Relocation Decisions Research Articles

Precious Neighbors: The Value of Co-Locating with the Government

Abstract In many countries, disproportionately many firms locate their headquarters in the capital city. Spatial proximity to a country’s leading politicians may be beneficial for a number of reasons. Since neither firms nor governments move randomly, the effects of firms’ co-locating with the government are normally hard to identify. I solve this problem by examining a unique event—the decision to relocate the German federal government from Bonn to Berlin in 1991. Following reunification, there was a free vote in the German parliament on the future location of the government. Berlin won by a narrow margin, an event that could not be anticipated even days before and that is free from confounding factors. Firms with corporate headquarters in Berlin experience abnormal equity returns of more than 3% following the relocation decision.

Impacts of New Light Rail Transit Service on Riders’ Residential Relocation Decisions

Using the rider survey data collected from Hudson-Bergen Light Rail Transit System in New Jersey, this paper investigated the residential relocating decisions of the riders who have been riding the LRT for a period of time. Using the Heckman’s sample selection model, the paper extends the current mobility literature by describing not only a rider’s likelihood to move as a result of the new LRT service but also the movers’ orientation toward their residence distances to LRT stations. Information on the socio-economic characteristics of the movers and their residence distances to LRT stations would help planners and developers identify areas where housing growth associated with specific characteristics of the riders will occur, and plan for these areas to provide affordable housing and amenities for relocating residents.

A rolling-horizon decision framework for integrating relocation and user flexibility in one-way electric carsharing systems

One-way electric carsharing system has become the most widely used form of carsharing service in practice. However, the demand uncertainty, car imbalance, parking shortage, and battery charging jointly impose great challenges upon the operation. Many carsharing operators, e.g., GoFun, offer incentives to flexible users for accepting alternative itineraries and also adopt a full reservation policy. In this paper, we propose a rolling-horizon decision framework that integrates the operator’s car and staff relocation decisions, user flexibility, and charging of electric cars in the real-time setting. We formulate the problem at each decision epoch as a time–space network flow model that simultaneously optimizes the decisions. We develop an iterated local search heuristic algorithm to solve the model for large-scale practical problems. Based on theoretical insights and clustering of stations, we develop new local search procedures customized for the dynamic carsharing problem. We use real-world data from GoFun as an example to conduct extensive computational experiments. The results show that our algorithm significantly outperforms a particle swarm optimization algorithm and the popular greedy policy, and also has a low efficiency loss compared to the off-line heuristic solution. The transportation network size, clustering size, and operational efficiency are revealed to have significant impacts on the profit and service level.

The impact of hurricanes and floods on domestic migration

Migration is a possible adaptation to climate change. We employ FEMA disaster declaration data and American Community Survey data to study household post-disaster migration choices. We find that natural disasters increase households’ propensity to migrate both out of their county but within their greater metropolitan area, as well as out of their greater metropolitan area, by up to one percentage point. However, low-income households are especially less likely to move following disasters associated with less FEMA aid. We also find that disaster-affected migrants favor safer destinations. While migrating households appear to factor disaster risk reduction into relocation decisions, the results imply the need for incentivizing and aiding migration for vulnerable populations who are less likely to do so on their own.

Post-Brexit: Do board interlocks make banks take similar relocation decisions?

Abstract Because Brexit has implied a surge of relocation decisions by financial service firms during a short period of time, we examine the locational decisions of the financial industry in Europe. Adopting a relational perspective we analyze the association between similar relocation decisions of UK-based banks and the connectivity of their decision boards. Based on an analysis of relocation announcements in the media, press releases, and annual reports, as well as of interlocking directorships within the financial sector, our study connects research streams on relocation and internationalization with cross-board memberships and interlocks. Our findings suggest that the higher the competition between two banks and the stronger the connectivity in interlocking board memberships between them, the more likely are these banks to announce different relocation decisions. We interpret these robust findings as a behavior that effectively reduces competition for limited localized resources.

Disaster-risk communication, perceptions and relocation decisions of rural residents in a multi-disaster environment: Evidence from Sichuan, China

Existing studies on disaster-risk management from the humanistic perspective have focused on disaster-risk perception as the starting point and ignored the important role of risk communication in shaping individual risk perception and changing behavioural responses. Taking Sichuan Province―a typical disaster-prone province in China―as an example and selecting rural residents in mountainous areas threatened by multiple disasters as interviewees, this study measured the characteristics of interviewees’ disaster-risk communication in the four dimensions of content preference, channel selection, communication frequency and communication form, and appraised the levels of their disaster-risk perceptions in the four dimensions of possibility, threat, self-efficacy and response efficacy. Additionally, econometric models were used to explore the chain of disaster-risk communication, perceptions and relocation decisions in different scenarios in the context of multiple disasters. The results revealed two main findings. (1) In the scenario where social relations promoted the relocation decision, interviewees derived their relocation decisions by two action paths: disaster-risk communication indirectly influenced relocation decisions through risk perception and disaster-risk communication directly influenced relocation decisions. For the indirect action path, the disaster-risk communication of interviewees had a significant impact on their risk perceptions, and self-efficacy and response efficacy played effective roles in their relocation decisions. For the direct action path, some channels of access to information and indicators of the communication form were significantly correlated with the relocation decision. (2) In the relocation-decision scenario promoted by the government, interviewees derived their relocation decision only by their disaster-risk communication. In risk communication, some channels of access to information and the communication forms of residents were significantly correlated with their relocation decisions, while the role of disaster-risk perceptions was ineffective.

Exploring the dynamics of neighbourhood ethnic segregation with agent-based modelling: an empirical application to Bradford, UK

ABSTRACT How individuals’ residential moves translate into overall emergent segregation patterns remains a key challenge in neighbourhood ethnic segregation research. In this paper, we use agent-based modelling to explore this concern, focusing on the interactive role of ethnic and socio-economic homophilic preferences and socioeconomic housing constraints as determinants of residential choice. Specifically, we extend the classic Schelling model to a random utility discrete choice approach to simulate the relocation decisions of people. We model different weights for preferences for ethnic and socioeconomic similarity in neighbourhood composition over random relocations, in addition to housing constraints. We formalise how different combinations of these variables could replicate empirically observed ethnic segregation scenarios in Bradford, a substantially segregated local authority in the UK. We initialise our model with geo-referenced data from the 2011 Census and use various measures of segregation to describe our results. As in the original Schelling model, we find that even mild ethnic preferences alone would lead to unrealistic ethnic over-segregation in Bradford. However, we demonstrate that such process can be altered in favour of less ethnic segregation when agents’ preferences for socioeconomic similarity are slightly stronger than their preferences for ethnic similarity. We discuss theoretical and policy contributions of our findings.

Serving Two Masters: Conflicts of Interest in Intermediary HQ Relocation Decisions?

How do intermediary headquarters (IHQs) deal with changes in the scope of the multinational corporation (MNC) and in country conditions? To investigate this question, we created a unique panel dataset of 386 IHQ cross-border relocations in Europe between 2000 and 2019. Drawing on extant literature on MNC structures and HQ location choices, we argue that IHQs’ special role as ’hybrid’ units with a middle position between the subsidiaries and the corporate parent puts (potentially) opposing pressures on IHQs. We hypothesize that increasing distance between the IHQ and the frontline subsidiaries as well as between the IHQ and the corporate parent is detrimental for IHQs. We also suggest that IHQs are especially pulled towards countries with highly developed institutions. We find that higher distance between the IHQ and the subsidiaries is related to a higher likelihood of IHQ relocations and that IHQs relocate to countries with a higher institutional quality than their previous host country. In contrast, the distance between the IHQ and the corporate parent does not influence IHQ relocation decisions. With those findings, we contribute to literature on the antecedents of HQ relocations, IHQ structures and configurations in MNCs, and intra-firm networks.

Forecasting worldwide empty container availability with machine learning techniques

Due to imbalances in the global transport of containerised goods, liner shipping companies go to great lengths to match the regional supply and demand for empty containers by transporting equipment from surplus to deficit regions. Making accurate forecasts of regional empty container availability could support liner companies and other involved actors by making better relocation decisions, thus avoiding unnecessary transport costs of empty equipment. Previously proposed container availability prediction models are limited to the application in individual regions and typically characterized by a high degree of temporal aggregation. Against this background, this paper introduces two novel approaches based on machine learning and probabilistic techniques to predict the future weekly availability of empty containers for more than 280 locations worldwide. The machine learning and probabilistic prediction models are built by analysing a unique data set of more than 100 million events from past container journeys. These events represent different stages during the transport process of a container. Both models use a two-step forecast logic. First, the expected future location of a container is predicted. Second, the expected timestamp for arriving at that location is estimated. The machine learning model uses artificial neural networks and mixture density networks to forecast the movements of containers. The models are quantitatively assessed and compared to the actual availability of containers and two more conventional forecasting approaches. The results indicate that the probabilistic prediction approach can keep up with conventional approaches while the neural network approach significantly outperforms the other approaches concerning every evaluation metric.

A hybrid fuzzy-AHP-TOPSIS model for evaluation of manufacturing relocation decisions

Manufacturing relocation decisions are complex because they involve combinations of location modes like offshoring or reshoring, and governance modes like insourcing or outsourcing. Furthermore, the uncertainty involved in the decision-making process makes it challenging to reach a right-shoring decision. This study presents a hybrid fuzzy-AHP-TOPSIS model to support generic relocation decisions. Industry experts were involved in a pairwise comparison of the competitive priorities’ decision criteria. A meta-synthesis of empirical studies is used to generate theoretical relocation scenarios. The presented hybrid model is used to rank the relocation scenarios in order to identify the most pertinent alternative. The resiliency of the solution is presented through a sensitivity analysis. The results indicate that the proposed hybrid model can simultaneously handle all the main relocation options involving governance modes. Based on the input data in this study, the competitive priorities criteria quality, time and cost are shown to have a strong impact, whereas the sustainability criterion has a weak impact on the choice of relocation option. The research presented in this paper contributes to the research field of manufacturing relocation by demonstrating the suitability of the hybrid fuzzy-AHP-TOPSIS model for relocation decisions and the resilience of the results. Furthermore, the research contributes to practice by providing managers with a generic relocation decision-support model that is capable of simultaneously handling and evaluating various relocation alternatives.

Perceptions of Local Leaders Regarding Postdisaster Relocation of Residents in the Face of Rising Seas

Despite the growing literature on sea level rise (SLR), the current understanding of how SLR risks influence postdisaster relocation remains limited. This paper addresses this knowledge gap by examining how local leaders (i.e., public officials and community leaders) perceive: (a) resident relocation decisions in a disaster-affected community that is also vulnerable to SLR; and (b) the role of SLR in residents’ relocation decisions. Based on the case of Monroe County, Florida, which was affected by Hurricane Irma in 2017, our findings suggest that local leaders perceive residents’ relocation decisions as being driven by predisaster challenges that were exacerbated by conditions in the aftermath of the hurricane—specifically: the lack of affordable housing, low wages, and high cost of living. Leaders believe that SLR-related risks have little/no direct influence on relocation decisions; instead, they suggest that the community’s focus is on the next storm and community members’ short-term needs.

Willingness-to-relocate: Examining preferences for parking relocation of privately-owned automated vehicles

Privately-owned automated vehicles (PAVs) can relocate themselves elsewhere after arriving at destinations, thereby inducing empty vehicle mile traveled (VMT) and more greenhouse gas emissions. This paper examines travelers’ preferences for PAV relocation, using stated preference surveys distributed in the Seattle and Kansas City regions in the U.S. Model results suggest that trip purpose, individual socio-economic and household characteristics, and local contexts influence PAV relocation decisions. Additionally, a willingness-to-relocate (WTR) metric is defined to represent how much time travelers would be willing to spend to relocate PAVs to save $1 in parking costs. The WTR estimates in both regions indicate the potential of induced VMT due to PAV parking relocation. Furthermore, travelers’ awareness of fuel/energy costs associated with PAV relocation does not necessarily relate to a lower WTR, depending on the region. To curb excessive empty VMT from PAV relocation, explicit disincentives, such as a VMT fee, may be needed.

Herd behaviour and the emergence of clusters

ABSTRACT Agglomeration externalities are generally credited as the prime cause of cluster formation. This theory is far from uncontroversial. We create an alternative perspective on clustering by integrating herd behaviour into the conceptualization of this process. An innovative agent-based model is used in which cluster formation is constructed from individual relocation decisions. It thus connects individual relocation decisions to macro-level emergent patterns and shows that imitation has the potential to severely exacerbate the effects of agglomeration economies on clustering on an urban scale. The outcomes of this model challenge the dominant neoclassical view on clustering. An alternative hypothesis is postulated in which the interaction between herd behaviour and agglomeration externalities is proposed as an explanation for cluster formation.

Mobility of Top Income Taxpayers in Response to Regional Differences in Personal Taxes: Evidence from Spain

Abstract The study empirically tests whether regional differences in personal taxes (Personal Income Tax, Wealth Tax, and Inheritance and Gift tax) have had some influence on the decision of the richest Spanish taxpayers to change their residence. The estimates use the Personal Income Tax (IRPF) Panel database provided by the Ministry of Public Finance. The study offers evidence that regional tax differences affect the decision to change residence of the top income taxpayers and also that this decision is also affected by other attractiveness and opportunity factors offered by the regions, and by certain characteristics of the individuals. However, the marginal effects are very slight, except for the coefficient of the variable showing regional differences in IRPF: If the difference between average regional IRPF rates increases by 10 percentage points in favour of the other regions, the probability of a top 1% taxpayer changing its residence increases by 11.2 percentage points. The study also documents the importance of the Community of Madrid in the relocation decisions of rich taxpayers. Finally, the study finds that the behaviour of taxpayers aged 65 years and over in the top 1% does not seem very different from that of all taxpayers in the same income bracket.

Relocation Decisions in Uncertain Times: Brexit and Financial Services

abstract This article examines the impact of uncertainty and profound political, economic, and regulatory changes on the process of geographic reorganization of the financial industry in the course of Brexit. It draws on historic lessons of massive relocations within the financial industry in Europe to conjecture three scenarios: (1) concentrated relocation to build a new European lead financial center (FC), (2) least necessary relocation to meet regulatory requirements for operation in the EU single market, and (3) selective relocation and cumulative functional specialization of regional FCs. Drawing on official statistics, corporate and media reports, as well as on qualitative interviews and participant observation in the field, we build an original database of published plans and confirmed practices of geographic reorganization. Our analysis of the relocations of financial service firms from London to five leading FCs on the European continent supports the least necessary relocation as well as selective relocation scenarios. We conclude that Brexit-induced reorganization actually reproduces the existing geographic architecture while simultaneously deepening the divisions of labor among the established European FCs.

Residents' and Family Perspectives of Involuntary Healthcare Facility Closure and Relocation

AbstractOlder adults who receive care in a healthcare facility certified by the Centers for Medicare and Medicaid Services receive services mandated and regulated by state and federal policy to ensure that basic quality and safety standards are met. Termination of certification due to ongoing care deficiencies results in the involuntary relocation of residents receiving Medicare or Medicaid services to another healthcare facility. If a facility ultimately closes, all residents are displaced. Facility decertification happens infrequently, but when it does occur, it affects both residents and their families. Few studies have examined resident input and the role that family plays in the relocation process. The purpose of this study was to explore residents' and families' perceptions of the relocation process and its effect on residents' health and wellbeing. The study sample included 27 residents and 93 family members from two facilities that lost their certification. Semi‐structured, audio‐recorded interviews conducted with participants either in‐person or by telephone focused on perceptions of relocation processes, relocation challenges and stressors, and physical and mental effects on residents. Interviews were transcribed verbatim and analyzed using an open coding process to identify common themes and patterns in the data. Four interrelated, overarching themes emerged from the analysis: 1) awareness of need to relocate; 2) notification of need to relocate; 3) involvement in relocation decisions; and 4) relocation stressors. Neither residents nor families were satisfied with the relocation process and offered multiple suggestions to guide policy and practice to improve their involuntary relocation experiences.

How does Industry 4.0 affect international exposure? The interplay between firm innovation and home-country policies in post-offshoring relocation decisions

This paper investigates whether and how Industry 4.0 innovation intensity, which is developed through patenting and which provides a firm-specific ownership advantage, relates to “relocation of second degree” (RSD) of manufacturing activities, i.e. the movement of previously offshored activities to either the home country (RHC) or a third country (RTC), thus representing a reconfiguration of the firm’s extant international exposure. Moreover, we analyse the role of Industry 4.0 policies adopted in the home country, which offers a location advantage that can possibly moderate this relationship. Our findings, based on a sample of 118 RSDs implemented by European companies, reveal that both Industry 4.0 innovation intensity (at the firm level) and policies (at home-country level) have an impact, with the former pushing firms towards RTC, unless the latter are in place at the home country, thus showing the pivotal role of Industry 4.0 policies in re-attracting Industry 4.0 innovative companies in their country of origin.

Lagrangian dual decomposition for the ambulance relocation and routing considering stochastic demand with the truncated Poisson

The pre-hospital Emergency Medical Service (EMS) provides the critical care to the ill or injured patients, and evaluates and manages those patients at scene before their transport to an emergency medical facility. The Time to Arrive at Hospital (TAH) is a useful performance measurement defined as the time interval from the dispatch of an ambulance until the arrival of the patient at the destination facility. By taking into consideration of the short-term demand estimation, there is chance to improve the management of ambulances and reduce the TAH. This study proposes a new stochastic programming model to minimize the TAH within a complete dynamic relocation system. In this system, a truncated Poisson distribution is utilized for forecasting near future EMS requests, and a Lagrangian dual decomposition with branch-and-bound is adapted as the solution methodology. By dynamically generating near future scenarios for the planning of ambulance relocation among bases, we obtain close-to-real-time ambulance relocation decisions. Scenarios collected from New Taipei City, Taiwan have shown that the proposed system has the potential to enhance the performance of the pre-hospital EMS.

Shared autonomous vehicle management considering competition with human-driven private vehicles

Shared autonomous vehicles (SAVs) are expected to be an essential component in developing efficient and sustainable transportation systems. This paper focuses on optimizing the operational decisions of SAV systems when the mode choices between SAVs and human-driven private vehicles are considered. A time–space network flow model is formulated to optimize the vehicle assignment and relocation decisions, while a binary logit model is used to describe the nonlinear relationship between the elastic demand and its various important attributes, including the trip price and the level of service. An enhanced outer–inner approximation approach is proposed for solving the mixed-integer nonlinear programming (MINLP). For reducing the approximation error, a dynamic programming algorithm is developed to select the optimal breakpoints. The proposed modeling and solution approaches are tested based on a case study in Singapore. Our computational experiments show that the proposed solution approaches can efficiently obtain the optimal solutions. Numerical results reveal that a SAV system can achieve higher performance by the active relocation activities under the elastic demand. And the SAV system with a high level of service could produce a higher operating profit if users are more sensitive to the level of service.

Machine Learning vs Conventional Specifications for Housing Relocation Decisions

Machine Learning vs Conventional Specifications for Housing Relocation Decisions