Assessing the Productivity Effects of Data Analytics and Supply-Chain Management Technologies in Canada’s Agri-Food Sector

This paper delivers the first causal estimates of how two core digital‐technology families, supply‐chain management (SCM) and data analytics, affect labor productivity in Canada’s agri‐food sector. Drawing on a panel of 5,733 firm‐years from 2018 to 2022 (654 producers, 763 processors, and 4,316 distributors/retailers), we employ the Callaway & Sant’Anna (2021) multiple‐period difference‐in‐differences estimator to quantify average treatment effects overall, by adoption cohort, and by period. Our findings show that data‐analytics adoption, contingent on sufficiently robust IT investments, generates substantial productivity gains for processors and, to a lesser extent, for producers. In contrast, isolated SCM adoption can prove counterproductive, particularly for retailers and distributors. This granular segmentation sheds new light on the heterogeneous impacts of digital innovations and complements prior, mainly descriptive or qualitative, analyses. These results suggest that digital‐transformation support policies should be tailored to each segment of the agri‐food supply chain. For producers and processors, strengthening IT infrastructure and targeted training programs is essential to convert data‐analytics adoption into measurable productivity improvements. Simultaneously, retailers would benefit from integrated governance of SCM tools to mitigate potential negative effects of standalone adoption and to ensure continuity of IT funding, especially to buffer against exogenous shocks that can disrupt the value chain.