A suggestion on improving human migration models through incorporation of regional identities Willem R. J. Vermeulen BSc August 25, 2018 Supervisors: Rick Quax PhD, Debraj Roy PhD Assessor: Valeria Krzhizhanovskaya PhD UvA student id: 10561714 VU student id: 2606467 ii Abstract Human migration is a complex phenomenon, on which many different factors have their influence. The migration process is modelled using gravity models or radiation models. In this paper we propose that human migration models can be improved by embedding regional identities into the model. This is tested by adding three different sets of Dutch identity regions to a gravity model. Through analysis of the Dutch internal migration data between 1996 and 2016, we discover that individuals are more likely to move towards municipalities located within the same identity region. We also find that this influence of identity becomes larger when the identity regions are as smaller and as well defined as possible. iii iv Contents Acknowledgements xii 1 Introduction 1 1.1 Research question and hypotheses .......................... 1 1.1.1 Introducing regional identities to a human migration model increases its predictive value ................................. 2 1.1.2 Distance is handled differently when regional identities are introduced .. 2 1.1.3 Smaller regional identities have a higher predictive power than larger iden- tities ....................................... 2 2 Methodologies 3 2.1 Model specification ................................... 3 2.2 Used migration data .................................. 4 2.3 Fitting a standard gravity model for human migration ............... 4 2.4 Expansion of the gravity model ............................ 5 2.4.1 Specification of regional identities ....................... 6 2.4.2 Introduction of the different sets of identity regions ............. 7 2.5 Comparison of the importance of identity in different sets of regions ....... 7 2.6 Creation of other sets of identity regions ....................... 8 2.6.1 Randomly generated regions .......................... 8 2.6.2 Randomly generated spatially clustered regions ............... 8 2.7 Optimisation of a set of regions ............................ 9 3 The significance of the influence of the specified identity regions 13 3.1 Differences in the mean ICM values .......................... 14 3.2 Differences in the median ICM values ........................ 17 3.3 Sensitivity to parameter changes in the optimisation algorithm .......... 19 4 Discussion 21 4.1 Specification of the model ............................... 22 4.2 Specification of the ICM value ............................. 22 4.2.1 Other influences that can contribute to the ICM value ........... 22 4.3 Identity regions ..................................... 23 4.3.1 Optimisation technique ............................ 23 4.3.2 Quality ..................................... 24 4.3.3 Challenges in the usage of identity regions .................. 24 5 Conclusions 27 6 Future work 29 Appendices I v A Influences on migration decisions III A.1 Considerations in migration decisions ......................... III A.2 Economic benefits ................................... III A.3 Availability of amenities ................................ IV A.4 Travel distance ..................................... V A.5 Information distance .................................. V A.6 Social distance ..................................... VI A.7 Household optimisation ................................ VII A.8 Family-cycle considerations .............................. VII A.9 Policies & disasters ................................... VIII B Specification of identity regions IX C Tactics to increase the average ICM value XVII C.1 Using network metrics ................................. XVII C.2 Similarity of migration behaviour ........................... XVIII C.3 Reassigning municipalities ............................... XVIII C.4 Simulated annealing .................................. XIX C.5 Discussion ........................................ XIX D Geographical distributions of ICM values for different sets of regions XXI vi List of Figures 2.1 Visual representation of the steps taken to assign municipalities to spatially clus- tered regions using the k-means algorithm. This algorithm can be applied to generate different numbers of regions. This number of regions is controlled by the variable k. To be able to compare the generated regions with a certain set of identity regions, this k is set equal to the number of regions present in this set of identity regions. .................................... 9 2.2 A visual representation of the optimisation algorithm. Given a certain starting configuration, it is determined what regions are located within a distance of20 kilometres from each existing municipality. For each of these municipalities, the change in the global average ICM value is measured when a municipality would be part of that region. Every municipality that should be part of another region than it already was, is then relocated with a chance of 50%. This process is repeated until no more municipalities are relocated for three iterations. .......... 11 3.1 The mean ICM values for 250 sets of twelve randomly generated regions, 250 sets of twelve randomly generated spatially clustered regions, fifty sets of twelve optimised randomly generated spatially clustered regions and fifty sets of optimised NUTS 2 regions, compared to the ICM value of the original NUTS 2 regions. ...... 14 3.2 Whatever changes in parameters we make to the human migration model, the mean value of the ICM values of the randomly generated spatially clustered regions is always significantly lower than the ICM value of the NUTS 2 regions. For each of the parameter configurations, thirty different randomly generated spatially clustered regions were generated. ........................... 14 3.3 The mean ICM values for 250 sets of forty randomly generated regions, 250 sets of forty randomly generated spatially clustered regions, fifty sets of forty optimised randomly generated spatially clustered regions and fifty sets of optimised NUTS 3 regions, compared to the ICM value of the original NUTS 3 regions. ...... 15 3.4 Whatever changes in parameters we make to the human migration model, the mean value of the ICM values of the randomly generated spatially clustered regions is always significantly lower than the ICM value of the NUTS 3 regions. For each of the parameter configurations, thirty different randomly generated spatially clustered regions were generated. ........................... 15 3.5 The mean ICM values for 250 sets of seventy randomly generated regions, 250 sets of seventy randomly generated spatially clustered regions, fifty sets of seventy op- timised randomly generated spatially clustered regions and fifty sets of optimised literature regions, compared to the ICM value of the original regions specified through literature. ................................... 16 3.6 Whatever changes in parameters we make to the human migration model, the mean value of the ICM values of the randomly generated spatially clustered re- gions is always significantly lower than the ICM value of the literature defined regions. For each of the parameter configurations, thirty different randomly gen- erated spatially clustered regions were generated. .................. 16 vii 3.7 The median ICM values for 250 sets of twelve randomly generated regions, 250 sets of twelve randomly generated spatially clustered regions, fifty sets of twelve opti- mised randomly generated spatially clustered regions and fifty sets of optimised NUTS 2 regions, compared to the ICM value of the original NUTS 2 regions. .. 17 3.8 The median ICM values for 250 sets of forty randomly generated regions, 250 sets of forty randomly generated spatially clustered regions, fifty sets of forty optimised randomly generated spatially clustered regions and fifty sets of optimised NUTS 3 regions, compared to the ICM value of the original NUTS 3 regions. ...... 18 3.9 The median ICM values for 250 sets of seventy randomly generated regions, 250 sets of seventy randomly generated spatially clustered regions, fifty sets of sev- enty optimised randomly generated spatially clustered regions and fifty sets of optimised literature regions, compared to the ICM value of the original regions specified through literature. .............................. 18 3.10 Differences between the mean ICM values of the optimised randomly spatially clustered regions and the mean ICM values of the optimised predefined regions for each predefined set of regions, using three different distance cut-offs inthe optimisation algorithm. For each of the parameter configurations, thirty different optimised regions were generated. ........................... 19 3.11 Differences between the median ICM values of median ICM values of the optimised randomly spatially clustered regions and the optimised predefined regions for each predefined set of regions, using three different distance cut-offs in the optimisation algorithm. For each of the parameter configurations, thirty different optimised regions were generated. ................................ 20 4.1 Three different approaches to defining identity region: with hard boundaries, fuzzy boundaries, or by looking at the existing connections between two municipalities. 25 D.1 The ICM values calculated for each municipality when the Netherlands are split into the NUTS 2 regions. All ICM values are positive. The ICM