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A lab-in-field experiment on land trade complexity and market centralization
Last registered on August 20, 2019

Pre-Trial

Trial Information
General Information
Title
A lab-in-field experiment on land trade complexity and market centralization
RCT ID
AEARCTR-0004581
Initial registration date
August 16, 2019
Last updated
August 20, 2019 10:46 AM EDT
Location(s)

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Primary Investigator
Affiliation
Institute for International Economic Studies
Other Primary Investigator(s)
PI Affiliation
Maastricht University & UNU-MERIT
PI Affiliation
University of Melbourne
PI Affiliation
London School of Economics
Additional Trial Information
Status
In development
Start date
2019-08-18
End date
2020-08-31
Secondary IDs
Abstract
We study the effects of complexity and market centralization in a land trading game. The game involves groups of 18 participants in villages in Masaka, Uganda. Participants are endowed with game currency and a set of artificial land plots on a "map", and trade plots among themselves to earn returns. They earn returns if they improve the efficiency of the land allocation in the game, which can be achieved by sorting higher "ability" players to better plots and/or by consolidating fragmented plots into contiguous blocks.

Our treatment variations are
1. Map complexity: some maps are "simple" because there are many possible payoff-equivalent first-best allocations of plots. Some maps are "complex" because there are only a few. Complexity is varied by minimally altering maps to add non-trading plots. These decrease the number of possible ways to arrange contiguous blocks of plots into the map.

2. Trade centralization: trade is either "decentralized," with participants trading free-form among themselves within the village, or "centralized," taking place with all participants present in the same location at the same time.

We will study the effects of these treatments on trading efficiency, measured by the share of possible gains from trade achieved in the game.
External Link(s)
Registration Citation
Citation
Bryan, Gharad et al. 2019. "A lab-in-field experiment on land trade complexity and market centralization." AEA RCT Registry. August 20. https://doi.org/10.1257/rct.4581-1.0.
Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2019-08-19
Intervention End Date
2019-12-31
Primary Outcomes
Primary Outcomes (end points)
Overall trading efficiency. Trading efficiency broken down into a) Trading efficiency due to defragmentation, b) Trading efficiency due to sorting
Primary Outcomes (explanation)
Overall trading efficiency (share of potential gains from trade realized)
Trading efficiency due to defragmentation (share of potential gains from defragmenting land)
Trading efficiency due to sorting (share of potential gains from sorting high types to high quality land)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We play a series of land trading games, in villages, over the course of 2 weeks and three meetings. The games involve trading artificial land titles, which correspond to plots on a generated "map." Participants trade amongst themselves using game currency, and earn returns according to the gains from trade they achieve. Gains from trade can be achieved by sorting higher "ability" players onto better land, and consolidating fragmented plots into contiguous blocks. Trade is done in free-form bargaining between players. Maps can be either "simple" or "complex" and trade can be "decentralized" or "centralized."
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
Village and week.
Each village will play either a simple map in week 1 and a complex map in week 2, or complex in week 1 and simple in week 2.
At the end of week 2 trade, all villages play a "trading day"
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
64 villages

Each village plays a simple map followed by a complex map, or a complex map followed by a simple map. The second week's game is also followed by a trading day.
Sample size: planned number of observations
192 in total. 3 observations per village: a week 1 trading efficiency outcome, a week 2 pre-trading day efficiency outcome, and a week 2 post-trading day efficiency outcome.
Sample size (or number of clusters) by treatment arms
All villages play the simple treatment once and the complex treatment once, so 64 simple and 64 complex.

The trading day is played at the end of week 2, so we will have 32 observations for simple post-trading day and 32 for complex post-trading day.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Mildmay Uganda Research Ethics Committee (MUREC)
IRB Approval Date
2018-07-18
IRB Approval Number
0406-2018
IRB Name
Research Ethics Committee, London School of Economics
IRB Approval Date
2018-05-29
IRB Approval Number
000718