36,691 research outputs found
An analytical framework to nowcast well-being using mobile phone data
An intriguing open question is whether measurements made on Big Data
recording human activities can yield us high-fidelity proxies of socio-economic
development and well-being. Can we monitor and predict the socio-economic
development of a territory just by observing the behavior of its inhabitants
through the lens of Big Data? In this paper, we design a data-driven analytical
framework that uses mobility measures and social measures extracted from mobile
phone data to estimate indicators for socio-economic development and
well-being. We discover that the diversity of mobility, defined in terms of
entropy of the individual users' trajectories, exhibits (i) significant
correlation with two different socio-economic indicators and (ii) the highest
importance in predictive models built to predict the socio-economic indicators.
Our analytical framework opens an interesting perspective to study human
behavior through the lens of Big Data by means of new statistical indicators
that quantify and possibly "nowcast" the well-being and the socio-economic
development of a territory
Going the same 'weigh': spousal correlations in obesity in the UK
The obesity epidemic has received widespread media and research attention. However, the social phenomenon of obesity is still not well understood. Data from the 2004 and 2006 waves of the British Household Panel Survey (BHPS) show positive and significant correlations in spousal body mass index (BMI). This paper explores three mechanisms of shared individual characteristics, social influence and shared environment to explain this correlation. A number of econometric specifications are used to investigate the role of observed individual characteristics, own health, spouse health, social influence, contextual effects and unobserved individual effects on the influence of these three hypotheses on the correlation in spousal BMI. Results indicate that social influence and shared individual characteristics, which may arise through assortative matching, both contribute to correlation in spousal BMI
Regional economic status inference from information flow and talent mobility
Novel data has been leveraged to estimate socioeconomic status in a timely
manner, however, direct comparison on the use of social relations and talent
movements remains rare. In this letter, we estimate the regional economic
status based on the structural features of the two networks. One is the online
information flow network built on the following relations on social media, and
the other is the offline talent mobility network built on the anonymized resume
data of job seekers with higher education. We find that while the structural
features of both networks are relevant to economic status, the talent mobility
network in a relatively smaller size exhibits a stronger predictive power for
the gross domestic product (GDP). In particular, a composite index of
structural features can explain up to about 84% of the variance in GDP. The
result suggests future socioeconomic studies to pay more attention to the
cost-effective talent mobility data.Comment: 7 pages, 5 figures, 2 table
Complex networks analysis in socioeconomic models
This chapter aims at reviewing complex networks models and methods that were
either developed for or applied to socioeconomic issues, and pertinent to the
theme of New Economic Geography. After an introduction to the foundations of
the field of complex networks, the present summary adds insights on the
statistical mechanical approach, and on the most relevant computational aspects
for the treatment of these systems. As the most frequently used model for
interacting agent-based systems, a brief description of the statistical
mechanics of the classical Ising model on regular lattices, together with
recent extensions of the same model on small-world Watts-Strogatz and
scale-free Albert-Barabasi complex networks is included. Other sections of the
chapter are devoted to applications of complex networks to economics, finance,
spreading of innovations, and regional trade and developments. The chapter also
reviews results involving applications of complex networks to other relevant
socioeconomic issues, including results for opinion and citation networks.
Finally, some avenues for future research are introduced before summarizing the
main conclusions of the chapter.Comment: 39 pages, 185 references, (not final version of) a chapter prepared
for Complexity and Geographical Economics - Topics and Tools, P.
Commendatore, S.S. Kayam and I. Kubin Eds. (Springer, to be published
Cluster structure of EU-15 countries derived from the correlation matrix analysis of macroeconomic index fluctuations
The statistical distances between countries, calculated for various moving
average time windows, are mapped into the ultrametric subdominant space as in
classical Minimal Spanning Tree methods. The Moving Average Minimal Length Path
(MAMLP) algorithm allows a decoupling of fluctuations with respect to the mass
center of the system from the movement of the mass center itself. A Hamiltonian
representation given by a factor graph is used and plays the role of cost
function. The present analysis pertains to 11 macroeconomic (ME) indicators,
namely the GDP (x1), Final Consumption Expenditure (x2), Gross Capital
Formation (x3), Net Exports (x4), Consumer Price Index (y1), Rates of Interest
of the Central Banks (y2), Labour Force (z1), Unemployment (z2), GDP/hour
worked (z3), GDP/capita (w1) and Gini coefficient (w2). The target group of
countries is composed of 15 EU countries, data taken between 1995 and 2004. By
two different methods (the Bipartite Factor Graph Analysis and the Correlation
Matrix Eigensystem Analysis) it is found that the strongly correlated countries
with respect to the macroeconomic indicators fluctuations can be partitioned
into stable clusters
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