Improving the allocation of land use subsidies through self-selection in Malawi


Many social programs are geared towards recipients with high benefits or low costs of participation. However, implementers often cannot observe the full costs or benefits to participants, which prevents them from efficiently targeting who should participate. In this case, encouraging beneficiaries to self-select to participate in a program may help programs get to the right people by allowing implementers to overcome this lack of information.

One potential application of this strategy is payments for ecosystem services, such as incentivizing conservation or reforestation. Planting trees generates large social benefits as well as benefits to landowners. However, the benefits to landowners—such as erosion control and soil fertility—accrue slowly over time, which can discourage landowners from planting. Payments for ecosystem services compensate farmers for these private costs through direct payments for the environmental benefits they generate. We tested the impact of a subsidized tree planting program in Malawi, evaluating whether allocating contracts randomly or through self-selection (an auction) resulted in better program outcomes.


The reforestation program required participating farmers to plant 50 seedlings on half an acre of private land. Our partners at the World Agroforestry Center (ICRAF) distributed the seedlings, trained farmers in planting and care, and monitored tree survival at regular intervals—six months, one year, two years, and three years after the start of the program. At each inspection, households received a payment per surviving tree. At the beginning and end of the three-year contract, researchers surveyed households on their land use, their ability to implement the tree planting contract, and socioeconomic outcomes such as per capita spending on consumption, total household income, and food shortages. 

Prior to the program rollout, ICRAF invited households owning at least one acre of land to an event to learn more about the tree-planting program. Households that attended were randomly assigned to one of two allocation mechanisms: an auction (self-selection) or a lottery (random). After hearing a detailed explanation of the tree-planting program and the payment structure, the auction group submitted sealed bids with the lowest price that would make them willing to accept the tree-planting contract. Of the 228 individuals assigned to the auction mechanism, the 85 individuals with the lowest bids were ultimately enrolled in the program. The lowest rejected bid from the auction was then offered to the lottery group as the final contract price. Among the 204 households who agreed to participate in the program at this price, 91 were selected through the lottery. Households that lost the lottery served as the comparison group.

Key Findings

Self-targeting outperformed random allocation of contracts in terms of tree survival and mitigated increases in land clearing and labor shortages. Researchers also ran a simulation with baseline household data to compare self-selection with targeting participant farmers based on observable characteristics. The simulation showed that targeting on observables can select farmers who will have better tree survival outcomes, but not as well as self-selection. Furthermore, collecting the amount of household information needed to implement a targeting rule may be very costly or logistically difficult. Self-selection may therefore be a more practical way for implementers to choose participants who will maximize the benefits of a program.


This project was a collaboration with partners at Ecobank, Harvard University, USAID, and the World Agroforestry Center (ICRAF)  with support from the Abdul Latif Jameel Poverty Action Lab.