Sophie Nottmeyer
Low agricultural productivity in developing countries is often linked to limited mechanization. Using tractors for land preparation can significantly increase farm productivity, but most farmers are too small to afford their own. In principle, rental markets could overcome this barrier and help them mechanize. In practice, however, they remain underdeveloped in agriculture.
Tractors – unlike stationary machinery – need to move across space to be shared efficiently, creating unique challenges. Tractor owners typically hire operators to drive their tractors and provide mechanization services to farmers. However, they cannot easily observe the operators’ actions, giving rise to moral hazard. For example, operators may underreport the number of jobs or acres serviced, which lowers owners’ revenue and raises maintenance costs. To mitigate these risks, owners prefer to keep tractors close to home or in areas where they can rely on trusted contacts to supervise and control them directly, e.g. through unannounced field visits or checking fuel consumption upon return.
However, the areas where tractor owners are willing to send their tractors are not necessarily those where they are most needed or most productive. Two features likely exacerbate the monitoring problem and this resulting gap between where tractors are and where they are needed. First, with rapid urbanization tractors are increasingly owned by entrepreneurs and wage workers based in cities, who invest in them to rent out to farmers on the side, often far from the most productive rural areas. Second, because planting seasons differ across regions, tractors are in demand only briefly in any one location, making efficient and profitable use dependent on their ability to move with the rains.
In my job market paper, I study how these monitoring frictions matter for the spatial allocation of tractors in Kenya, leveraging the introduction of a GPS tracking app for tractors that allows owners to monitor their operators remotely. Using georeferenced activity records from all tractors that adopted the new monitoring technology – covering about 1,200 tractors and 900,000 fields over seven years – along with home locations inferred from nighttime pings, I show how digital monitoring changes spatial patterns of tractor use and how this affects productivity.
Does digital monitoring increase tractor mobility?
To assess the effects of digital monitoring, I look at how each tractor’s activity evolves over time compared to the first month after adoption, when owners and operators are still adapting to monitoring. I find that tractors gradually extend their range of operations, servicing jobs that are on average 55 km further away from home after one year of monitoring, which corresponds to an increase of about 80% over the baseline mean. This pattern reflects greater mobility and is consistent with a reduction in the moral hazard problem, as owners build trust and learn to use the additional information from the app to better manage their operators.
To be continued Thursday next week.
