Geographical Network Research Articles

Game in another town: Geography of stock watchlists and firm valuation

Beyond a bias toward local stocks, investors prefer companies in certain cities over others. This study uses the geographic network of investor-followed stocks from stock watchlists to identify intercity investment preferences in China. We measure the city-pair connectivity by its likelihood of sharing an investor in common whose stock watchlist is highly concentrated in the firms of that city pair. We find that a higher connectivity-weighted aggregate stock demand-to-supply ratio across connected cities is associated with higher stock valuations, higher turnover, better liquidity, and lower cost of equity for firms in the focal city. The effects are robust to controls for geographic proximity and the broad investor base, are stronger among small firms, extend to stock return predictability, and imply excess intercity return comovement. Our results suggest that city connectivity revealed on the stock watchlist helps identify network factors in asset pricing.

Facilitating Policy Innovation in the Middle East

There is a need to ensure the effective transfer and diffusion of successful policy innovations that facilitate sustainable development in the Middle East region. However, to date there has been limited research on policy innovation in the Middle East region through theories such as policy diffusion, with most studies focused on North America and Western Europe. The following short essay represents an attempt to highlight this shortfall, stimulate discussion on what key factors could facilitate policy innovation in the Middle East, and promote further studies to address this imbalance. This is a theoretical study that primarily examines three theories that address the spread of policy innovation between governments: policy diffusion, policy laboratories, and borrowing strength. These theories are applied to different factors that influence policy innovation (geographic proximity, political ideology, culture, and networks and individuals) considered within the context of the Middle East to assess their relevance and applicability. Based on the application of these theories and key factors, the essay proposes three novel hypotheses for policy innovation, which are linked to the different factors. Each hypothesis identifies an area where it is expected that policy innovation would occur, if the theories hold true in a Middle Eastern Context. The essay contains an accompanying call to test these hypotheses within the region. It is expected that further studies in this area will be able to both test and build on the proposed hypotheses to determine the applicability of current (Western) theories to explain policy innovation in the Middle East and move towards a more comprehensive and targeted theory of policy in this region.

Road Accessibility during Natural Hazards Based on Volunteered Geographic Information Data and Network Analysis

Natural hazards can present a significant risk to road infrastructure. This infrastructure is a fundamental component of the transportation infrastructure, with significant importance. During emergencies, society heavily relies on the functionality of the road infrastructure to facilitate evacuation and access to emergency facilities. This study introduces a versatile, multi-scale framework designed to analyze accessibility within road networks during natural hazard scenarios. The first module of the framework focuses on assessing the influence of natural hazards on road infrastructure to identify damaged or blocked road segments and intersections. It relies on near real-time information, often provided by citizen science through Volunteered Geographic Information (VGI) data and Natural Language Processing (NLP) of VGI texts. The second module conducts network analysis based on freely available Open Street Map (OSM) data, differentiating between intact and degraded road networks. Four accessibility measures are employed: betweenness centrality, closeness centrality, a free-flow assumption index, and a novel alternative routing assumption measure considering congestion scenarios. The study showcases its framework through an exemplary application in California, the United States, considering different hazard scenarios, where degraded roads and connected roads impacted by the hazard can be identified. The road extraction methodology allows the extraction of 75% to 100% of the impacted roads mentioned in VGI text messages for the respective case studies. In addition to the directly extracted impacted roads, constructing the degraded network also involves finding road segments that overlap with hazard impact zones, as these are at risk of being impacted. Conducting the network analysis with the four different measures on the intact and degraded network, changes in network accessibility due to the impacts of hazards can be identified. The results show that using each measure is justified, as each measure could demonstrate the accessibility change. However, their combination and comparison provide valuable insights. In conclusion, this study successfully addresses the challenges of developing a generic, complete framework from impact extraction to network analysis independently of the scale and characteristics of road network types.

Geographic network effects in a circular economy: A field data analysis of reusable packaging services

AbstractProduct–service systems (PSS) have the potential to align businesses’ financial incentives with environmental objectives. Conceptual and qualitative research on non‐ownership consumption suggests that such offerings benefit from dense local networks, which motivate system adoption and use. However, geographic network effects and their magnitude have not been examined with field data capturing revealed behavior. This paper leverages a large field dataset from a system for reusable take‐out food containers to evaluate the effect of increased geographic network density of participating restaurants on (a) the acquisition of new users and (b) the frequency of system use. Based on fixed effects Poisson panel models, this paper finds statistically significant and practically meaningful positive effects of increased geographic network density on acquiring new users. Notably, marginal effects of increased geographic network density on user acquisition diminish as networks get denser. In terms of frequency of use, no significant effects of geographic network density are identified. These results contribute to the literature on non‐ownership consumption by presenting nuanced field evidence of indirect, cross‐side network effects in PSS. Furthermore, findings encourage businesses and policymakers to promote PSS with dense local networks.

Tombstones, Stonemasons, and Mental Maps

Abstract This article proposes a methodology for understanding the business networks and mental maps of Jewish communities in central and eastern Europe from the late nineteenth and first half of the twentieth centuries. The methodology is based on scrupulous documentation of Jewish tombstones, especially the signatures of stonemasons. Through analysis of the signatures found in the Jewish cemeteries of Croatia, geographical networks become apparent, and we can reconstruct the mental maps of Croatian Jews, both in the Austro-Hungarian period and in interwar Yugoslavia. The Jewish cemeteries in some other central European cities serve as comparative material for our discussion.

Comparative analysis of archival data on yields of main field crops from fertilizer experiments on different soil types in Bulgaria

The main aim of the study was to systematize and summarize archival data on yields from main field crops from multiple fertilizer experiments of Nikola Poushkarov ISSAPP on various soil types in Bulgaria. A large set of results from the field fertilizer experiments carried out on four different soil types: Calcareous Chernozem (Trastenik village), Haplic Chernozem (Gorni Dabnik village), Haplic Luvisol (Nikolaevo village) and Planosol (Sekirovo village) in the geographical network of Bulgaria of the Nikola Poushkarov ISSAPP has been accumulated. Eight fertilization variants were tested with annual application of potassium (0, 8, 16 and 32 kg/da K2 O) on two backgrounds of nitrogen (N) and phosphorus (P) fertilization. Crop rotation during the individual years was as follows: wheat (Triticum aestivum L.), maize (Zea mays L.), sunflower (Helianthus annuus L.) and sugar beet (Beta vulgaris L.), grown under non-irrigation conditions. Optimum rates of fertilization with the main nutrients, N, P and K have been established for the different soil types. There are complex interactions between the different rates of fertilization with the main macronutrients and this has a significant impact on the quantity and quality of agricultural crop yields. The difference in yields between the two investigated periods (1963-1970 and 1971-1975), at the same levels of fertilization, shows that the level of yields and accordingly, the use of nutrients are also determined to a significant extent by the climatic conditions. With an increase in the fertilizer rate, the yields also increase, and the rates of 16 kg and 32 kg K2 O remain the most effective, regardless of the occurrence of potassium accumulation in these variants. This was also confirmed by the results of the analysis of variance of the yields of cultivated crops. There was a pronounced increase of the effect of potassium fertilization in soils less supplied with this element, i.e. the Haplic Luvisol (Nikolaevo village) and Planosol (Sekirovo village). The effect of the potassium fertilization increases and expands in well supplied with potassium soils, but possessing unfavorable conditions for nutrition with this element, as in the case with the Calcareous Chernozem (Trastenik village). There was also an effect of potassium fertilization, although inconsistent, in soils well supplied with potassium, such as Haplic Chernozem (Gorni Dabnik village).

Analysis of a Multiple Traffic Flow Network’s Spatial Organization Pattern Recognition Based on a Network Map

Detecting the spatial organization patterns of urban networks with multiple traffic flows from the perspective of complex networks and traffic behavior will help to optimize the urban spatial structure and thereby promote the sustainable development of the city. However, there are notable differences in regional spatial patterns among the different modes of transportation. Based on the road, railway, and air frequency data, this article investigates the spatial distribution and accessibility patterns of multiple transportation flows in the Yangtze River Economic Belt. Next, we use the TCD (Transportation Cluster Detection) community discovery algorithm and integrate it with the Baidu Maps API to obtain real-time time cost data to construct a community detection model of a multiple traffic flow network. We integrate the geographical network and topological network to perform feature extraction and rule mining on the spatial organization model of the urban network in the Yangtze River Economic Belt. The results show that: (1) The multiple traffic flow network of the Yangtze River Economic Belt has significant spatial differentiation. The spatial differentiation of aviation and railway networks is mainly concentrated between regions and within provinces, while the imbalance of highway networks is manifested as an imbalance within regions and between provinces. (2) The accessibility pattern of the highway network in the Yangtze River Economic Belt presents a “core–edge” spatial pattern. The accessibility pattern of the railway network generally presents a spatial pattern of “strong in the east and weak in the west”. Compared with sparse road and railway networks, the accessibility pattern of the aviation network shows a spatial pattern of “time and space compression in western cities”. (3) A total of 24 communities were identified through the TCD algorithm, mainly encompassing six major “urban economic communities” located in Guizhou, Yunnan, Anhui, Sichuan–Chongqing, Hubei–Hunan–Jiangxi, and Jiangsu–Zhejiang–Shanghai. (4) The urban network space organization model of the Yangtze River Economic Belt can be roughly divided into three models: the “single-core” model, with Guizhou, Kunming, and Hefei as the core, the “dual-core” model, constructed by Chengdu–Chongqing, and the “multi-core” model, constructed by Changsha–Wuhan–Nanchang and Shanghai–Nanjing–Hangzhou. This model of urban network spatial organization holds indicative significance in revealing the spatial correlation pattern among prefecture-level city units.

PANDORA: Deep Graph Learning Based COVID-19 Infection Risk Level Forecasting

Coronavirus disease 2019 (COVID-19) as a global pandemic causes a massive disruption to social stability that threatens human life and the economy. An effective forecasting system is arguably important to provide an early signal of the risk of COVID-19 infection so that the authorities are ready to protect the people from the worst. However, making a good forecasting model for infection risks in different cities or regions is not an easy task, because it has a lot of influential factors that are difficult to be identified manually. To address the current limitations, we propose a deep graph learning model, called PANDORA, to predict the infection risks of COVID-19, by considering all essential factors and integrating them into a geographical network. The framework uses geographical position relationships and transportation frequency as higher order structural properties formulated by higher order network structures (i.e., network motifs). Moreover, four significant node attributes (i.e., multiple features of a particular area, including climate, medical condition, economy, and human mobility) are also considered. We propose three different aggregators to better aggregate node attributes and structural features, namely, Hadamard, Summation, and Connection. Experimental results over real data show that PANDORA outperforms the baseline methods with higher accuracy and faster convergence speed, no matter which aggregator is chosen.

Research on optimization strategies for urban park green space planning in Nanjing based on GIS from the perspectives of network analysis and Thiessen polygon theory

Urban Park green space is an essential carrier and form of urban recreation space, playing an important role in improving the ecological environment and enhancing the image of the city. With the process of urbanization and the continuous improvement of residents’ living standards, urban park construction has become increasingly important to meet people’s growing needs for a better life. However, the evaluation indicators for urban park green space in China mostly consider macroscopic levels, such as the area, number and per capita green area of parks. This cannot fully reflect the level of park green space services. Accessibility can reflect the convenience of residents to reach the park, provide guidance for the reasonable layout of parks and have various evaluation methods relying on geographic information technology. Therefore, it is necessary to quantitatively study the layout and accessibility of park green space. Taking the central urban area of Nanjing as the research object, this paper summarizes existing research and focuses on evaluating the accessibility of park green space while analyzing the layout of parks from multiple perspectives, combined with the existing pressure appraisal of park green space services. Relevant data on Nanjing, including overall planning and statistical yearbooks, were collected and analyzed using geographic information system (GIS) tools, network analysis and Thiessen polygon theory to analyze the service pressure, demand and accessibility of park green space in the central urban area under different transportation methods. Based on the results, targeted optimization strategies are proposed.

Adaptive Seamless Redundancy to Achieve Highly Dependable MQTT Communication

Nowadays, large enterprises, critical infrastructures, and utilities are complex systems, and consist of a number of sub-systems, often located far away from each other, that coordinate their operations over geographic networks. Typically, they are interconnected through the Internet and communicate by means of TCP/IP-based protocols. When system-level resilience is required, extremely high dependability is demanded from the network. In this paper a solution is described and evaluated that loosely resembles SDN architectures. A redundant version of MQTT is used for the data plane, whereas an adaptive mechanism is implemented in the control plane that evaluates the quality of paths and dynamically selects the best choice. To maximize dependability, yet keeping resource consumption to acceptable levels, concepts borrowed from Reinforcement Learning are exploited. An experimental campaign corroborated our expectations and showed the practical feasibility of our proposal.

Constraint-Based Breakpoints for Responsive Visualization Design and Development.

This paper introduces constraint-based breakpoints, a technique for designing responsive visualizations for a wide variety of screen sizes and datasets. Breakpoints in responsive visualization define when different visualization designs are shown. Conventionally, breakpoints are static, pre-defined widths, and as such do not account for changes to the visualized dataset or visualization parameters. To guarantee readability and efficient use of space across datasets, these static breakpoints would require manual updates. Constraint-based breakpoints solve this by evaluating visualization-specific constraints on the size of visual elements, overlapping elements, and the aspect ratio of the visualization and available space. Once configured, a responsive visualization with constraint-based breakpoints can adapt to different screen sizes for any dataset. We describe a framework that guides designers in creating a stack of visualization designs for different display sizes and defining constraints for each of these designs. We demonstrate constraint-based breakpoints for different data types and their visualizations: geographic data (choropleth map, proportional circle map, Dorling cartogram, hexagonal grid map, bar chart, waffle chart), network data (node-link diagram, adjacency matrix, arc diagram), and multivariate data (scatterplot, heatmap). Interactive demos and supplemental material are available at responsive-vis.github.io/breakpoints.

Assessment of Disparity in Accessing Railway Stations in Thailand: an Application Geographic Information System Network Analysis

Ensuring that people from all regions in a country can easily access railway stations is a crucial objective in the development of transportation and mobility systems and is essential for the convenient travel within the country. Furthermore, accurately assessing accessibility in disadvantaged areas is pivotal in designing and implementing development policies for equitable and sustainable transportation systems. Although various methods and tools are available for measuring accessibility, interpreting local-level data remains challenging. This study was conducted in Thailand, a country with severe accessibility disparities but undergoing considerable development. Therefore, the objective of this study was to evaluate accessibility disparities to analyze accessibility capabilities and inequality of railway stations. The objective of the study was to aid the development of inequality and railway services to cover all areas. The geographic information system methodology with network analysis tools was used to visualize and analyze spatial accessibility data and identify regional disparities. The results of this study indicate inequality in accessing railway services, which revealed that densely populated areas have superior access to railway stations compared with districts with a lower population density. Additionally, over 41% of the study area’s population resides more than half an hour away from access to a railway station. This study highlights regional inequality in accessing railway station services. Furthermore, strategies to alleviate this inequality are presented, and the findings will aid government agencies and railway planners in designing critical strategies or formulating policies for improving accessibility to rail transportation systems and reducing inequality in accessing railway services.

The segregation of physician networks providing care to black and white patients with heart disease: Concepts, measures, and empirical evaluation

Black-White disparities in cardiac care may be related to physician referral network segregation. We developed and tested new geographic physician network segregation measures. We used Medicare claims to identify Black and White Medicare heart disease patients and map physician networks for 169 hospital referral regions (HRRs) with over 1000 Black patients. We constructed two network segregation indexes ranging from 0 (integration) to 100 (total segregation): Dissimilarity (the unevenness of Black and White patient distribution across physicians [Dn]) and Absolute Clustering (the propensity of Black patients’ physicians to have closer ties with each other than with other physicians [ACLn]). We employed conditional logit models to estimate the probability of using the best (lowest mortality) geographically available hospital for Black and White patients undergoing coronary artery bypass grafting (CABG) surgery in 126 markets with sufficient sample size at increasing levels of network segregation and for low vs. high HRR Black patient population. Physician network segregation was lower than residential segregation (Dissimilarity 21.9 vs. 48.7, and Absolute Clustering 4.8 vs. 32.4) and positively correlated with residential segregation (p < .001). Network segregation effects differed by race and HRR Black patient population. For White patients, higher network segregation was associated with a higher probability of using the best available hospitals in HRRs with few black patients but unchanged (ACLn) or lower (Dn) probability of best hospital use in HRRs with many Black patients. For Black patients, higher network segregation was not associated with a substantial change in the probability of best hospital use regardless of the HRR Black patient population size. Measuring physician network segregation is feasible and associated with nuanced effects on Black-White differences in high-quality hospital use for heart disease. Further work is needed to understand underlying mechanisms and potential uses in health equity policy.

SPATIO-TEMPORAL ANALYSIS OF URBAN ECONOMIC RESILIENCE DURING COVID-19 WITH MULTILAYER COMPLEX NETWORKS

Abstract. The COVID-19 pandemic has impacted the economic growth of almost every country, with many industries facing operational difficulties, and the failures of a large number of restaurants, in particular, have extensively tested the resilience of urban economies. The gastronomy business is one of the most decentralized and location-based consumer business in urban, which is highly related to the economic attributes of cities. However, there are few studies on quantitative analysis of urban economic resilience through the opening and closing of restaurants. Understanding and planning for the aftermath of the COVID-19 may not only minimize detrimental effects but also provide insights into the economic recovery policies. This study analyzes the phenomenon of restaurant failures after the pandemic in Shenzhen, China via percolation in multilayer complex networks. We identify the closed restaurants through data mining, and construct the human mobility network through mobile phone location data, aggregating origin and destination points into grids. We then embedded the restaurants’ Points of Interest (POIs) into the grids, creating an additional restaurant network layer. By considering spatial interactions between restaurants, we constructed a geographical proximity network for restaurants in each grid. Finally, Using these multilayered nested networks, we analyzed the pandemic’s impact and the occurrence of critical phenomena related to restaurant closures under lockdown policies through percolation in multilayer complex networks. As a result, this study found that the severity of the pandemic significantly increased the probability of restaurant failures, with cascade and critical phenomena. However, implementing precise lockdown measures can effectively lower the probability of restaurant closures. These results highlight the effectiveness of accurate lockdown policies in striking a balance between epidemic prevention and economic development, contingent upon the correct identification of high-risk areas. This finding suggests that policy makers and public health departments need to balance policy effectiveness with interventions in economic activities in order to increase the resilience of urban economies during the pandemic.

An analysis of the context factors influencing the diverse response of airports to COVID-19 using panel and group regression

Since 2020, the ongoing impact of the COVID-19 pandemic has led to various levels of recovery across different aviation markets. To better plan for the future, post-pandemic era, air transport policies should consider the potential differential responses of airports to the pandemic based on their geographic network and competitive situations. Much literature provides policy recommendations for planning and development in the post-pandemic era, however fewer studies focus on the short-term recovery trends and diverse responses to the pandemic (that were) made by airports. This paper introduces four impact dimensions faced by airports as a result of the pandemic: airport operations, geographical location, socio-economic environment, and government pandemic policies. Panel and group regression were employed to explore the factors associated with airports’ response to the pandemic in relation to the four dimensions. The seat capacity at the airports was forecast for a non-pandemic scenario using ARIMA models. The residual ratios between the predicted and the actual seat capacity values were the dependent variables capturing the pandemic effect. The results show that, in addition to the direct effect of the pandemic policy, the location of the airport (and other transport modes in its vicinity) had a significant impact on its short-term recovery. Further, the extent of the impact varied by region. The findings of the correlation between different factors and airport responses provide policymakers with more targeted recovery strategies. At the macro-scale, the analysis of multidimensional factors suggests recommendations for airlines and planners for a faster and more efficient recovery of the aviation networks for future disruptions.

Clocking the Processes and Challenges of Urgent Red Blood Cell Exchange for Acute Complications of Sickle Cell Disease

Background: Red blood cell exchange transfusion (RBCX) is the therapy of choice for some acute complications of sickle cell disease (SCD), including stroke, severe acute chest syndrome and multi-organ failure. Rapid initiation of RBCX is critical to reduce morbidity and mortality in those with acute complications of SCD. A typical RBCX procedure requires anywhere from 6-10 units of packed red blood cells (RBC) and measures are taken to provide higher-fidelity RBC matches (key RHCE and KEL antigens at a minimum, ±suitable KIDD, FY, Ss profiles for the sensitized). Our recent review of urgent apheresis transfers to our tertiary-care centre, including 26 for RBCX, demonstrated that the median time per transfer was approximately 4 hours. Not yet characterized is the additional time required to start RBCX once a patient arrives. The time required to investigate antibody repertoires, and to procure sufficient quantities of blood that can bypass these specificities, is of interest in a specialty center with substantial but not unlimited investigative expertise and inventory. Methods: This was a retrospective database and chart review of all patients referred to our apheresis service for urgent RBCX for severe complications of SCD between Jan 1, 2013 to Dec 31, 2020. Time stamps for admission, blood bank, and apheresis processes were extracted from the electronic medical record and blood bank database. Categorical variables were analyzed using nonparametric tests. Results: Thirty-eight patients were included (65.8% males, 35.2% females) (Table 1). HbSS was the most common SCD genotype represented (81.6%). Most patients (71.1%) were transferred from another hospital, with the remaining patients arriving through the emergency department (26.3%) and one (2.6%) as a direct admission from clinic. ACS +/- additional complications constituted the most common indication for RBCX (78.9%). Two patients died due to complications of multi-organ failure, the remaining patients were discharged from hospital. Six patients (15.8%) had a positive antibody screen. The majority had RBC phenotype (42.1%) or genotype +/- phenotype (36.8%) on file to facilitate extended matching, and sufficient appropriate units were available in the blood bank in 82% of cases. Four (10.5%) patients required top-up transfusions prior to RBCX due to significant anemia. The median number of units issued per procedure was 9 (IQR 2). The median times from index group and screen (G&amp;S) collection to sample accession, and from accession to result were 23 minutes (IQR 26) and 46 minutes (IQR 31), respectively (Figure 1). The median time from G&amp;S result to RBC units issued was 348.5 minutes (IQR 479). The median total time from patient admission (or determination of RBCX indication) to RBC units issued was 569 minutes (IQR 464). A positive antibody screen, patient origin (ED vs. transfer), and the availability of sufficient RBC units in house were associated with turnaround time from accession of the index G&amp;S sample to the issuing of RBCs (p&amp;lt;0.01 for all). Patients having antibodies to high prevalence antigens (e.g. anti-U) or to multiple targets (demanding unique donor profiles) had some of the longest times to initiate treatment. These constraints necessitated orders and coordinated deliveries of units from across the geographic network of the national blood provider.Discussion: These data from the largest apheresis unit in the country characterize the time invested in blood preparation for an urgent RBCX for acute SCD complications. A positive antibody screen is predictive of increased time to procedure start, reflecting delays inherent to serologic investigations, product sourcing, and serologic crossmatch times. While the precise moment of a blood request was available for a minority of patients, the G&amp;S time stamp was used as a surrogate, thus potentially overestimating turnaround times in the blood bank. Nevertheless, the serologic demands inherent to a case present a tension between speed (treating the disease) and safety (averting immune rejection reactions).

Rooted America: Immobility and Segregation of the Intercounty Migration Network

Despite the popular narrative that the United States is a “land of mobility,” the country may have become a “rooted America” after a decades-long decline in migration rates. This article interrogates the lingering question about the social forces that limit migration, with an empirical focus on internal migration in the United States. We propose a systemic, network model of migration flows, combining demographic, economic, political, and geographic factors and network dependence structures that reflect the internal dynamics of migration systems. Using valued temporal exponential-family random graph models, we model the network of intercounty migration flows from 2011 to 2015. Our analysis reveals a pattern of segmented immobility, where fewer people migrate between counties with dissimilar political contexts, levels of urbanization, and racial compositions. Probing our model using “knockout experiments” suggests one would have observed approximately 4.6 million (27 percent) more intercounty migrants each year were the segmented immobility mechanisms inoperative. This article offers a systemic view of internal migration and reveals the social and political cleavages that underlie geographic immobility in the United States.

Graph Theory and Geography: From Collaboration to Autocorrelation

Theoretical, methodological, and empirical contributions regarding networks within the context of graph theory constitute a valuable interface between geography and mathematics. Seeking to assess mathematical network geography’s past, present, and future, this article addresses such research and literature by discussing new knowledge produced from networked geographic phenomena—especially Erdös collaboration graphs depicting mentorships or authorships among scholars; outlining a brief history of networks in geographical analysis; and couching spatial autocorrelation within its geographic network links. General commentary transitions into examples of Erdös graphs and their implications for collaborations. The narrative also speaks to networked space challenges of established geographic thought (e.g., Tobler’s first law of geography), with special reference to contributions by Boots and his coauthors. This article concludes with a treatment of graph theory, networks, and spatial autocorrelation.

Dynamic analysis of modified SEIR epidemic model with time delay in geographical networks

Dynamic analysis of modified SEIR epidemic model with time delay in geographical networks

The international transmission of local economic shocks through migrant networks

Using newly validated data on geographic migration networks, we study how labor demand shocks in the United States propagate across the border with Mexico. We show that the large exogenous decline in US employment brought about by the Great Recession affected demographic and economic outcomes in Mexican communities that were highly connected to the most affected markets in the US. In the Mexican locations with strong initial ties to the hardest hit US migrant destinations, return migration increased, emigration decreased, and remittance receipt declined. These changes significantly increased local employment and hours worked, but wages were unaffected. Investment in children’s education also slowed in these communities. These findings document the effects in Mexico when potential migrants lose access to a strong US labor market, providing insight into the potential impacts of stricter US migration restrictions.