Sophie Nottmeyer
Where do tractors go?
The previous result suggests that tractors expand into new areas over time. To understand how these areas differ, I link job locations to agro-ecological potential yields from the FAO-GAEZ database via location and compute the marginal return to mechanization for each location as the difference in predicted yields between high- and low-input scenarios. Using the same specification, I find that tractors increasingly shift toward areas where marginal returns are higher (Figure 2). This suggests that the observed reallocation after adoption is efficiency-enhancing, as owners give their operators more freedom to experiment and learn where demand is high.
What about actual yields?
Next, I examine whether greater mobility and improved sorting translate into higher agricultural productivity, using satellite data. I construct a proxy for productivity based on a measure of vegetation health and density (Normalized Difference Vegetation Index), taking the difference between its value at the peak and the start of the growing season. This captures mechanization, as fields that were prepared with a tractor would start bare and green up more strongly after planting. I then compare each serviced field to a nearby field with similar pre-treatment characteristics before and after the tractor visit. I find that fields serviced by a monitored tractor experience a large jump in field-level vegetation growth in the year of the visit (about 0.4 SD). Since the control fields may or may not have been mechanized, this estimate represents a lower bound on the overall effect of mechanization on productivity, or the differential effect of monitoring relative to average mechanization levels.
Quantifying aggregate efficiency gains
Because measuring aggregate output effects empirically is difficult and would miss equilibrium effects of reallocating scarce capital across locations, I develop a quantitative spatial model of tractor location choice to quantify the gains from reduced monitoring frictions. In the model, tractor owners decide where to send their tractor given some cost of moving that include monitoring costs. Better monitoring makes these choices less sensitive to distance. I calibrate the model using my unique GPS data and structurally estimate how digital monitoring shifts these costs, exploiting the same gradual adaptation process as documented above. Simulating equilibria where tractors behave as if they had just adopted digital monitoring versus after five years of experience, I find that improved monitoring reduces spatial misallocation by about 15% and raises aggregate output by around 2%.
Takeaways
This paper shows how monitoring frictions on the supply side of tractor rental market can constrain tractor mobility and limit the efficient spatial allocation of capital. The findings highlight how digital technology can be used as an easily scalable and cost-effective tool to expand farmers’ access to mechanization and raise agricultural productivity. More broadly, the results suggest that reducing information frictions in sectors involving the delegated operation of mobile capital (e.g. trucking, ride-hailing, public transportation) can unlock productivity gains in developing countries and beyond.
Sophie Nottmeyer is a recent PhD graduate and Job Market Candidate at CEMFI.
Source: World Bank Blog
