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NAÏVE ECONOMIC THEORIES IN ITALIAN PRIMARY SCHOOL CHILDREN: A QUALITATIVE ANALYSIS OF ECONOMIC CONCEPTS ACROSS AGE AND SOCIAL CONTEXTS
Free University of Bozen-Bolzano (ITALY)
About this paper:
Appears in: EDULEARN26 Proceedings
Publication year: 2026
Article: 0698
ISBN: 978-84-09-88444-5
ISSN: 2340-1117
doi: 10.21125/edulearn.2026.0698
Conference name: 18th International Conference on Education and New Learning Technologies
Dates: 29 June-1 July, 2026
Location: Palma, Spain
Abstract:
Economic education has gained increasing prominence in the international landscape in recent years, in response to the growing complexity of economic systems, the digitalization of financial instruments, and the expansion of access to services. While these processes have fostered inclusion and participation, they also require the development of specific competencies to enable informed and responsible economic citizenship. However, available data indicate low levels of competence across all age groups, with particularly critical gaps among younger individuals and those from socio-economically disadvantaged backgrounds.

In Italy, the official introduction of financial and economic education into the primary school curriculum in 2024 represents a significant milestone. Yet, this innovation risks becoming a purely transmissive intervention if it is not grounded in a thorough understanding of the spontaneous representations children construct regarding economic phenomena. Research has shown that children develop naïve economic theories from an early age concerning concepts such as money, exchange, work, price, and wealth, primarily through processes of family socialization. These representations constitute interpretative frameworks that shape children’s understanding of the social and economic world. In the Italian context, however, empirical studies on naïve economic theories remain limited and largely date back to the 1980s, making it necessary to update research in light of the economic, technological, and social transformations that have occurred in recent decades.

This contribution aims to analyze the naïve economic theories of a stratified sample of 200 children aged 6 to 10 from Northern Italy, with the objective of exploring how they conceptualize key economic phenomena related to money, exchange and value. The study adopts a comparative qualitative design, considering two main variables – age and social context (rural vs. urban) – in order to investigate possible differences in the developmental trajectories of economic representations.

Data were collected through semi-structured focus groups and analyzed using Qualitative Content Analysis, which enabled the development of a coding system composed of concept-driven categories derived from the theoretical framework and data-driven subcategories emerging from the empirical material. The analysis also involved the construction of comparative matrices by age group and social context, to identify recurring interpretative configurations or discontinuities.

The findings reveal a shift from concrete and normative conceptions to more abstract and systemic representations. Development appears to be non-linear, shaped by the dynamic interactions of cognitive development, social context and economic shifts – highlighted by children's early grasp of digital money, electronic cards, and cryptocurrencies. Notably, rural children anchor money to ethical and communal values, while urban peers emphasize electronic payments, virtual currency, success, and consumption, reflecting differentiated socialization trajectories.

The analysis of naïve theories therefore becomes epistemologically and pedagogically crucial: understanding children’s prior knowledge allows for the design of financial and economic education pathways that are developmentally appropriate and capable of fostering progressive conceptual restructuring, rather than the mere superimposition of adult-centered economic models.
Keywords:
Naïve economic theories, economic and financial education, primary school, family socialization, Qualitative Content Analysis.