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Carla Anjos (University of Aveiro) Pedro Campos (Statistics Portugal and University of Porto)

The role of Social Networks in the projection of international migration flows: an Agent-Based approach. Carla Anjos (University of Aveiro) Pedro Campos (Statistics Portugal and University of Porto) Work Session on Demographic Projections - April, 29, 2010, Lisbon. Contents. Motivation, goals

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Carla Anjos (University of Aveiro) Pedro Campos (Statistics Portugal and University of Porto)

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  1. The role of Social Networks in the projection of international migration flows: an Agent-Based approach Carla Anjos (University of Aveiro) Pedro Campos (Statistics Portugal and University of Porto) Work Session on Demographic Projections - April, 29, 2010, Lisbon

  2. Contents • Motivation, goals • The context • Demography and migrations • Social Networks • The Multi-agent System • The Model • Variables • Gravitational Model • Simulation/Parameters • Results • Final Remarks

  3. Demography and Migrations • Population estimates (Comp. Method) • Pt = population at time t • Pt-1 = population at time t-1 • N = number of births between Pt-1 and Pt • M = number of deaths between Pt-1 and Pt • I = number of imigrants between Pt-1 and Pt • E = number of emigrants between Pt-1 and Pt

  4. Motivation • Population Projections • Need to elaborate social policies • Importance of studies in migration flows • More accurate demographic forecasts • Lack of information of migration flows • “New” approaches based on Agent-Based Computational Demography (ABCD) • bottom-up approach (Billari et al. (2003a); Billari and Prskawetz (2005))

  5. Macro Level Situacional Mechanism Transformational Mechanism Micro Level Mechanism of formation Interaction between social mechanisms Interaction between social mechanisms - Billari e Prskawetzy (2005)

  6. Main goals • Verify the effect of the structure of social networks on the migration flows • Social network analysis • Density • Degree centralization • Input • Output • General

  7. Social Networks Relationships and individuals • Agents or actors – “vertices” • Graph theory • Organized within a society • Well defined structure (or not?) • A set of units • Social • Economic • Cultural • Links between individuals • Oriented – “arcs” • Directed transmission of something (goods, services,information). • Non oriented– “links” • Undirected links between pairs of agents

  8. Indicators of Social Networks • Agents • Degree – Number of adjacent agents • Non oriented networks  Total number of links • Oriented networks: • Indegree – number of links received that an agent “receives” • Outdegree – number of links received that depart from an agent • General – number of adjacent agents (total Indegree+Outdegree) • Networks • Density • Proportion between the number of existent links and the number of possible links among all the agents • More links  More cohesion Estrutura  Higher denisy • Degree centralization • Evaluates the structure of the communication in the network • More variation in agents centrality  More centralized networks • Indegree,Outdegree, General

  9. Multi-Agent Systems • Agent • Entity that lives in a certain environment, having the capacity to interact with other agents • Characteristics: • Action and interaction • Agents interact with other agents and with the environment • Communication • Individual goals and autonomy • Agents are oriented towards specific goals • (Limits of) Perception • “Limited Racionality” – Limited computational resources

  10. Our study: the Variables

  11. Gravitational Model, Ma • Migration Level (ML) • If ML is greater than the value Ma, then the agent remains in the country of origin. Otherwise, the agent will migrate or stay in U.S. We assumed that three different levels of ML may occur (low, medium and high). These values are defined as 1,5, 4,0 and 5,0 respectively Ma= propensity of an agent to migrate CM – Migration cost Fm –Force of migration PM - Propensity to migrate

  12. Gravitational Model fEUA - per capita income of USA h – Geographical distance between two countries fO - per capita income of the country of origin U(0,5;0,9)  From the Country of origin to USA U(0,1;0,4)  From USA to country of origin

  13. Gravitational Model Fm – Force of migration ma – Agente mass MN - Mass of social network d – Average distance between agents G = 1

  14. Gravitational Model da – average distance between agents ma – Agent’s mass MN – mass of the social network

  15. The data • IPUMS (Integrated Public Use Microdata Series, Ruggles et al, (2009)) • The extracted database contains data of migration flows to the United States between 2001 and 2008. • Four communities in the U.S. were considered with origin in four different countries (Portugal, Mexico, China and Germany)

  16. Parameters of the simulation • Countries • Germany • China • Mexico • Portugal • Three different continents • Different terrritorial and social dynamics • Different development stages • Different migration flows • migrantes have different characteristics in the USA

  17. Parameters of the simulation • Initial considerations • The majority of the individuals migrate to the communities created by other individuals of the same nationality. • Simulated population is proportional to the population in database IPUMS • Individuals are created within the scope of three clusters that were found in the original population • Simulação: 2000 to 2008

  18. Simulation • 2000 • Agents are created (respecting the clusters found in IPUMS) • 2001 to 2008 • Ageing of agents in USA • Agents decide their situation as migrants • Creation of potential new migrants according to original migrants • Agents decide to migrate to USA or to stay in their country of origin • Three different scenarios (with 15 runs in each) • Simulation I (ML=1.5) • Migration level is Low, number of agents is high • Simulation II (ML=4.0) • Migration level is medium, low number of agents • Simulation IIII (ML=5.0) • Migration level is high, low number de agentes

  19. Validation • Stability of the model according to the variability of the means in the 15 runs • Simulated data are similar to reality for the following variables: * Wilcoxon test, p<0,05

  20. Density and Centrality degree

  21. Density Mexico – Simulation I Ano 2008, n = 2476 Ano 2000, n = 404

  22. Final Remarks • Trends between 2000 and 2008 • Variables • Number of individuals in household and age have different trens when comparing simulated to real data • Income and working condition are similar for some scenarios • Density • The greater the diameter of the networks, tjhe lower the density • Links disappear • Centralization • Indegree – the importance of the arrival of information to the agents in the network is high in the first periods, and stabilizes in the following. • Agents in USA are important to the arrival of new agents • Outdegree – the importance of the information that leaves from every agent decreases during the period • Os agentes nos EUA tendem a perder a sua ligação aos outros agentes da rede • General - has the same trend as indegree • In general, the communicaton in the network is higher in the first years and stabilizes subsequently

  23. Limitations and further work • The model is not able to preview the trend of evolution of the main variables in the simulation • It should be important to introduce a calibration procedure in a intermediate period (2004?) • The structure of the networks is important has some influence in the flow of migrants

  24. Some references • Billari, F. C., F. Ongaro, et al. (2003a), "Introduction: Agent-Based Computational Demography", in Agent-Based Computational Demography: Using Simulation to Improve Our Understanding of Demographic Behaviour, F. C. Billari e A. Prskawetz (editores), Contributions to Economics, pp.1-15, Heidelberg: Physica- Verlag. • Billari, F. C., A. Prskawetzy (2005), "Studying Population Dynamics from the Bottom- Up: The Crucial Role of Agent-Based Computational Demography", International Union for the Scientific Study of Population XXV International Population Conference, Tours, France. • Carrilho, M. J. (2005), "Metodologias De Cálculo Das Projecções Demográficas: Aplicação Em Portugal", Revista de Estudos Demográficos, Vol. 37, pp. 5-24.

  25. The role of Social Networks in the projection of international migration flows: an Agent-Based approach Carla Anjos (University of Aveiro) Pedro Campos (Statistics Portugal and University of Porto) Work Session on Demographic Projections - April, 29, 2010, Lisbon

  26. IMPORTÂNCIA DAS REDES SOCIAIS NOS FLUXOS MIGRATÓRIOS:Aplicação de Sistemas Multi-agente Carla Anjos Mestrado em Análise de Dados e Sistemas de Apoio à Decisão Orientador: Doutor Pedro Campos Faculdade de Economia da Universidade do Porto Porto, 15 de Março de 2010

  27. Migração • “Deslocação de uma pessoa através de um determinado limite espacial, com intenção de mudar de residência de forma temporária ou permanente. A migração subdivide-se em migração internacional (migração entre países) e migração interna (migração no interior de um país).” Instituto Nacional de Estatística (INE, (2003a))

  28. Redes sociais – Medidas Agentes • Grau (degree) • Redes não orientada • É igual ao número de vértices adjacentes • Redes orientadas: • Indegree - ligações que são recebidas pelo vértice • Outdegree - as ligações que saem do vértice • Geral - número de vértices adjacentes • Centralidade • Proporção entre o número de ligações do agentes e o número total de ligações. • Centralidade do grau (degree centrality) • Número de conexões directas de cada agente num grafo • Centralidade de proximidade (closeness centrality) • Medida do comprimento do caminho mais curto que liga dois agentes • Centralidade de intermediariedade (betweenness centrality) • Proporção de todos os caminhos geodésicos entre um par de vértices que incluem um determinado vértice, e o número total possível.

  29. Algorithm • Age(y) – if the age in year t (yt) • yt ≤ 94 then yt+1 = yt +1; • yt = 95 then the agent die. • Educational level (e) – depends on variable age: • If et = 1 and 1 ≤ yt+1 ≤ 14, then et = et+1 = 1; • If et = 1 e 15 ≤ yt+1 ≤ 18, então et+1 = U(1, min(2, maxe)); • If et = 1 e 19 ≤ yt+1 ≤ 94, então et+1 = U(1, min(2, maxe)) • If et = 2 e 19 ≤ yt+1 ≤ 94, então et+1 = U(2, min(3, maxe)); • Income (r) varies in [2;+∞[, and depends on the inflation rate of USA (equal to 3 %). In t+1, the value of r is given by: rt+1=rt+[U(-1,1)x0,03]. • Labour status (w) depends on variable age: • If 1 ≤ yt+1 ≤ 15 then w t+1 = 0; • If 16 ≤ yt+1 ≤ 94 then w t+1 = Bernoulli(k), being k the fraction w of working people in USA. • Number of individuals in the household (p): • If pt = 1, then p t+1 = pt + U(0,1); • If pt = 15, then p t+1 = pt + U(-1, 0); • If 2 ≤ pt+1 ≤ 14 then p t+1 = pt + U(-1,1); • The Number of individuals in the agents’ social network (s) varies according to the value of MN in the previous year.

  30. Educação (e) Valor possível de e 1 - Menos de 9 anos de frequência escolar 2 - Entre 9 e 12 anos de frequência escolar 3 - Mais de 12 anos de frequência escolar Restrições y ≤ 14  e=1 e 15 ≤ y ≤ 18  e=1 ou e=2 Atribuição de e Distribuição aleatória uniforme , U(mine,maxe) Rendimento do agregado familiar (r) r = [2; +∞[ Atribuição do rendimento Distribuição normal, N(r,r) Parâmetros da simulação Idade (y) • 1 ≤ y ≤ 95 • Atribuição de y • Distribuição normal, N(y,y)

  31. Número de pessoas do agregado familiar (p) 1 ≤ p ≤ 15 Atribuição de p Distribuição aleatória uniforme , U(1º quartilp,3ºquartilp) Número de indivíduos da rede social do agente (s) 2 ≤ s ≤ p+10, mas no máximo s=20 Atribuição de s Distribuição aleatória uniforme , U(p,maxs) Parâmetros da simulação Condição perante o trabalho (w) • Valor possível de w • w = 0, se o agente não está a trabalhar • w = 1, se o agente está empregado (y>15) • Atribuição do rendimento • Distribuição Bernoulli(k), • k=fracção de indivíduos a trabalhar nos EUA

  32. Redes sociais – Medidas Redes • Clustering (transitivity) • Probabilidade de dois vizinhos de um dado vértice estarem ligados • Densidade • Proporção entre o número de relações existentes e o número de relações possíveis. • Orientada o número de relações possíveis é igual ao número de vértices N multiplicado por N-1. • Rede não for orientada, o número de relações possíveis é dado por N(N-1)/2 • Comprimento médio de um caminho • Número médio de ligações no caminho mais curto entre qualquer dois pares de vértices • Diâmetro • Número máximo de ligações no caminho mais curto entre qualquer dois vértices • Grau de centralização (degree centralization) • Variação centralidade que existe na rede

  33. Recursos utilizados • Base de dados • IPUMS – recolha de dados reais de migrações • Software • SPSS – tratamento de dados • Repast – execução da simulação do modelo • Pajek – análise das redes sociais

  34. Estabilidade do modelo • Variabilidade das médias das 15 simulações • Alemães - Simulação I Simulação II < 10 % Simulação III • Simulação I < 5 %

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