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Private rental markets can help small-scale producers in developing countries overcome capital indivisibilities and adopt productive technologies, yet they remain limited in scope. This paper studies the role of monitoring problems on the supply side of tractor rental markets in Kenya, where moral hazard between tractor owners and operators discourages owners from sending tractors to remote areas, limiting the efficient spatial allocation of scarce capital and, thus, access to mechanization. To study this mechanism, I evaluate the impact of a new GPS tracking app that allows owners to monitor their operators remotely, combining unique tractor GPS data from around 1,200 tractors and 900,000 georeferenced fields, satellite data and an original farmer survey with a quantitative spatial model. After adoption, monitored tractors gradually expand their range of operations and reallocate toward areas where potential returns to mechanization are higher, consistent with a reduction in the monitoring cost and a more efficient spatial allocation. Further, fields visited by monitored tractors experience larger increases in remotely-sensed vegetation growth than similar nearby fields, suggesting productivity gains at destination. Finally, I develop and estimate a quantitative spatial model of tractor location choice that incorporates monitoring cost to quantify the aggregate gains from the technology. The results indicate that digital monitoring raises output by 2%, reducing spatial misallocation by 15%, and is more cost-effective than an equivalent subsidy.

This paper analyzes how pervasive informality shapes the distribution of productivity gains typically associated with larger cities in Brazil. Using Census data, it shows that informality attenuates the urban wage premium by generating differential returns to density across worker types. Decomposing employment density into formal and informal components, the analysis reveals that formal workers experience strong wage gains from formal density, which are partially offset by a negative effect from informal density, while informal workers only benefit from formal density. These findings suggest that productive advantages of cities are primarily driven by the presence of formal workers, and that informality alters the composition of productivity spillovers in urban labor markets of emerging economies.

Crop yield mapping is essential for food security and policy making. Recent machine learning (ML) and deep learning (DL) methods have achieved impressive accuracy in crop yield estimation. However, these models require numerous training samples that are scarce in regions with underdeveloped infrastructure. Furthermore, domain shifts between different spatial regions prevent DL models trained in one region from being directly applied to another without domain adaptation. This effect is particularly pronounced between regions with significant climate and environmental variations such as the U.S. and Kenya. To address this issue, we propose using fine-tuning-based transfer learning, which learns general associations between predictors and response variables from the data-abundant source domain and then fine-tunes the model on the data-scarce target domain. We assess the model's performance on estimating corn yields using Kenya (target domain) and the U.S. (source domain). Feature variables, including time-series vegetation indices (VIs) and sequential meteorological variables from both domains, are used to pre-train and fine-tune the deep neural network model. The model is fine-tuned using data from 5 years (2019-2023) and tested using leave-one-year-out cross validation. The fine-tuned DNN achieves an overall R2 of 0.632—higher than both the U.S.-only and Kenya-only baselines—but paired significance tests show no aggregate difference, though a statistically significant gain does occur in 2023 under anomalous heat conditions. These results demonstrate that fine-tuning can reliably transfer learned representations across continents and, under certain climatic scenarios, yield meaningful improvements.