VoxDev Development Economics podkast

S7 Ep54: Tracking Kenya's tractors

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A tractor can plough an acre of Kenyan maize land in about an hour and a half. By hand, it takes a farmer nearly a week. Few smallholders can afford to buy a tractor, so they rent one. The tractor’s owner often lives in town, and employs an operator to drive the tractor to the fields and do business. To minimise risk, the owner keeps their tractor close to home, even when the best work is far away.

Sophie Nottmeyer studied what changed when owners could observe exactly where the tractor was. She used GPS records from about 1,200 tractors fitted with a tracking app from the start-up Hello Tractor, covering close to a million fields, and satellite images of the land they worked. As owners learned to use the app, their tractors travelled further, finding better business opportunities. A year after adoption, they worked about 55 km further from home than in their first month.

Nottmeyer presented the research at STEG's sixth Annual Conference in Nairobi in January 2026. She tells Tim Phillips what satellites reveal about the fields the tractors reached, and why a fuel subsidy that did the same job would cost hundreds of times more.

The research behind this episode

Nottmeyer, Sophie. 2025. "Trac(k)tors of Change: Monitoring, Tractor Mobility and Agricultural Mechanization in Kenya." Working paper, November 2025. A summary is available as a STEG policy brief: "Following the tracks: how digital monitoring expands access to agricultural mechanisation across space" (2026).

To cite this episode

Phillips, Tim, and Sophie Nottmeyer. 2026. "Tracking Kenya's tractors." Conversations on Transformation (podcast).

About the guest

Sophie Nottmeyer is a Research Economist in the Directorate General of Economic Research at Banco de Mexico. She was previously a Postdoctoral Researcher at Universitat Pompeu Fabra in Barcelona, and holds a PhD in Economics from CEMFI in Madrid. Her research spans market access, organisational frictions and informal institutions, and how they interact with geography and climate. She has also worked with remote-sensing scientists to estimate maize yields in Kenya from satellite data.

Research cited in this episode

Hello Tractor is an agricultural technology company based in Nairobi. In 2018 it introduced a GPS tracking device for tractors, fitted by a mechanic, with a mobile and web app that shows owners where their tractor is in real time. The app reports how many jobs the tractor did, how many acres it worked, how long it ran and how far it drove, and it can replay the tractor's exact path across a field. In 2023 owners paid US$125 to install the device and US$60 a year to use the app. Nottmeyer's data cover every job done by tractors that adopted it between 2018 and 2025, about 917,000 in total.

Moral hazard arises when one party cannot observe what another does on its behalf. Kenyan tractor owners hire operators, who are often paid from the revenue they collect. Because the owner cannot see how many acres were ploughed, an operator can under-report jobs, take unauthorised work or use fuel for other purposes. Owners respond with surprise visits, fuel checks and rules that the tractor must come home every night. All of these work only if the tractor stays nearby. The problem is sharper because many owners are not farmers at all; according to Kenya's 2019 census, 33% of tractor owners live in cities.

Misallocation describes an economy in which capital or labour does not go where it would produce the most. Chang-Tai Hsieh and Peter Klenow showed in "Misallocation and Manufacturing TFP in China and India" (Quarterly Journal of Economics, 2009) how large the cost of such gaps can be. Nottmeyer measures misallocation as the spread in the returns that tractors earn in different places. If a tractor would earn more in a distant district than near home, but never goes there, the economy loses output.

Counting Kenya's tractors is harder than it sounds. No official source records tractor ownership or use, Kenya's last nationwide agricultural census was in 1979, and the Food and Agriculture Organization stopped publishing its global series on agricultural machinery in 2009. The roughly 1,225 tractors in Nottmeyer's data represent between 1.73% of the tractors counted by the 2019 census and 8.75% of the last FAO figure, from 2008.

Comparing each tractor with itself is how Nottmeyer deals with the lack of a control group. The data begin only when a tractor adopts the app, so she uses each tractor's first month as its baseline, on the assumption that owners take time to trust the information and change how they manage operators. The average distance from home was 70 km in that first month. It rose by about 30 km after six months, 55 km after one year and 112 km after two years. The pattern is similar for early and late adopters, for older tractors whose owners had run them before fitting the device, and for tractors that return home every night. Nottmeyer does not claim these estimates are strictly causal, but the alternative explanations she tests do not account for the pattern.

Satellite measures of crop growth stand in for yields, which nobody records at field level in Kenya. Nottmeyer uses Sentinel-2 images from the European Union's Copernicus programme to calculate the Normalized Difference Vegetation Index (NDVI), which measures how green and dense vegetation is. Ploughing leaves a field bare at the start of the season, so the gap between the lowest and highest NDVI in a year captures both land preparation and crop growth. She matches 33,256 fields worked by monitored tractors with similar fields nearby. In the year of the visit, the gap rises by about 0.4 standard deviations more in the visited fields, and part of the gain persists in later years. Because some of the comparison fields may also have been ploughed by other tractors, this is a lower bound on what mechanisation does.

A spatial model of tractor location turns these results into national figures. Owners choose where to send a tractor by weighing the returns in each place against the cost of distance, and monitoring lowers that cost. When all Hello Tractor tractors behave as if they had five years of experience with the app, the spread of returns across places falls by 15.9% and total output rises by 2.18%, compared with tractors that have just adopted it. If every tractor in Kenya had the technology, output would rise by 1.3%.

A fuel subsidy is the obvious alternative policy, because it makes long trips cheaper. Nottmeyer calculates that to achieve the same gains, a subsidy would have to cover almost all the fuel a tractor uses per kilometre, at a total cost of about US$9.33 billion. Fitting all 70,930 tractors in the 2019 census with the device and a five-year subscription would cost about US$30.1 million.

Monitoring in other industries follows the same logic wherever an owner hands a vehicle to someone else to operate. Thomas Hubbard's "The Demand for Monitoring Technologies: The Case of Trucking" (Quarterly Journal of Economics, 2000) studied the onboard computers that US trucking firms installed to track drivers. Erin Kelley, Gregory Lane and David Schonholzer ran an experiment with Nairobi's minibus owners, reported in "Monitoring in Small Firms: Experimental Evidence from Kenyan Public Transit" (American Economic Review, 2024), and found that tracking devices helped owners manage drivers and raised profits.

More Conversations on Transformation episodes

Moving to profitability. Carolyn Pelnik asks why street vendors and motorcycle taxi drivers in Kampala stay in less profitable parts of the city, another case in which mobile workers and assets do not go where they would earn most.

Slack and economic development. Felix Samy Soliman explains why firms in Kenya leave labour and capital idle, and how much growth that unused capacity could deliver.

Further reading

Misallocation and Product Choice, a STEG working paper by Stepan Gordeev and Sudhir Singh, uses farm and crop data from India to estimate how much output is lost when inputs are spread unevenly between farms, and shows that the answer depends on which crops farmers can choose to grow.

Tillers of Prosperity: Land Ownership, Reallocation, and Structural Transformation, a STEG working paper by Shuhei Kitamura, finds that when the land reform enforced by the Allies after the Second World War gave tenants ownership of their farms, they adopted low-cost farm technology, relied less on family labour, and more young people left for the cities.

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