Migration Preferences Under Economic and Geopolitical Uncertainty: An Asymmetric Approach

Panagiotis Palaios; Aikaterini Chatzimichailidou

doi:10.1515/peps-2024-0024

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Migration Preferences Under Economic and Geopolitical Uncertainty: An Asymmetric Approach

Published/Copyright: August 20, 2024

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From the journal Peace Economics, Peace Science and Public Policy Volume 30 Issue 3

Abstract

This paper aims at assessing asymmetric dynamics among migration preferences and various security and economic factors, focusing on the movements from Syria to Turkey and then to the EU via the Greek sea borders (2010M1−2022M12). We perform an economic analysis by developing a utility model, according to which migration preferences depend on security and employment and empirically test asymmetric responses of migration to corresponding shocks. We contribute to the literature by finding that migration is downwards sticky. Despite that theories of international relations may not be monolithic, the magnitude of security impact on migration implies that the state remains the primary actor responsible for managing this phenomenon, which brings us closer to neo-realism theory of International Relations (IR). Our findings reveal the factors that lead to the downwards stickiness of migration thus contributing to a better understating of the incentives for migration and to the formulation of more efficient policies.

Keywords: utility; migration; public choice; security; unemployment; international relations

JEL Classification: F22; F50; H56; C24

Corresponding author: Panagiotis Palaios, School of Business and Economics, Deree – The American College of Greece, 6 Gravias St., Aghia Paraskevi, Athens 15342, Greece, E-mail: ppalaios@acg.edu

Informed consent: This study presents original material that has not been published elsewhere.
Author contributions: The authors have equally contributed to all parts of this paper. All the authors have read and approved the final manuscript.
Competing interests: The authors declare that they have no competing interests, or other interests that might be perceived to influence the results and/or discussion reported in this paper.
Data availability: The data employed in this research paper can be accessed in https://doi.org/10.6084/m9.figshare.25089590.v1. The codes to replicate the results are available upon request.

Appendices

Appendix A.1: Second Order Conditions

When it comes to the second order conditions of Section 3.1, the bordered Hessian matrix of second order partial derivatives is: H _B = [ d 2 L d S 2 d 2 L d S d E d 2 L d λ d S d 2 L d E d S d 2 L d E 2 d 2 L d λ d E d 2 L d λ d S d 2 L d λ d E 0 ] = [ − a S i 2 0 − P S 0 − ( 1 − a ) E i 2 − P E − P S − P E 0 ] and the determinant |H _B| > 0 thus indicating the existence of maximum.

When it comes to the second order conditions of Section 3.2, the bordered Hessian matrix of second order partial derivatives is: H _B = [ d 2 L d S 2 d 2 L d S d E d 2 L d λ d S d 2 L d E d S d 2 L d E 2 d 2 L d λ d E d 2 L d λ d S d 2 L d λ d E 0 ] = [ − γ 0 − P S 0 − 1 − P E − P S − P E 0 ] and the determinant |H _B| > 0 thus indicating the existence of maximum.

Appendix A.2: Monte Carlo Simulation and Unit Root Test

A.2.1 Monte Carlo Simulation

According to Enders and Siklos (2001) the test statistic, represented by Φ, for testing the null hypothesis of no cointegration (H ₀:ρ ₁ = ρ ₁ = 0) is a non-standard F-statistic, as it does not follow the standard distribution. Therefore, we need to simulate the critical values by conducting a Monte Carlo simulation. Five sets of normally distributed numbers with standard deviation equal to unity were generated, using random walk processes with 5,000 trials and for T = 157 observations, to represent the {u _migr,t}, {u _gpr,t}, {u _ungap,t}, {u _confl,t}, {u _viol,t} sequences, as follows:

l n m i g r t = l n m i g r t − 1 + u m i g r , t u m i g r , t ≈ Ν ( 0 , 1 )

l n g p r t = l n g p r t − 1 + u g p r , t u g p r , t ≈ Ν ( 0 , 1 )

l n u n g a p t = l n u n g a p t − 1 + u u n d i f , t u u n g a p , t ≈ Ν ( 0 , 1 )

c o n f l t = c o n f l t − 1 + u c o n f l , t u c o n f l , t ≈ Ν ( 0 , 1 )

l n v i o l t = l n v i o l t − 1 + u l n v i o l , t u v i o l , t ≈ Ν ( 0 , 1 )

For each of the 5,000 series, we estimate the long-run relationship in Eq. (19) and then follow the methodology described in Section 3.2.1 to estimate the cTAR and cMTAR models. For each of these models, and for each of the 5,000 trials, the nonstandard F-statistic for the null hypothesis (H ₀:ρ ₁ = ρ ₁ = 0), denoted by Φ, was recorded. This procedure was repeated for each of the three threshold variables and for the case for zero, one, two and three lags. The critical values generated are used to test the null hypothesis of no cointegration (H ₀:ρ ₁ = ρ ₁ = 0) and are reported in Table A.2.1.

Table A.2.1:

Monte Carlo simulation. The distribution of Φ (non-standard F-statistic).

	0 lags			1 lag			2 lags			3 lags
Threshold variable	0.10	0.05	0.01	0.10	0.05	0.01	0.10	0.05	0.01	0.10	0.05	0.01
lngpr _t-1	11.840	14.341	20.347	11.499	14.036	21.257	11.126	13.701	19.857	11.145	13.667	19.276
Δlngpr _t-1	9.880	11.357	14.426	9.635	11.046	14.028	9.274	10.507	13.474	9.087	10.386	13.542
lnungap _t-1	10.285	12.069	17.170	10.470	12.434	17.883	10.225	12.203	17,149	10.271	12.387	17.057
Δlnungap _t-1	9.905	11.467	14.280	9.625	11.051	14.453	9.292	10.500	13.679	9.097	10.451	13.644
lnviol _t-1	12.212	14.569	20.756	11.971	14.276	19.724	11.405	13.758	19.507	11.349	13.893	19.149
Δlnviol _t-1	9.796	11.399	14.290	9.555	10.947	17.198	9.257	10.489	13.755	9.032	10.274	13.629

The critical values of the non-standard F-statistic have been simulated for the five variables of our model and for sample size T = 157.

A.2.2 Unit Root Tests

To examine the stationary properties of our series, we perform the augmented Dickey-Fuller (ADF 1979) and the Kwiatkowski, Phillips, Schmidt and Shin (KPSS 1992) conventional unit root tests. Further, considering the asymmetric features of our data we account for the presence of possible asymmetries that could lead to spurious results by utilizing the Zivot and Andrews (1992) asymmetric unit root tests that allow for possible structural breaks in the series. ADF and Zivot and Andrews test the null hypothesis of a unit root, while KPSS tests the null hypothesis of stationarity. According to Engle and Granger (1987) representation theorem an error – correction model can be estimated to examine the short run behavior of the variables after a shock, provided that all the variables in consideration are cointegrated. As revealed by our results, reported in Table A.2.2, our variables are integrated of order one I(1), implying that we can perform threshold cointegration analysis for the long run behavior and asymmetric error correction model for the short run behavior.

Table A.2.2:

Unit root tests.

Variable	lnmigr		lngpr		lnungap		confl		lnviol
Variable	Level	First difference	Level	First difference	Level	First difference	Level	First difference	Level	First difference
Part i: Unit root test not allowing for structural breaks

ADF	−2.094 [1]	−10.613 [1]***	−2.6083 [2]	−10.299 [2]***	−0.671 [3]	−5.208 [2]***	−1.541 [1]	−8.689 [1]*	−2.687 [3]	−9.541 [3]***
KPSS	0.559 [3]***	0.038 [3]	0.172 [3]**	0.0221 [3]	0.377 [3]***	0.074 [3]	0.533 [3]***	0.061 [3]	0.425 [3]***	0.0219 [3]

Part ii: Unit root test allowing for structural break

Zivot-Andrews	−3.847 [1]	−11.136 [1]***	−4.834 [3]	−10.6381 [2]***	−3.129[ 3]	−5.986 [3]***	−3.539 [3]	−9.086 [1]***	−3.958 [3]	−10.239 [1]***
Breakpoint	7/2012	9/2015	5/2015	5/2010	4/2016	7/2019	12/2013	11/2013	9/2011	4/2016

The number in the bracket are lags used in the test. The lag order is in accordance with the AIC lag length. *, **, ***Denotes significance at 10 %, 5 % and 1 % level, respectively.

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Received: 2024-05-17

Accepted: 2024-07-31

Published Online: 2024-08-20

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https://doi.org/10.1515/peps-2024-0024

Keywords for this article

utility; migration; public choice; security; unemployment; international relations