This dataset is a preliminary version of the data described in Iacovone and McKenzie (forthcoming). It conforms to an earlier blog post written by one of the authors (McKenzie, 2017) and contains data collected during a randomized controlled trial on supply chains among fresh produce vendors in Bogota, Colombia.
Format
A data frame with 3336 rows and 8 variables:
- b_block
business block (geographic identifier)
- b_pair
randomly assigned treatment pairs (plus one triplet)
- b_treat
treatment status (1 = yes, 2 = no)
- dayscorab
number of days that week requiring visits to Corabastos central market
- b_dayscorab
baseline of the previous variable
- miss_b_dayscorab
dummy for missing baseline information (1 = yes, 2 = no)
- round2, round3
survey round dummies
Details
The RCT studies the impact of purchase aggregation by many microenterprises (here: fruit and vegetable vendors), which enables a reduction in costly individual visits to a large central market. My thanks to David McKenzie for sharing the data.
References
McKenzie, D. (2017) "Finally, a way to do easy randomization inference in Stata!", Development Impact (World Bank blog). https://blogs.worldbank.org/impactevaluations/finally-way-do-easy-randomization-inference-stata.
Iacovone, L. and McKenzie, D. (forthcoming) "Shortening Supply Chains: Experimental Evidence from Fruit and Vegetable Vendors in Bogota", Economic Development and Cultural Change. https://doi.org/10.1086/714050.