How Armed Conflict Influences Migration

Williams, Nathalie E.; O’Brien, Michelle L.; Yao, Xiaozheng

doi:10.1111/padr.12408

Cited by 7 publications

(3 citation statements)

References 68 publications

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“…While this is somehow our ‘least negative’ finding, it is well known that armed conflict has myriad other consequences on civilians, beyond immediate mortality. Physical and mental health traumas, displacement, consequences for reproductive health, family choices, and bereavement, to name a few, have been documented in many conflict-affected settings, including in relation to the First Karabakh War (Alburez-Gutierrez, 2022 ; Kerimova et al, 2003 ; Mavisakalyan & Minasyan, 2021 ; Murray et al, 2002 ; Torrisi, 2020 ; van Baelen et al, 2005 ; Williams et al, 2021 ). Additionally, many civilians as well as combatants remain wounded and suffer long-term disabilities.…”

Section: Discussionmentioning

confidence: 99%

The Casualties of War: An Excess Mortality Estimate of Lives Lost in the 2020 Nagorno-Karabakh Conflict

Karlinsky

Torrisi

2023

Popul Res Policy Rev

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Who and how many died in the 2020 Karabakh War? With limited evidence provided by authorities, media outlets, and human rights organizations, still little is known about the death toll caused by the 44-day conflict in and around Nagorno-Karabakh. This paper provides a first assessment of the human cost of the war. Using age–sex vital registration data from Armenia, Azerbaijan, and the de facto Republic of Artsakh/Nagorno-Karabakh, we difference the 2020 observed mortality values from expected deaths based on trends in mortality between 2015 and 2019 to offer sensible estimates of excess mortality resulting from the conflict. We compare and contrast our findings with neighboring peaceful countries with similar mortality patterns and socio-cultural background and discuss them against the backdrop of the concurrent first wave of Covid-19. We estimate that the war led to almost 6,500 excess deaths among people aged 15–49. Nearly 2,800 excess losses occurred in Armenia, 3,400 in Azerbaijan, and 310 in de facto Artsakh. Deaths were highly concentrated among late adolescent and young adult males, suggesting that most excess mortality was directly related to combat. Beyond the human tragedy, for small countries like Armenia and Azerbaijan, such loss of young men represents a considerable long-term cost for future demographic, economic, and social development.

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Section: Discussionmentioning

confidence: 99%

The Casualties of War: An Excess Mortality Estimate of Lives Lost in the 2020 Nagorno-Karabakh Conflict

Karlinsky

Torrisi

2023

Popul Res Policy Rev

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“…The conflict-migration nexus has existed long before the constitution of the 1951 Refugee Convention [37]. Theoretically, the threat-based decision model postulated that perceived (unbearable) threat to personal security is the main driver for people to migrate away from a conflict zone [38][39][40]. Empirical studies have reported high rates of out-migration during armed conflict, civil war, genocide and human right violations [39,41].…”

Section: Forced Migration: Processes and Driversmentioning

confidence: 99%

Modelling and predicting forced migration

Bircan

2023

PLoS ONE

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Migration models have evolved significantly during the last decade, most notably the so-called flow Fixed-Effects (FE) gravity models. Such models attempt to infer how human mobility may be driven by changing economy, geopolitics, and the environment among other things. They are also increasingly used for migration projections and forecasts. However, recent research shows that this class of models can neither explain, nor predict the temporal dynamics of human movement. This shortcoming is even more apparent in the context of forced migration, in which the processes and drivers tend to be heterogeneous and complex. In this article, we derived a Flow–Specific Temporal Gravity (FTG) model which, compared to the FE models, is theoretically similar (informed by the random utility framework), but empirically less restrictive. Using EUROSTAT data with climate, economic, and conflict indicators, we trained both models and compared their performances. The results suggest that the predictive power of these models is highly dependent on the length of training data. Specifically, as time-series migration data lengthens, FTG’s predictions can be increasingly accurate, whereas the FE model becomes less predictive.

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“…ABMs link individual-level dynamics to emergent population processes, and thus have been used in social sciences and population health to address a wide range of complex issues (Billari et al 2007; Bonabeau 2002; Grow and Van Bavel 2017; Silverman et al 2020). An incomplete list includes such a range of demographic topics as dynamic marriage markets (Bijak et al 2013; Billari et al 2007); the effects of family planning efforts on conserving panda habitat in China (An and Liu 2010); sex ratio at birth (Kashyap and Villavicencio 2017); population change after armed conflict in Nepal (Williams, O’Brien, and Yao 2017, 2021); migration and mobility (Hinsch and Bijak 2022); and fertility decline and economic growth (Karra, Canning, and Wilde 2017).…”

Section: Introductionmentioning

confidence: 99%

FPsim: An agent-based model of family planning

O’Brien

Valente

Kerr

et al. 2023

Preprint

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The biological and behavioral underpinnings of family planning (FP) unfold on an individual level, across a full reproductive life course, and within a complex system of social and structural constraints. Yet, much of the existing FP modeling landscape has focused solely on macro- or population-level dynamics of family planning. There is a need for an individual-based approach to provide a deeper understanding of how family planning is intertwined with individuals' lives and health at the micro-level, which can contribute to more effective, person-centered design of both contraceptive technologies and programmatic interventions. This article introduces the Family Planning Simulator (FPsim), a data-driven, agent-based model of family planning, which explicitly models individual heterogeneity in biology and behavior over the life course. Agents in FPsim can experience a wide range of life-course events, such as increases in fecundability (and primary infertility), sexual debut, contraceptive choice, postpartum family planning, abortion, miscarriage, stillbirth, infant mortality, and maternal mortality. The core components of the model - fecundability and contraceptive choice, are represented individually and probabilistically, following age-specific patterns observed in demographic data and prospective cohort studies. Once calibrated to a setting leveraging multiple sources of data, FPsim can be used to build hypothetical scenarios and interrogate counterfactual research questions about the use, non-use, and/or efficacy of family planning programs and contraceptive methods. To our knowledge, FPsim is the first open-source, individual-level, woman-centered model of family planning.

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How Armed Conflict Influences Migration

Cited by 7 publications

References 68 publications

The Casualties of War: An Excess Mortality Estimate of Lives Lost in the 2020 Nagorno-Karabakh Conflict

The Casualties of War: An Excess Mortality Estimate of Lives Lost in the 2020 Nagorno-Karabakh Conflict

Modelling and predicting forced migration

FPsim: An agent-based model of family planning

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