Quantifying the impact of COVID-19 on immigration in receiving high-income countries 1

: Previous studies have examined the impact of COVID-19 on mortality and fertility. However, little is known about the effect of the pandemic on constraining international migration. We quantify the impact of COVID-19 on immigration flows in 15 high-income countries by forecasting their counterfactual levels in 2020 assuming no pandemic and comparing these estimates with observed immigration counts. We then explore potential driving forces, such as stringency measures and changes in unemployment moderating the extent of immigration decline. Our results show that immigration declined in all countries, except in Finland. Yet, significant cross-national variations exist. Australia (60%), Spain (45%) and Sweden (36%) display the largest declines, while immigration decreased by between 15% and 30% in seven states, and by less than 15% in four where results were not statistically significant. International travel, mobility restrictions and stay-at-home requirements exhibit a relationship with declines in immigration, although countries with similar levels of stringency witnessed different intensities of decline. Work and school closings and unemployment show no relationship. This paper quantifies the impact of the COVID-19 pandemic on the size of immigration flows in a global sample of high-income countries. We employed Auto Regressive Integrated Moving Average (ARIMA) models to forecast the expected immigration counts in 2020 if the pandemic had not been occurred. We then compared the forecasted immigration count to the observed immigration count in 2020. We used 2012-2019 data to produce country-specific forecasts of immigration counts for 2020. An ARIMA model comprises three components: an autoregressive (AR) process, a moving average (MA) and an integrated (I) element. Intuitively, these components capture the long-term, stochastic and short-term trends of a time series, respectively. Formally, the AR and MA components control for temporal autocorrelation in a time series resulting from two mechanisms. The first assumes a variable ( Y ) at time t (Y t ) which is explained by its past value(s) (i.e., 𝑦 𝑡−1 , 𝑦 𝑡−2 , ⋯ , 𝑦 𝑡−𝑝 ). The second assumes Y t is a function of current and past moving averages of error terms (e.g., 𝑢 𝑡−1 + 𝑢 𝑡−2 + ⋯ + 𝑢 𝑡−𝑞 ); that is, current deviations from the mean depends on previous deviations. A general ARMA ( p, q ) model takes the form of: terms,