# Modelling the Influence of Regional Identity on Human Migration

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methodologies

#### 2.1. Used Migration Data

#### 2.2. Model Specification

#### 2.3. Fitting a Standard Gravity Model for Human Migration

#### 2.4. Expansion of the Gravity Model

#### 2.4.1. Specification of Regional Identities

#### 2.4.2. Introduction of the Different Sets of Identity Regions

#### 2.5. Comparison of the Importance of Identity in Different Sets of Regions

#### 2.6. Creation of Randomly Generated Identity Regions

#### Optimisation of a Set of Identity Regions

## 3. Results

#### 3.1. Influence of the Embedding of Identity Regions on the Expected Number of Migrants

#### 3.2. Comparison of Median $ICM$ Value Distributions

## 4. Discussion

#### 4.1. Effect on the Influence of the Distance Parameter

#### 4.2. Model Limitations

#### 4.2.1. Strong Geographical Identities Require Little Displacement

#### 4.2.2. Identity Regions

#### 4.3. Development of Identity Influence over Time

## 5. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

GLM | Generalised Linear Model |

ICM | Identity Comparison Measure |

NUTS | Nomenclature of Territorial Units for Statistics |

## References

- UN. World Migration Report; Technical Report; International Organization for Migration: Grand-Saconnex, Switzerland, 2018. [Google Scholar]
- Skeldon, R. International Migration, Internal Migration, Mobility and Urbanization: Towards More Integrated Approaches. IOM Migr. Res. Ser.
**2018**. [Google Scholar] [CrossRef] - Bell, M.; Charles-Edwards, E. Cross-National Comparisons of Internal Migration: An Update on Global Patterns and Trends; Technical Report; United Nations Department of Economic and Social Affairs: New York, NY, USA, 2013. [Google Scholar]
- Sjaastad, L. The Costs and Returns of Human Migration. J. Political Econ.
**1962**, 70, 80–93. [Google Scholar] [CrossRef] - Stark, O. The Migration of Labor; Blackwell Publishers: Oxford, UK, 1991. [Google Scholar]
- Tiebout, C. A pure theory of local expenditures. J. Political Econ.
**1956**, 64, 416–424. [Google Scholar] [CrossRef] - Graves, P.; Linneman, P. Household migration: Theoretical and empirical results. J. Urban Econ.
**1979**, 6, 383–404. [Google Scholar] [CrossRef][Green Version] - Grigg, D.E.G. Ravenstein and the “laws of migration”. J. Hist. Geogr.
**1977**, 3, 41–54. [Google Scholar] [CrossRef] - Kok, J. Choices and constraints in the migration of families: The central Netherlands, 1850–1940. Hist. Fam.
**2004**, 9, 137–158. [Google Scholar] [CrossRef] - Hipp, J.; Boessen, A. Immigrants and Social Distance. Ann. Am. Acad. Political Soc. Sci.
**2012**, 641, 192–219. [Google Scholar] [CrossRef] - Bauer, T.; Zimmermann, K. Network Migration of Ethnic Germans. Int. Migr. Rev.
**1997**, 31, 143–149. [Google Scholar] [CrossRef] - Massey, D. Migration: Motivations; Elsevier: Amsterdam, The Netherlands, 2015; Volume 15, pp. 452–456. [Google Scholar]
- Greenwood, M. Human migration: Theory, models, and empirical studies. J. Reg. Sci.
**1985**, 25, 521–544. [Google Scholar] [CrossRef] - Lucassen, J. In Search of Work; IISG (International Institute of Social History) Research Papers; International Institute of Social History: Amsterdam, The Netherlands, 2000. [Google Scholar]
- Ranis, G.; Fei, J. A Theory of Economic Development. Am. Econ. Rev.
**1961**, 51, 533–565. [Google Scholar] - Bauer, T.; Zimmermann, K. Assessment of Possible Migration Pressure and Its Labour Market Impact Following EU Enlargement to Central and Eastern Europe; IZA Research Reports; IZA—Institute of Labor Economics: Bonn, Germany, 1999; Volume 3. [Google Scholar]
- Wallerstein, I. A world-system perspective on the social sciences. 1974. Br. J. Sociol.
**2010**, 61 (Suppl. 1), 167–176. [Google Scholar] [CrossRef] [PubMed] - Piore, M. Birds of Passage: Migrant Labor and Industrial Societies; Cambridge University Press: Cambridge, UK, 1979. [Google Scholar]
- Wolpert, J. Behavioral aspects of the decision to migrate. Pap. Reg. Sci.
**1965**, 15, 159–169. [Google Scholar] [CrossRef] - Todaro, M. A model of labor migration and urban unemployment in less developed countries. Am. Econ. Rev.
**1969**, 59, 138–148. [Google Scholar] - De Haas, H. The internal dynamics of migration processes: A theoretical inquiry. J. Ethn. Migr. Stud.
**2010**, 36, 1587–1617. [Google Scholar] [CrossRef] - Crawford, T. Beliefs about birth control: A consistency theory analysis. Represent. Res. Soc. Psychol.
**1973**, 4, 53. [Google Scholar] [PubMed] - Hoda Rahmati, S.; Tularam, G. A critical review of human migration models. Clim. Chang.
**2017**, 3, 924–952. [Google Scholar] - Stewart, J. The development of social physics. Am. J. Phys.
**1950**, 18, 239–253. [Google Scholar] [CrossRef] - Poot, J.; Alimi, O.; Cameron, M.; Maré, D. The gravity model of migration: The successful comeback of an ageing superstar in regional science. Investig. Reg.
**2016**, 2016, 63–86. [Google Scholar] - Ramos, R.; Suriñach, J. A Gravity Model of Migration Between the ENC and the EU. Tijdsch. Econ. Soc. Geogr.
**2017**, 108, 21–35. [Google Scholar] [CrossRef] - Simini, F.; González, M.C.; Maritan, A.; Barabási, A.L. A universal model for mobility and migration patterns. Nature
**2012**, 484, 96–100. [Google Scholar] [CrossRef] [PubMed] - Yang, Y.; Herrera, C.; Eagle, N.; González, M. Limits of Predictability in Commuting Flows in the Absence of Data for Calibration. Sci. Rep.
**2014**, 4, 5662. [Google Scholar] [CrossRef] [PubMed][Green Version] - Ren, Y.; Ercsey-Ravasz, M.; Wang, P.; González, M.; Toroczkai, Z. Predicting commuter flows in spatial networks using a radiation model based on temporal ranges. Nat. Commun.
**2014**, 5, 5347. [Google Scholar] [CrossRef] [PubMed][Green Version] - Kang, C.; Liu, Y.; Guo, D.; Qin, K. A Generalized Radiation Model for Human Mobility: Spatial Scale, Searching Direction and Trip Constraint. PLoS ONE
**2015**, 10, e0143500. [Google Scholar] [CrossRef] [PubMed] - Robinson, C.; Dilkina, B. A Machine Learning Approach to Modeling Human Migration. In Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS ’18), San Jose, CA, USA, 20–22 June 2018; ACM: New York, NY, USA, 2018; pp. 30:1–30:8. [Google Scholar] [CrossRef]
- Vermeulen, W. Dutch Migration Datasets—Computational History; Computational History: Diemen, The Netherlands, 2019. [Google Scholar]
- Piovani, D.; Arcaute, E.; Uchoa, G.; Wilson, A.; Batty, M. Measuring Accessibility using Gravity and Radiation Models. R. Soc. Open Sci.
**2018**, 5, 171668. [Google Scholar] [CrossRef] [PubMed] - Phibbs, C.; Luft, H. Correlation of Travel Time on Roads versus Straight Line Distance. Med Care Res. Rev.
**1995**, 52, 532–542. [Google Scholar] [CrossRef] - Nelder, J.; Wedderburn, R. Generalized Linear Models. J. R. Stat. Soc. Ser.
**1972**, 135, 370–384. [Google Scholar] [CrossRef] - Eurostat. NUTS 2 Regions in The Netherlands, 2010 and 2013; Eurostat: Luxembourg, 2013.
- Eurostat. Principles and Characteristics; Eurostat: Luxembourg, 2018.
- Vermeulen, W. Dutch Historical Regions—Computational History; Computational History: Diemen, The Netherlands, 2019. [Google Scholar]
- MacQueen, J. Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics; University of California Press: Berkeley, CA, USA, 1967; pp. 281–297. [Google Scholar]

**Figure 1.**Visual representation of the steps taken to assign municipalities to spatially clustered 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.

**Figure 2.**Difference between the expected number of migrants found in Equations (3) and (6)–(8). When two municipalities are located in the same identity region, the number of migrants in the basic gravity model is much lower than expected in the extended models, whereas this is the other way around for municipalities that are not located within the same region. The maximum distance travelled by migrants within the same region is cut to the reflect the size of the regions within each of the sets of regions.

**Figure 3.**Indexed changes in the predicted number of migrants between two municipalities with 50,000 residents that are located 50 km apart and the indexed changes in the exp($\iota $) value inside the model for each of the three sets of identity regions between 1996 and 2016. For every year, all municipalities were merged to form the municipalities that existed in 2016 to prevent the different numbers of municipalities from having any influences.

Intra-Municipal Migration | Inter-Municipal Migration | |
---|---|---|

Migration data | One CBS dataset [32] | Multiple CBS datasets [32] |

Distance data | Estimate: Distance between the centres of both municipalities | Estimate: $\sqrt{\frac{1}{2}|\varnothing |}$ |

Parameter | Description |
---|---|

$\alpha $ | Influence of ${p}_{a}$ on the number of migrants |

$\beta $ | Influence of ${p}_{b}$ on the number of migrants |

$\delta $ | Influence of ${\Delta}_{a\to b}$ on the number of migrants |

$\gamma =\mathrm{log}\left(G\right)$ | Normalisation constant of the regression function |

$\iota =\mathrm{log}\left(I\right)$ | Increase in the number of migrants when both municipalities are located in the same identity region |

${\Delta}_{a\to b}$ | Distance between municipality a and municipality b in kilometres |

${M}_{a\to b}$ | Number of migrants between municipality a and municipality b |

${p}_{a}$ | Population of municipality a |

${p}_{b}$ | Population of municipality b |

Data Set | Regions | Specification |
---|---|---|

NUTS 2 (Provinces) | 12 | [36] |

NUTS 3 (COROP regions) | 40 | [36] |

Historic regions | 70 | [38] |

**Table 4.**A comparison between the median $ICM$ values of the three different identity region configurations and the median $ICM$ value distributions of the same number of randomly generated spatially clustered regions, both optimised and non-optimised. Fifty samples were taken for distributions that involve the optimisation algorithm and 250 samples were taken for each of the non-optimised distributions that consist of randomly spatially clustered regions.

Predefined | Spatially Clustered | ||||
---|---|---|---|---|---|

95% CI | Max. | 95% CI | Max. | ||

NUTS 2 | Default | 12.34 | [12.60, 13.39] | 13.79 | |

Optimised | [13.02, 13.75] | 13.75 | [13.43, 14.24] | 14.38 | |

NUTS 3 | Default | 33.03 | [28.23, 33.33] | 34.23 | |

Optimised | [37.49, 39.12] | 39.12 | [36.88, 40.54] | 40.59 | |

Historic | Default | 42.34 | [40.11, 47.26] | 49.73 | |

Optimised | [54.48, 59.51] | 59.94 | [53.96, 61.83] | 62.05 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Vermeulen, W.R.J.; Roy, D.; Quax, R.
Modelling the Influence of Regional Identity on Human Migration. *Urban Sci.* **2019**, *3*, 78.
https://doi.org/10.3390/urbansci3030078

**AMA Style**

Vermeulen WRJ, Roy D, Quax R.
Modelling the Influence of Regional Identity on Human Migration. *Urban Science*. 2019; 3(3):78.
https://doi.org/10.3390/urbansci3030078

**Chicago/Turabian Style**

Vermeulen, Willem R. J., Debraj Roy, and Rick Quax.
2019. "Modelling the Influence of Regional Identity on Human Migration" *Urban Science* 3, no. 3: 78.
https://doi.org/10.3390/urbansci3030078