Measuring the Cost of Covid-19 in Terms of the Rise in the Unemployment Rate: The Case of Spain

Juan Carlos Cuestas,Mercedes Monfort,Javier Ordóñez

doi:10.1515/econ-2021-0009

Juan Carlos Cuestas, Mercedes Monfort + Show 1 more

Open Access

https://doi.org/10.1515/econ-2021-0009

Copy DOI

Abstract

Abstract In this article, we aim to estimate what the cost of Covid-19 has been in terms of unemployment for Spain. We use a simple autoregressive model to simulate what the unemployment rate would have been without the pandemic and we compare this with the actual values. We also forecast the unemployment rate for 2021 and 2022 to analyse the persistence of the pandemic shock to unemployment.

Highlights

The Covid-19 pandemic started in China in December 2019, since it has cost millions of lives and had huge consequences for most of the world’s economies
The estimated persistence of the pandemic shock to unemployment is consistent with the previous literature, which concludes that shocks to unemployment in Spain tend to be long-lasting because of the institutional features of the Spanish labour market
The seminal paper by Blanchard and Summers (1986) argues that hysteresis, or the tendency of unemployment to react with a high degree of persistence to temporary supply and demand shocks, is explained by the bargaining power of strong unions and worker protection schemes

Summary

Introduction

The Covid-19 pandemic started in China in December 2019, since it has cost millions of lives and had huge consequences for most of the world’s economies. The unemployment rate has shown significant rises as the pandemic has progressed in countries with traditionally high unemployment rates. We aim to quantify the effect of Covid-19 on the unemployment rate for Spain, which is a country that traditionally has high and persistent unemployment rates. Spain is one of the countries alongside Greece and Italy where unemployment rates reached above 20% after the Great Recession started in 2008. To measure the effect of the pandemic, we estimate a simple seasonal autoregressive, integrated and moving average (SARIMA) model until the end of 2019 and use this model to forecast the unemployment rates after that. The policy insights we aim to gain here have to do with assessing to what extent the already deployed measures have worked and how further fiscal policy easing can help the recovery

Objectives

Results

Conclusion