Resumo

Financial education is a topic that is becoming increasingly important worldwide. Rising life expectancy and declining birth rates have led to changes in social security and pension systems around the world. The focus of financial decisions shifted from institutions to individuals, imposing on workers the responsibility to save, invest, and spend wisely throughout their life cycle.

The use of digital media has proved to be a way to broaden access to financial education, as technology is increasingly widespread in people’s lives and can be used as an ally to spread knowledge in financial education. Several channels on YouTube discuss financial education, ranging from professional financial advisors to amateurs sharing their financial experiences.

This study aims to characterize supply of financial education (content posted by YouTubers) and demand for financial education (visualization of content by users) to understand how interested people are with this theme.

We reviewed a theoretical background of financial education content, wich was then related to the empirical results obtained from YouTube. The empirical work involves documentary research of the audio transcripts of YouTube video totaling approximately 1,507 hours in length. To analyze that data as objectively as possible, we used probabilistic topic modeling, a text-mining technique based on machine learning. Topic modeling algorithms are statistical methods that analyze the words of the original texts to find out the themes that compose them and how they are connected.

The analysis of content offered and demanded allowed us to infer that Brazilians looking for financial education content on YouTube are more concerned with less sophisticated and more short-term topics. YouTubers, on the other hand, offer many videos on investments, although the number of views is relatively lower. People watching personal finance videos on YouTube seem more concerned with balancing their budgets and improving their consumption patterns. These are commendable concerns, but they indicate a warning situation.

The contribution of this paper is twofold. First, it contributes to the literature in the field of financial education by consolidating a category structure for financial education content, validated empirically by topic modeling. Moreover, these results may contribute to the development of public policies that make the population aware of the importance of long-term financial planning. For instance, understanding public response to financial education themes may help to improve courseware and other teaching material.