Data and Code for: Land Rental Markets: Experimental Evidence from Kenya

Principal Investigator(s): View help for Principal Investigator(s) Michelle Acampora, ETH Zurich; Lorenzo Casaburi, University of Zurich (Switzerland); Jack Willis, Columbia University

Version: View help for Version V1

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AER_dta			02/12/2025 02:29:PM
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AER_out			02/12/2025 02:30:PM
AER_surveys			02/12/2025 02:29:PM
README.pdf	Unknown	232 KB	08/22/2024 02:51:AM
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README_.pdf	application/pdf	319.8 KB	02/12/2025 10:14:AM
README_.rtf	application/rtf	301 KB	02/09/2025 05:31:PM

Project Citation:

Acampora, Michelle, Casaburi, Lorenzo, and Willis, Jack. Data and Code for: Land Rental Markets: Experimental Evidence from Kenya. Nashville, TN: American Economic Association [publisher], 2025. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2025-04-22. https://doi.org/10.3886/E208341V1

Project Description

Summary: These files contain the code and data for the journal article "Land Rental Markets: Experimental Evidence from Kenya", American Economic Review

Abstract

Do land market frictions cause misallocation in agriculture? In a field experiment in Western Kenya, we randomly subsidize owners to rent out land. Induced rentals mostly persist after the subsidy ends and increase output and value added, consistent with misallocation. Gains from trade arise from renters choosing higher-value crops, having higher productivity, and adopting more non-labor inputs, while, perhaps surprisingly, renters use similar quantities of labor as owners. Induced rentals are not those with the largest predicted gains, underlining the importance of the joint distribution of gains and frictions, with frictions arising from search, risk, and learning.

Funding Sources: Swiss National Science Foundation (181127); European Union’s Horizon 2020 research and innovation programme (851961)

Scope of Project

Subject Terms: Randomized Control Trial; Land markets; Misallocation

JEL Classification:
      O12 Microeconomic Analyses of Economic Development
      O13 Economic Development: Agriculture; Natural Resources; Energy; Environment; Other Primary Products
      Q15 Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment

Geographic Coverage: Kenya

Time Period(s): 2019 – 2021

Collection Date(s): 2019 – 2021

Universe: Farmers in Western Kenya

Data Type(s): experimental data; survey data

Methodology

Response Rate: The experimental sample consisted 521 Target Plots, their owners, and their renters if rented out. We attempted to survey them in the baseline (where the "renter" was the person who would be renting the plot in the subsequent season) and in each of the four subsequent seasons. We have two types of outcomes: Target Plot outcomes, coming from the survey of whoever managed the plot that season (the owner, or the renter if rented out), and owner-level outcomes, coming from the owner survey. Response rates across the seasons for Target Plot outcomes were: 97% in season 0, 94% in season 1, 95% in season 2, 93% in season 3, and 91% in season 4. Response rates across the seasons for owner outcomes were: 100% in season 0, 98% in season 1, 98% in season 2, 97% in season 3, and 94% in season 4. (See Appendix Table D.6 in the paper)

Sampling:

Enumerators visited 161 villages in four West Kenyan counties (Bungoma, Kakamega, Migori and Siaya) and conducted a brief listing module with 7,405 plot owners. Each respondent answered a short section on demographics and listed each of their owned plots. For each plot, we asked questions on size, distance from the respondent’s house, and use —cultivation, fallowing, and renting out —for both the 2019 Long Rains season and the upcoming 2019 Short Rains.

At the end of the listing survey, we asked whether the respondent would be interested in receiving a subsidy (“top up”) for renting out one plot among those she was not already planning. For ethical considerations, only owners with at least two plots (N=5,350) were eligible for the subsidy. Each of these interested owners was then asked to identify one plot they would be interested in renting out, should they receive the rental subsidy. We refer to this plot as the ‘Target Plot’ and restricted the experimental sample to such "potential complier" owners.

Due to time constraints with the approaching season, we attempted to baseline only 767 of the 877 interested owners and tracked and interviewed 618 of them (80.5%). After applying basic sample restriction criteria, our final study sample included 521 owners interested in the subsidy (and their Target Plots). Sample restriction criteria included: outliers in plot size, enumerator reporting the wrong subsidy amount to the respondent, owners reporting they had already rented out the plot or that they did not own the plot or that they did not expect to be able to find a renter. Appendix Table A.1 in the paper compares listing data for interested owners who were surveyed (and thus entered the study) and those who weren’t. The two groups have similar baseline characteristics.

Data Source: Household surveys

Collection Mode(s): face-to-face interview; on-site questionnaire; telephone interview

Scales: Several Likert-type scales were used including in questions about confidence in finding a renter (listing C5) and farming experience (baseline B15)

Weights: N/A

Unit(s) of Observation: Farmer

Geographic Unit: Village

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