Does Urban Density Promote Soci al Interaction? Evidence from Instrumental Variable Estimation

Hawley, Zackary

doi:10.52324/001c.8101

Cited by 13 publications

(8 citation statements)

References 27 publications

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“…We found that people with psychosis living in more densely populated areas reported fewer social contacts, in contrast with results from similar studies in general populations [ 18 – 21 ]. Social deprivation and social fragmentation scores were not associated with the number of social contacts.…”

Section: Discussioncontrasting

confidence: 99%

“…Future large, longitudinal studies are required to overcome these limitations. As described above, our findings in a sample of people with psychotic disorders are in contrast with previous research in general populations [18][19][20][21][22]. Longitudinal designs are required to confirm our findings and test hypotheses as to how more densely populated environments might affect the social connections of people with psychosis.…”

Section: Discussioncontrasting

confidence: 81%

“…We identified six crosssectional studies of general populations (non-clinical samples). Three studies [18][19][20] reported that the number of social contacts increased in areas with higher social propinquity (i.e., physical or psychological proximity), and one study found that people living in more densely populated areas had more social contacts, even if not necessarily with neighbours [21]. Two studies showed that higher social deprivation and fragmentation were associated with subjective aspects of social isolation, such as reduced social trust [22] and greater loneliness [23].…”

Section: Introductionmentioning

confidence: 99%

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Neighbourhood characteristics and social isolation of people with psychosis: a multi-site cross-sectional study

Giacco

Kirkbride

Ermakova

et al. 2021

Soc Psychiatry Psychiatr Epidemiol

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Purpose People with psychosis are vulnerable to social isolation, which is associated with worse clinical outcomes. In general populations, people living in areas with higher population density have more social contacts, while those living in more socially deprived and fragmented areas are less satisfied with their relationships. We assessed whether and how neighbourhood factors are associated with social contacts and satisfaction with friendships for people with psychosis. Methods We carried out a cross-sectional study including people with psychosis aged 18–65 years in urban and rural sites in England. Population density and social deprivation and fragmentation indexes were described within Lower Level Super Output Areas (LSOA). Their associations with participants’ social contacts and satisfaction with friendships were tested with negative binomial and ordinal regression models, respectively. Results We surveyed 511 participants with psychotic disorders. They had a median of two social contacts in the previous week (interquartile range [IQR] = 1–4), and rated satisfaction with friendships as 5 out of 7 (Manchester Short Assessment of Quality of Life; IQR = 4–6). Higher population density was associated with fewer social contacts (Z-standardised relative risk [RR] = 0.88; 95% CI = 0.79–0.99, p = 0.03), but not with satisfaction with friendships (RR = 1.08; 95% CI = 0.93–1.26, p = 0.31). No associations were found for social contacts or satisfaction with friendships with social deprivation or fragmentation indexes. Conclusions Clinicians in urban areas should be aware that their patients with psychosis are more socially isolated when more people live around them, and this could impact their clinical outcomes. These findings may inform housing programmes.

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Section: Discussioncontrasting

confidence: 99%

Section: Discussioncontrasting

confidence: 81%

Section: Introductionmentioning

confidence: 99%

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Neighbourhood characteristics and social isolation of people with psychosis: a multi-site cross-sectional study

Giacco

Kirkbride

Ermakova

et al. 2021

Soc Psychiatry Psychiatr Epidemiol

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“…The use of PD measures is motivated by the finding that people in a more populated city have greater social connections and interactions (Hawley 2012;Bailey et al 2018b) and the "home bias" phenomenon that shows investors tend to overweight local firms in their portfolios (see, e.g., Huberman 2001;Hong, Kubik, and Stein 2008). Thus, we expect that investors from more densely populated areas are also more likely to talk with their social network friends about their gains from investing in local stocks, especially those that exhibit lottery properties.…”

mentioning

confidence: 99%

Attention, Social Interaction, and Investor Attraction to Lottery Stocks

Bali¹,

Hirshleifer²,

Peng³

et al. 2021

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We find that among stocks dominated by retail investors, the lottery anomaly is amplified by high investor attention (proxied by high analyst coverage, salient earnings surprises, or recency of extreme positive returns) and intense social interactions (proxied by Facebook social connectedness or population density near firm headquarters). Such stocks' lottery features attract greater Google search volume and retail net buying, followed by more negative earnings surprises and lower announcement-period returns. The findings provide insight into the roles of attention and social interaction in securities markets, and support the hypothesis that these forces contribute to investor attraction to lottery stocks.

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“…As population density continues to increase, households again become more inclined to online shopping, which may be attributed to two factors. First, greater population density means greater human interactions in working, social, and other contexts, reducing the time available for in-store shopping (Hawley 2012 ; Van den Berg et al 2014 ). Second, previous research has argued that people living in dense areas tend to have greater access to the Internet (Loomis and Taylor 2012 ), which is essential to online shopping.…”

Section: Post-modeling Analysismentioning

confidence: 99%

Modeling household online shopping demand in the U.S.: a machine learning approach and comparative investigation between 2009 and 2017

et al. 2021

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Despite the rapid growth of online shopping and research interest in the relationship between online and in-store shopping, national-level modeling and investigation of the demand for online shopping with a prediction focus remain limited in the literature. This paper differs from prior work and leverages two recent releases of the U.S. National Household Travel Survey (NHTS) data for 2009 and 2017 to develop machine learning (ML) models, specifically gradient boosting machine (GBM), for predicting household-level online shopping purchases. The NHTS data allow for not only conducting nationwide investigation but also at the level of households, which is more appropriate than at the individual level given the connected consumption and shopping needs of members in a household. We follow a systematic procedure for model development including employing Recursive Feature Elimination algorithm to select input variables (features) in order to reduce the risk of model overfitting and increase model explainability. Among several ML models, GBM is found to yield the best prediction accuracy. Extensive post-modeling investigation is conducted in a comparative manner between 2009 and 2017, including quantifying the importance of each input variable in predicting online shopping demand, and characterizing value-dependent relationships between demand and the input variables. In doing so, two latest advances in machine learning techniques, namely Shapley value-based feature importance and Accumulated Local Effects plots, are adopted to overcome inherent drawbacks of the popular techniques in current ML modeling. The modeling and investigation are performed at the national level, with a number of findings obtained. The models developed and insights gained can be used for online shopping-related freight demand generation and may also be considered for evaluating the potential impact of relevant policies on online shopping demand.

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Does Urban Density Promote Soci al Interaction? Evidence from Instrumental Variable Estimation

Cited by 13 publications

References 27 publications

Neighbourhood characteristics and social isolation of people with psychosis: a multi-site cross-sectional study

Neighbourhood characteristics and social isolation of people with psychosis: a multi-site cross-sectional study

Attention, Social Interaction, and Investor Attraction to Lottery Stocks

Modeling household online shopping demand in the U.S.: a machine learning approach and comparative investigation between 2009 and 2017

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