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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.	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, and the distribution of those gains, measured by the log-utility Atkinson index of final payoffs.
Last Published	August 20, 2019 10:46 AM	November 27, 2019 02:57 PM
Intervention End Date	December 31, 2019	November 29, 2019
Primary Outcomes (End Points)	Overall trading efficiency. Trading efficiency broken down into a) Trading efficiency due to defragmentation, b) Trading efficiency due to sorting	Overall trading efficiency. Trading efficiency broken down into a) Trading efficiency due to defragmentation, b) Trading efficiency due to sorting, c) Trading efficiency due to exposure losses Distribution of final payoffs measured by the log-utility Atkinson index.
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)	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) Trading efficiency due to exposure losses (share of potential gains foregone due to some participants holding "too much" land) Distribution of final payoffs measured by the log-utility Atkinson index (1 minus the ratio of the geometric and arithmetic means of final payoffs)
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.	68 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.
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.	204 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.	All villages play the simple treatment once and the complex treatment once, so 68 simple and 68 complex. The trading day is played at the end of week 2, so we will have 34 observations for simple post-trading day and 34 for complex post-trading day.