Uncertainty in migration scenarios

Abstract

In this report, we propose ways of looking at the uncertainty of migration forecasts and scenarios across a range of time horizons, through the lens of macroeconomic modelling. As an illustration, for short-term horizons, we present the results of empirical models aiming to assess different aspects of the uncertainty in migration and economic dynamics following exogenous shocks. To that end, we estimate Bayesian panel vector autoregressive (VAR) models to generate forecasts, which can be also used in scenario-setting. We also examine the effects of an exogenous increase to migration on the macroeconomy. By looking at the forecast errors for different migration indicators, and for a range of models and groups of European countries, we assess the usefulness of VAR models for generating short- and long-range migration forecasts and scenarios and for estimating their uncertainty. For longer horizons, we also look into dynamic stochastic general equilibrium (DSGE) models, which are used here to generate theoretically-informed migration scenarios. In particular, we look at a scenario of job automation, examining inequalities in migration processes, either modelled as exogenous or, in a two-country model, with fully endogenous migration decisions, depending on the labour market conditions and costs. The results of modelling offer coherent migration scenarios and provide a tool for assessing the uncertainty of both migration and its impacts. We also identify and discuss several important remaining research gaps and methodological challenges of using modern macroeconomic approaches for forward-looking migration studies, and propose some practical solutions.

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Identifiers

ORCID for Emily Barker: orcid.org/0000-0003-3368-9169

ORCID for Jakub Bijak: orcid.org/0000-0002-2563-5040

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