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This study aims to investigate the relationship between family managers and firms’ risk levels in a context characterized by low investor protection and firm opacity. Specifically, this paper examines whether the level of risk faced by firms is affected by family shareholders’ ownership stake and activism.

Corporate governance data were hand-collected for a sample of 90 Italian listed companies and 540 observations from the year 2018. Regression analysis was then used to test the research hypotheses.

This study provides evidence of a positive association between active family ownership and risk faced by sampled firms. This study also finds that the number of inside directors is negatively correlated with firms’ risk-taking. Overall, the results confirm family managers’ influence on firms’ risk choices and show consistency with theoretical arguments in favor of hiring professional managers to guide family-owned firms.

Practical implications emerge from the study findings. First, family owners should consider to hire a larger number of professional managers to support firms’ wealth maximization and retention and to reduce default risks. Second, investors should take into account the firms’ board of directors and management composition to better assess the investments risk level. Finally, the positive correlation between active family owners and systematic risk suggests the opportunity for regulators to improve the legal requirements related to minority directors to increase their effectiveness and, therefore, minority shareholders’ protection.

This study extends the literature on the association between ownership structure and firms’ risk levels, showing the effect of family managers on firms’ risk levels. Besides, to the best of the authors’ knowledge, no previous study investigates professional executives’ influence on risk when family ownership prevails.

This paper presents the second-generation estimates for the Italian engineering industry in 1911, a year documented both by the customary demographic census, and the first industrial census. The first part of this paper uses the census data to estimate the industry’s value added, sector by sector; the second further disaggregates each sector by activity, and estimates the value added, employment, physical product, and metal consumption of each one. A third, concluding section dwells on the dependence of cross-section estimates on time-series evidence. Three appendices detail the specific algorithms that generate the present estimates; a fourth, a useful sample of firm-specific data.