WEBDATANET CONFERENCE May 26-28, 2015 Salamanca Forecasting unemployment with internet search data: does it help to improve predictions when job destruction is skyrocketing? María Rosalía Vicente Ana Jesús López Rigoberto Pérez University of Oviedo (Spain)

Monthly evolution of registered unemployment in Spain Source: Spanish Ministry of Employment and Social Security (2014) Unemployment rates. Year 2014 EU-28= 10.2% Spain= 24.5% Source: Eurostat (2015)

BACKGROUND Two main references: Ettredge, Gerdes and Karuga (2005) and Choi and Varian (2009). Evidence has been provided for different countries: France (Fondeur and Karamé, 2013), Germany (Askitas and Zimmermann, 2009), Israel (Suhoy, 2009), Italy (D’Amuri, 2009), Norway (Anvik and Gjelstad, 2010), the UK (McLaren and Shanbhogue, 2011) and the US (D'Amuri and Marcucci, 2009). Literature on nowcasting and forecasting unemployment with online search-related data:

DATA Explanatory variables: On the demand side: The Employment Confidence Indicator (ECI) which shows the balance between the positive and negative opinions of industrial firms on the current employment situation and their perspectives three-months ahead. Source: Spanish Ministry of Industry, Energy and Tourism. On the supply side: Google’s Trend Index which measures the volume of queries made by internet users through this search engine. Note: This is a weekly index that takes value 100 in the week with the highest number of searches for the words of interest. Keywords: “oferta de empleo” and “oferta de trabajo” (=job offer). Source: Google Trends service. Variable of interest: Monthly registered unemployment in Spain. Source: Spanish Ministry of Employment and Social Security. Period of analysis: January 2004-December Forecasting horizon: January 2013-December 2013.

METHODOLOGY Baseline B1: ARIMA(0,1,2)(0,1,1) (1-L)(1-L 12 )Y t =(1-q 1 L-q 2 L 2 )(1-Q 1 L 12 )u t Baseline B2: ARIMA(0,1,2)(0,1,1) with a level shift (LS) starting in March 2008 and a level shift with trend (t LS) (1-L)(1-L 12 )Y t = (1-q 1 L-q 2 L 2 )(1-Q 1 L 12 )u t + g 1 LS t + g 2 t LS t Model M1: (1-L)(1-L 12 )Y t = (1-q 1 L-q 2 L 2 )(1-Q 1 L 12 )u t + g 1 LS t + g 2 t LS t + b 1 X t ECI Model M2: (1-L)(1-L 12 )Y t = (1-q 1 L-q 2 L 2 )(1-Q 1 L 12 )u t + g 2 t LS t + b 1 X t ECI + b 2 X t Google-E Model M3: (1-L)(1-L 12 )Y t = (1-q 1 L-q 2 L 2 )(1-Q 1 L 12 )u t + g 2 t LS t + b 1 X t ECI + b 3 X t Google-T Two baselines models: Three specifications including Google-related variables on job search:

Baseline B1Baseline B2Model M1Model M2Model M3 q1q *** *** *** *** *** q2q *** *** *** *** *** Q1Q *** *** *** *** *** g 1 (Level shift) ** g 2 (Level shift with trend) ** ** *** *** b 1 (Employment Confidence Indicator) *** * * b 2 (Google index for “oferta de empleo”) ** b 3 (Google index for “oferta de trabajo”) * S.D. of innovations Akaike Criterion Schwarz Criterion Normality test Chi- square Chi-2=2.57 p=0.27 Chi-2=1.79 p=0.40 Chi-2=1.34 p=0.51 Chi-2=2.41 p=0.30 Chi-2=2.49 p=0.29 Estimation results for ARIMA and ARIMAX models on Spanish unemployment

Actual and forecasted unemployment in the horizon January-December 2013 Baseline B1 Baseline B2 Model M1 Model M2 Model M3 Root Mean Squared Error Mean Percentage Error Mean Absolute Percentage Error Theil's U

SUMMARY Emerging literature on the use of “Big Data” to improve the nowcasting and forecasting of macroeconomic variables. This paper has focused on the data coming from individuals’ internet search behavior in order to analyze the evolution of unemployment in Spain. Searches on “job offers”. Results confirm the potential of the proposed approach: It significantly improves the estimation and forecasting of unemployment’s figures in a context of important economic shocks. More details in the paper: Vicente, M.R., López, A.J. and Pérez, R. (2015): Forecasting unemployment with internet search data: Does it help to improve predictions when job destruction is skyrocketing?, Technological Forecasting & Social Change, 92,