Searcharxiv⌕ Search

arXiv · 2610.03250

No Women No Innovation? The Effect of Women on Boards on Hard and Soft Innovation in SMEs

Abstract

The role of women on corporate boards and its impact on innovation remains heavily debated. While existing literature offers conflicting perspectives on whether female board representation spurs or hinders innovaiton, most research has focused exlusively on large firms. This studt adresses this gap by investigating the causal impact of women on boards on both "hard" (technological) and "soft" (non-technological) innovation within SMEs. Using a longitudinal dataset f 2762 Italian innovative SMEs from 2015 to 2024 and a 2SLS identification strategy, we find highly nuanced patterns. For hard innovation, the instrumented model reveals a robust positive causal impacr, reversing a negative baseline coefficient and exposing a severe negative selection bias. For soft innovation, a stable positive impact is confirmed. Our findings provide crucial causal evidence for SMEs' governance, while offering tailored insights for policymakers and practitioners to incentivize women' management roles in SMES.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Francesca Pascale, Saverio Barabuffi, Giulio Ferrigno. 2026-10-02. No Women No Innovation? The Effect of Women on Boards on Hard and Soft Innovation in SMEs. https://arxiv.org/abs/2610.03250

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Journalist Ideology and the Production of News: Evidence from Movers

What role do journalists play in determining the political slant of the news they produce? We develop and estimate a model where journalists and newspaper outlets contract over both slant and wages. The model implies a set of conditions under which we can consistently estimate the role of journalist preferences in driving the observed variation in slant across outlets by leveraging journalist transitions between outlets. To measure slant, we train a transformer-based, machine learning model using articles tweeted by politicians and apply it to a full-text database of 9+ million newspaper articles published in the US between 2013 and 2018. Under our model and identifying assumptions, our estimates (a) reject the hypothesis that journalists have zero ideological preferences over the content they produce and (b) imply that 10% of the variation in slant can be explained by journalists. When journalists move across outlets, their average slant shifts by 77% of the gap between their destination and origin outlet averages. In counterfactuals that reweight journalist party composition, moving from the observed left-leaning distribution to an even distribution of Democrats, Republicans, and non-partisans shifts average article slant in a more conservative direction, but only by 0.04 standard deviations in slant.

econ.GN↗

A study on healthcare expenditure in Italian regions via Symbolic Regression

The study of the factors driving the dynamics of healthcare spending is of paramount importance to guide policymakers in the allocation of resources and to measure the effectiveness of the healthcare system under consideration. The identification of these drivers can be supported by the use of machine learning techniques, which enable the discovery of hidden patterns within vast amounts of data. In contrast to black-box methods, Symbolic Regression (SR) is an approach that allows for the identification of analytical models that explicitly capture the functional relationships within the data, thus enhancing interpretability. In this paper, we present the use of SR for identifying the drivers of healthcare expenditure in Italian regions. Given the dynamic and complex nature of this phenomenon, we generated several models based on distinct temporal windows, enabling us to analyze the drivers across different time horizons. In addition to identifying the main drivers based on variable frequency in the generated models, we also conducted a study on recurring substructures. The results show that SR was able to generate models with a good level of predictive accuracy for private healthcare expenditure, enabling a reliable analysis of its driving factors, but failed to do so for public healthcare expenditure.

econ.GN↗

Daycare Matching with Siblings: Social Implementation and Welfare Evaluation

In centralized matching markets, agents may value joint assignment, as with siblings or couples. Standard preference estimation ignores such complementarities, complicating welfare analysis of priority rules for paired assignment. We develop an empirical framework incorporating these preferences and apply it to Japanese daycare assignment. Families face both additional commuting distance and a fixed disutility from split assignment. We estimate the latter at 4.61 commuting-kilometer equivalents. Our fixed-report counterfactual estimates that the reform increased mean welfare by 0.032 kilometer-equivalent units. Ignoring sibling complementarity understates welfare gains for households applying simultaneously for multiple children by about 27%.

econ.GN↗