Joint modelling of dyadic and monadic count outcomes: an application to modelling forced migration flows

Abstract

The aim of this study is to explore the adoption of a joint modelling framework for dealing with dyadic and monadic count outcomes with excess zeros simultaneously via a common latent structure. As a case study, we consider the problem of identifying the different push and pull factors of cross-border forced migration and internal displacement. We consider a full panel data analysis and estimate a random effects joint hurdle model following the Bayesian paradigm; the resultant posterior is approximated through the integrated nested Laplace approximation.

Keywords

Excess zeros Forced migration Gravity models Hurdle models Integrated nested Laplace approximation Joint modelling

Get full access to this article

View all access options for this article.

References

Abel

, Brottrager

, Cuaresma

and Muttarak

(2019) Climate, conflict and forced migration. Global Environmental Change , 54, 239–49. doi: https://doi.org/10.1016/j.gloenvcha.2018.12.003.

Anderson

(2011) The gravity model. Annual Review of Economics , 3, 133–60. doi: https://doi.org/10.1146/annurev-economics-111809125114.

Anderson

and van Wincoop

(2003) Gravity with gravitas: a solution to the border puzzle. The American Economic Review , 93, 170–92. https://www-jstor-org-443.web.bisu.edu.cn/stable/3132167

Asmarian

, Ayatollahi

SMT

, Sharafi

and Zare

(2019) Bayesian spatial joint model for disease mapping of zero-inflated data with r-inla: a simulation study and an application to male breast cancer in iran. International Journal of Environmental Research and Public Health , 16, 4460. doi: https://doi.org/10.3390/ijerph16224460.

Backhaus

, Martinez-Zarzoso

and Muris

(2015) Do climate variations explain bilateral migration? a gravity model analysis. IZA Journal of Migration , 4, 1–15. doi: https://doi.org/10.1186/s40176-014-0026-3.

Baltagi

, Egger

and Pfaffermayr

(2003) A generalized design for bilateral trade flow models. Economic Letters , 80, 391–97. doi: https://doi.org/10.1016/S0165-1765(03)00115-0.

Baltagi

, Egger

and Pfaffermayr

(2015) Panel data gravity models of international trade. In Baltagi

B. H.

, editor, The Oxford handbook of panel data, pages 608-42 . Oxford University Press. doi: https://doi.org/10.1093/oxfordhb/9780199940042.013.0020.

Bosetti

, Cattaneo

and Peri

(2021) Should they stay or should they go? climate migrants and local conflicts. Journal of Economic Geography , 21, 619–51. doi: https://doi.org/10.1093/jeg/bbaa002.

Burger

, van Oort

and Linders

(2009) On the specification of the gravity model of trade: zeros, excess zeros and zero-inflated estimation. Spatial Economic Analysis , 4, 167–90. doi: 10.1080/17421770902834327.

10.

Buzzanca

, Conigliani

and Costantini

(2023) Conflicts and natural disasters as drivers of forced migrations in a gravity-type approach. Entrepreneurship and Sustainability Issues , 10. doi: https://doi.org/10.9770/jesi.2023.10.3(17).

11.

Coniglio

and Pesce

(2015) Climate variability and international migration: an empirical analysis. Environmental and Development Economics , 20, 434–68. doi: https://doi.org/10.1017/S1355770X14000722.

12.

da Silva

and Fan

(2017) Conflict, migration, and food security: the role of agriculture and rural development. FAO-IFPRI Joint Brief . Rome Italy and Washington D.C.: Food and Agricultural Organization of the United Nations (FAO) and International Food Policy Research Institute (IFPRI).

13.

Egger

(2002) An econometric view on the estimation of gravity models and the calculation of trade potentials. The World Economy , 25, 297–312. doi: https://doi.org/10.1111/1467-9701.00432.

14.

EGRIS (2018) International recommendations on refugee statistics (IRRS). United Nations and Eurostat Manuals and Guidelines. doi: https://doi.org/10.2785/52866.

15.

Freeman

and Lewis

(2021) Gravity model estimates of the spatial determinants of trade, migration, and trade-and-migration policies. Economic Letters , 204. doi: https://doi.org/10.1016/j.econlet.2021.109873.

16.

Gelman

(2008) Scaling regression inputs by dividing by two standard deviations. Statistics in Medicine , 27, 2865–73. doi: https://doi.org/10.1002/sim.3107.

17.

Gelman

, Jakulin

, Pittau

and Su

(2008) A weakly informative default prior distribution for logistic and other regression models. The Annals of Applied Statistics , 2, 1360–83. doi: https://doi.org/10.1214/08-AOAS191.

18.

Gómez-Rubio

(2020) Bayesian inference with INLA . Chapman & Hall/CRC Press.

19.

Hanson

, Branscum

and Johnson

(2014) Informative g-priors for logistic regression. Bayesian Analysis , 9, 597–612. doi: https://doi.org/10.1214/14-BA868.

20.

Hartmann

(2010) Rethinking climate refugees and climate conflict: Rhetoric, reality and the politics of policy discourse. Journal of International Development , 22, 233–46. doi: https://doi.org/10.1002/jid.1676.

21.

Head

and Mayer

(2014) Gravity equations: workhorse, toolkit, and cookbook. In Gopinath

, Helpman

and Rogoff

, editors, Handbook of international economics , volume 4, pages 131–95. Elsevier.

22.

Helpman

, Melitz

and Rubinstein

(2008) Estimating trade flows: trading partners and trading volumes. Quarterly Journal of Economics , 123, 441–87. doi: https://doi.org/10.1162/qjec.2008.123.2.441.

23.

Herman

(2023) Gravity estimation: best practices and useful approaches. Economics Working Paper Series, U.S. International Trade Commission (2023-10-C).

24.

Hoff

(2021) Additive and multiplicative effects network models. Statistical Science , 36, 34–50. doi: https://doi.org/10.1214/19-STS757.

25.

Kleinert

and Toubal

(2010) Gravity for FDI. Review of International Economics , 18, 1–13. doi: https://doi.org/10.1111/j.1467-9396.2009.00869.x.

26.

Letouzé

, Purser

, Rodriguez

and Cummins

(2009) Revisiting the migration-development nexus: a gravity model approach. Human Development Research Papers , 44. URL https://mpra.ub.uni-muenchen.de/id/eprint/19227

27.

Lewer

and Van den Berg

(2008) A gravity model of immigration. Economics Letters , 99, 164–67. doi: https://doi.org/10.1016/j.econlet.2007.06.019.

28.

Martin

and Pham

(2020) Estimating the gravity model when zero trade flows are frequent and economically determined. Applied Economics , 52, 2766–79. doi: 10.1080/00036846.2019.1687838.

29.

Martino

and Rue

(2008) Implementing approximate Bayesian inference using integrated nested Laplace approximation: a manual for the INLA program. Technical Report, Norwegian University of Science and Technology, Trondheim.

30.

Martino

and Rue

(2010) Case studies in Bayesian computation using INLA. In Mantovan

and Secchi

, editors, Complex data modeling and computationally intensive statistical methods. Contributions to statistics , pages 99–114, Milano, 2010. Springer. doi: https://doi.org/10.1007/978-88-470-1386-5_8.

31.

Martins

, Simpson

, Lindgren

and Rue

(2013) Bayesian computing with inla: new features. Computational Statistics and Data Analysis , 67, 68–83. doi: https://doi.org/10.1016/j.csda.2013.04.014.

32.

Mizutori

and Guha-Sapir

(2017) Economic losses, poverty and disasters 1998–2017 . United Nations Office for Disaster Risk Reduction (UNISDR).

33.

Morales-Munoz

, Jha

, Bonatti

, Alff

, Kurtenbach

and Sieber

(2020) Exploring connections-environmental change, food security and violence as drivers of migration-a critical review of research. Sustainability , 12, 5702. doi: https://doi.org/10.3390/su12145702.

34.

Mullahy

(1986) Specification and testing of some modified count data models. Journal of Econometrics , 33, 341–65. doi: https://doi.org/10.1016/0304-4076(86)90002-3.

35.

Okawa

and van Wincoop

(2013) Gravity in international finance. Journal of International Economics , 87, 205–15. doi: https://doi.org/10.1016/j.jinteco.2012.01.006.

36.

Reuveny

(2007) Climate change-induced migration and violent conflict. Polititical Geography: Climate Change and Conflict , 26, 656–73. doi: https://doi.org/10.1016/j.polgeo.2007.05.001.

37.

Rue

and Held

(2005) Gaussian Markov random fields: theory and applications . Chapman & Hall/CRC. doi: https://doi.org/10.1201/9780203492024.

38.

Rue

, Martino

and Chopin

(2009) Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B (Statistical Methodology) , 71, 319–92. doi: https://doi.org/10.1111/j.1467-9868.2008.00700.x.

39.

Santos Silva

JMC

and Tenreyro

(2006) The log of gravity. The Review of Economics and Statistics , 88, 641–58. doi: https://doi.org/10.1162/rest.88.4.641.

40.

Simpson

, Rue

, Riebler

, Martins

and Sørbye

(2017a) Penalising model component complexity: a principled, practical approach to constructing priors. Statistical Science , 32, 1–28. doi: https://doi.org/10.1214/16-STS576.

41.

Simpson

, Rue

, Riebler

, Martins

and Sørbye

(2017b) You just keep on pushing my love over the borderline: a rejoinder. Statistical Science , 32, 44–6. URL https://www-jstor-org-443.web.bisu.edu.cn/stable/26408119

42.

Stina

(2021) Uppsala Conflict Data Program (UCDP) Georeferenced Event Dataset (GED) Codebook version 21.1 . Department of Peace and Conflict Research, UPPSALA University.

43.

Sundberg

and Melander

(2013) Introducing the Uppsala conflict data program georeferenced event dataset. Journal of Peace Research , 50, 523–32. doi: https://doi.org/10.1177/0022343313484347.

44.

Tierney

and Kadane

(1986) Accurate approximations for posterior moments and marginal densities. Journal of the American Statistical Association , 81, 82–6. doi: https://doi.org/10.1080/01621459.1986.10478240.

45.

Tufis

and Hudson

(2023) The global state of democracy indices codebook, Version 7. International Institute for Democracy and Electoral Assistance, Stockholm . doi: https://doi.org/10.31752/idea.2023.37.

46.

Van Bergeijk

and Brakman

(2010) The gravity model in international trade: advances and applications . Cambridge University Press. doi: https://doi.org/10.1017/CBO9780511762109.

47.

Van Niekerk

, Bakka

and Rue

(2019) Joint models as latent gaussian models not reinventing the wheel . https://arxiv.org/abs/1901.09365

48.

Van Niekerk

, Bakka

and Rue

(2021a) Competing risks joint models using r-inla. Statistical Modelling , 21, 56–71. doi: https://doi.org/10.1177/1471082X20913654.

49.

Van Niekerk

, Bakka

, Rue

and Schenk

(2021b) New frontiers in Bayesian modeling using the inla package in r. Journal of Statistical Software , 100. doi: https://doi.org/10.18637/jss.v100.i02.

50.

Van Niekerk

, Krainski

, Rustand

and Rue

(2023) A new avenue for Bayesian inference with INLA. Computational Statistics & Data Analysis , 181, 107692. doi: https://doi.org/10.1016/j.csda.2023.107692.

51.

Watanabe

(2010) Asymptotic equivalence of bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research , 11, 3571–94.

52.

Yotov

(2022) On the role of domestic trade flows for estimating the gravity model of trade. Contemporary Economic Policy , 40, 526–40. doi: https://doi.org/10.1111/coep.12567.

53.

Zellner

(1983) Applications of Bayesian analysis in econometrics. The Statistician , 32, 23–34. doi: https://doi.org/10.2307/2987589.

54.

Zellner

(1983) Applications of Bayesian analysis in econometrics. The Statistician , 32, 23–34. doi: https://doi.org/10.2307/2987589.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

3.21 MB

0.00 MB