± 10.99% with VISLV, and - 14.80 ± 3.88%, - 23.94 ± 10.48%, and 38.73 ± 11.56% when VISLA was used. A comparison of strain measurements between VSSLV and VIS (VISLV and VISLA) showed VIS had significantly smaller mean differences and narrower limits of agreement. Similar results were observed in the coefficient of variation (CV) for measurements between VSSLV and VIS (VISLV and VISLA). Comparison of the intra-class correlation coefficients (ICCs) indicated that measurement reliability was weaker with VSSLV (ICC < 0.6) than with VIS (VISLV and VISLA) (ICC > 0.9). For intra-observer ICCs, VISLA > VSSLV = VISLV. For inter-observer ICCs, VSSLV > VISLA > VISLV.ConclusionsSoftware measurement results of LA strain vary considerably. We recommended not measuring LA strain across vendor platforms.

Project description:ImportanceEvidence suggests that limiting access to firearms among individuals at high risk of suicide can be an effective means of suicide prevention, yet accurately identifying those at risk to intervene remains a key challenge. Firearm purchasing records may offer a large-scale and objective data source for the development of tools to predict firearm suicide risk.ObjectiveTo test whether a statewide database of handgun transaction records, coupled with machine learning techniques, can be used to forecast firearm suicide risk.Design, setting, and participantsThis prognostic study used the California database of 4 976 391 handgun transaction records from 1 951 006 individuals from January 1, 1996, to October 6, 2015. Transaction-level random forest classification was implemented to predict firearm suicide risk, and the relative predictive power of features in the algorithm was estimated via permutation importance. Analyses were performed from December 1, 2020, to May 19, 2022.Main outcomes and measuresThe main outcome was firearm suicide within 1 year of a firearm transaction, derived from California death records (1996-2016). With the use of California's Dealer's Records of Sale (1996-2015), 41 handgun, transaction, purchaser, and community-level predictor variables were generated.ResultsThere are a total of 4 976 391 transactions in the California's Dealer's Record of Sale database representing 1 951 006 individuals (1 525 754 men [78.2% of individuals]; mean [SD] age, 43.4 [13.9] years). Firearm suicide within 1 year occurred in 0.07% of handgun transactions (3278 transactions among 2614 individuals). A total of 38.6% of observed firearm suicides were among transactions classified in the highest-risk ventile (379 of 983 transactions), with 95% specificity. Among the small number of transactions with a random forest score above 0.95, more than two-thirds (24 of 35 [68.6%]) were associated with a purchaser who died by firearm suicide within 1 year. Important features included known risk factors, such as older age at first purchase, and previously unreported predictors, including distance to firearms dealer and month of purchase.Conclusions and relevanceThis prognostic study presented the first large-scale machine learning analysis of individual-level handgun transaction records. The results suggested the potential utility of such records in identifying high-risk individuals to aid suicide prevention efforts. It also identified handgun, individual, and community characteristics that have strong predictive relationships with firearm suicide and may warrant further study.

Dataset Information

Proximity and Economic Activity: An Analysis of Vendor-University Transactions.

Publications

Proximity and Economic Activity: An Analysis of Vendor-University Transactions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets