Development and validation of a real-time happiness index using Google TrendsTM - Talita Greyling; Stephanié Rossouw (Global Labor Organization (GLO), 2024)
It is well-established that a country's economic outcomes, including productivity, future income, and labour market performance, are profoundly influenced by the happiness of its people. Traditionally, survey data have been the primary source for determining people's happiness. However, this approach faces challenges as individuals increasingly experience "survey fatigue"; conducting surveys is costly, data generated from surveys is only available with a significant time lag, and happiness is not a constant state. To address these limitations of survey data, Big Data collected from online sources like Google Trends™ and social media platforms have emerged as a significant and necessary data source to complement traditional survey data. This alternative data source can give policymakers more timely information on people's happiness, well-being or any other issue. In recent years, Google Trends™ data has been leveraged to discern trends in mental health, including depression, anxiety, and loneliness and to construct robust predictors of subjective well-being composite categories. We aim to develop a methodology to construct the first comprehensive, near real-time measure of population-level happiness using information-seeking query data extracted continuously using Google Trends™ in countries. We use a basket of English-language emotion words suggested to capture positive and negative affect based on the literature reviewed. To derive the equation for estimating happiness in a country, we employ machine learning algorithms XGBoost and ElasticNet to determine the most important words and weight the happiness equation, respectively. We use the United Kingdom's ONS (weekly and quarterly) data to demonstrate our methodology. Next, we translate the basket of words into Dutch and apply the same equation to test if the same words and weights can be used in a different country (the Netherlands) to estimate happiness. Lastly, we improve the fit for the Netherlands by incorporating country-specific emotion words. Evaluating the accuracy of our estimated happiness in countries against survey data, we find a very good fit with very low error metrics. If we add country-specific words, we improve the fit statistics. Our suggested methodology shows that emotion words extracted from Google Trends™ can accurately estimate a country's level of happiness.
Keywords: C53; C55; I31; 330; Happiness; Google Trends™; Big Data; XGBoost; machine learning