Nowcasting Regional Unemployment Rate in Denmark Using Google Trends to Develop Mining Sector

COVID-19 pandemic and the EU target to develop a competitive
digital economy, the aim of the paper is related to nowcasting the
regional unemployment rate in Denmark using specific keywords for
Google searches ('job', 'Jobnet', 'jobindex.dk' and 'ofir.dk') to predict
an economic crisis. Compared to previous studies that describe
unemployment using Internet data, this paper provides a regional
insight in nowcasting unemployment in Denmark using various panel
data models. According to the panel nonlinear autoregressive
distributed lag (ARDL) models, a significant short-run and long-run
connection between the quarterly unemployment rate at NUTS 2
level in Denmark and the mentioned keywords in the period 2008-
2020. The increase by 1% in job searches lowers the unemployment
rate by 0.055 to 0.061 percentage points in the short-run. However,
in the long-run, the amplitude of these regimes is higher (by 0.143 to
0.642 percentage points), showing that more searches for jobs in
Denmark will significantly reduce the unemployment rate. The
results suggest that the combination between official statistics and
data collected using Google Trends has the capacity to improve the
unemployment predictions in Denmark, and it is recommended in the
short-run planning of the government. The quarterly predictions for
economic growth in 2021 could be used to anticipate a potential
economic crisis in Denmark. This country has no active mines, and
quarrying plays a less significant role in the national economy, but in
the context of a future crisis, this sector should develop, and Google
Trends could be an efficient tool for policymakers and for people
searching for a job in this sector.