"background": "Understanding the determinants of technology adoption in agricultural manufacturing is critical for enhancing productivity and food security. However, longitudinal analyses of adoption dynamics within this sector in sub-Saharan Africa are scarce, hampered by a lack of structured, plant-level panel data. ", "purpose and objectives": "This Data Descriptor presents and methodologically evaluates a novel panel dataset designed to diagnose the rates and drivers of advanced manufacturing system adoption among agricultural processing plants. Its objective is to provide a robust framework for analysing temporal adoption patterns and their covariates. ", "methodology": "Data were collected via annual structured surveys from a stratified random sample of registered plants. The core econometric model for adoption analysis is a linear probability model with plant fixed effects: Adopt{it = \ + \ Xit +, where Adopt₈ₓ is a binary indicator for system use. Inference relies on cluster-robust standard errors at the district level to account for spatial correlation. ", "findings": "The dataset enables diagnostic measurement of adoption intensity and its predictors. An initial diagnostic application reveals a positive preliminary association between access to technical extension services and the probability of adoption, with a coefficient of 0. 15 (95% CI: 0. 08, 0. 22). The structure permits analysis of time-varying factors like input costs and policy shocks. ", "conclusion": "The constructed panel-data framework provides a methodologically sound foundation for rigorous, longitudinal research on technological change in the agricultural manufacturing sector, filling a significant data gap. ", "recommendations": "Researchers should utilise the panel structure to control for unobserved heterogeneity and model dynamic adjustment processes. Policymakers can employ the diagnostic framework to target interventions based on identified adoption barriers. ", "key words": "technology adoption, panel data, agricultural manufacturing, fixed effects model, Uganda, diagnostic framework", "contribution statement": "This paper provides the first publicly available, plant-level panel dataset coupled with a tailored econometric framework for diagnosing adoption rates of manufacturing
Kigozi et al. (Sat,) studied this question.
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