Micro-entrepreneurs in developing countries are often constraint by inefficient supply chains, facing high travel costs and high prices in purchasing their inventory. At the same time, due to their small scale, they buy in small quantities, limiting their benefit from economies of scale, whether in bulk discounts or transport efficiencies. Small-scale food vendors in Bogotá, whose customers are residents of low-income neighborhoods, face these very issues. Based on initial research, these vendors spend an average of 15 hours per week and 20-30 percent of their weekly income travelling to the central marketplace (Corabastos) for their purchase.
Agruppa, a start-up, has developed a new mobile based technology that agglomerates orders for these small vendors. The technology system aggregates produce orders from the vendors that add up to wholesale quantities, purchases them from farmer cooperatives, and delivers them directly to the vendors. It is estimated that the bulk orders are priced an estimated 30% below the small orders. Agruppa, through its technology, not only aims to lower the inventory and travel cost of the small vendors, but also intends to measure the benefits to spillovers to the residential consumers. The study will measure the impact of these direct effects of the technology, as well as the indirect effects, which are the loss of sales of the competitors who do not take-up the technology.
This impact evaluation will contribute to a larger question of how technology can be utilized to the benefit of MSMEs. This study will specifically look at 1) technology’s role in connecting small vendors to larger suppliers, 2) its ability to create value through new modes of transaction, and 3) its effects in creating competition.
Given that the vendors are located in densely populated residential areas, many of them are geographically close to one another. Thus, the evaluation takes a randomized block design, where the micro-entrepreneurs are divided into approximately 60 equal blocks, using major roads and natural geographic boundaries. These blocks are then randomly assigned to treatment and control blocks. It is assumed that within each block there will be interested vendors and uninterested firms. Thus, the evaluation measures the direct impact of the program by comparing the interested firms across the treated and untreated blocks, and the indirect impact by comparing the uninterested firms. The diagram below illustrates this: