Microenterprise Supply Chain Intermediation Pilot

The intervention consists of leveraging mobile phone technology to aggregate small vendors’ demand for produce, creating collective orders which add up to wholesale quantities, and delivering the goods directly to firms from farms, cutting out middlemen in the supply chain.

2016-04-29

2017-05-31

The Primary Research Questions and Main Outcomes are:
1) Can the technological solution offered overcome coordination issues among firms to reduce the amount of time they spend travelling to purchase inventories? [travel time measured in surveys]
2) Does the technology lower the cost firms pay for their inventories? [measured by surveys and market price collection]
3) Are lower costs passed onto consumers in terms of lower prices? [measured by surveys of good prices]
4) Do the lower costs and incomplete pass-through or elastic demand result in higher sales and higher profits for firms? [measured by surveys]
5) Do non-participating firms lose sales as a result of their competitors having lower prices or longer opening hours? [measured by surveys]

2500 firms in 60 market blocks will be randomly assigned to treatment (30 blocks) and control (30 blocks). Within each block, vendors will be screened for interest in the new service. The direct effect of treatment will be obtained by comparing interested firms in treated blocks to interested firms in control blocks; the indirect effects by comparing uninterested firms.

Randomization done by computer

market block (consisting of approximately 30-40 firms).

Yes

60 market blocks

2,500 firms

450-600 interested firms in treatment, 450-600 interested firms in control; 300-450 uninterested firms in treatment, 300-450 uninterested firms in control.

Included in grant applications: Since these firms are all operating in the same sector, selling similar products at the same scale, they will be more homogeneous than firms in many standard PSD interventions. This should improve the power of our experiment to detect impacts. Moreover, we believe that unit costs and travel time should be highly correlated over time, and will use multiple weeks of data to further improve power. The baseline data is required to fine-tune these power calculations, as we stand ready to adjust the design as needed if we find that based on the baseline data and initial take-up results that power is lower than anticipated. That said, here are some initial power calculations based on two outcomes that we have used to help guide our preliminary choices on sample size. We assume take-up of the intervention will be 65% among those who express interest–this is conservative, given Agruppa have found approximately 80% take it up in their initial pilots. Our design at present is then random assignment at the market block level of 30 blocks to treatment and 30 to control, with each block containing 20 firms interested in the intervention, for a total of 600 treated and 600 control firms among the interested (we also have uninterested firms in each block). Our starting point assumptions are that i) key outcomes are likely to be highly autocorrelated (firms that have long travel times today will have long travel times in a month due to geography, mode of transport, etc.); ii) key outcomes will have strong intra-cluster correlations in the cross-section within market blocks (e.g. firms that are all within the same block will charge similar prices and have similar travel times to market); and iii) the intra-cluster correlations will be much weaker in terms of changes (firms within blocks experience different shocks and react differently to the intervention). To see this, consider the time in hours per week spent to travel to market. In the cross-section at time t, we model this for firm i in block b as: Outcome 1: Reduction in travel time spent by firms to buy goods from the central market Assumptions: Mean weekly travel time of 15 hours (estimated by Agruppa), standard deviation 5 hours, intra-cluster correlation of 0.6 (since firms within markets will have similar travel times). The intervention aims to reduce hours by an average of 5. The ITT is thus 3.25 hours (5*0.65). Power using just a single round of follow-up data: If randomization was at the individual level, power is 1: sampsi 15 11.75, sd(5) n1(600) n2(600) gives power of 1 and we would need only 38 firms in each group to get 80% power sampsi 15 11.75, sd(5) power(0.8) But with an intra-cluster correlation of 0.6, and 20 firms per market, we need a minimum of 48 blocks (and hence 472 in each treatment group) to achieve 80 percent power. Power using the baseline to improve power: We assume now that using the baseline hours we have an autocorrelation of 0.7 with follow-up hours, so the residual variance becomes sqrt(1-0.72)*5 = 3.57 hours. We assume that this then reduces the intra-cluster correlation in hours to 0.3 (once the blockb component has been removed). Then we have: sampsi 15 11.75, sd(3.75) power(0.8) sampclus, obsclus(20) rho(.3) Gives a minimum of 15 blocks, and 141 treated and 141 control in total needed.

Small trading activities are a prevalent form of self-employment in developing countries, but their integration into
supply value chains is not efficient, especially when it comes
to perishable produce. This study tests a novel approach to
improve their efficiency by reducing the time and cost of
sourcing produce by aggregating purchases through the use
of an app and centralized distribution system. Fruit and
vegetable vendors in Bogotá currently travel most days to
a central market to purchase produce, incurring substantial time and monetary costs. A social enterprise attempted
to shorten the supply chain between farmers and vendors
by aggregating orders from many small stores, sourcing
directly from farmers, and delivering them to the stores.
The introduction of this new service was randomized at the
market block level. Initial interest was high and offering
the service reduced travel time for users by almost two
hours a week, reduced travel costs, and increased work-life
balance for store owners. Firms offered the service saved an
average of 6 to 8 percent on purchase costs, and although
some of this passed through into lower prices for consumers,
there was incomplete pass-through, so that markups rose.
However, stores reduced their sales of products that were
not originally offered by this new service, and their total
sales and profits appear to have fallen in the short run, with
service usage falling over time. The results highlight the
potential for new technologies to solve firm coordination
problems, offer a window into the nature of competition
among small retailers, and point to the challenges in achieving economies of scale when disrupting centralized markets
for multi-product firms.

Iacovone, Leonardo and David McKenzie (2022) "Shortening Supply Chains: Experimental Evidence from Fruit and Vegetable Vendors in Bogota", Economic Development and Cultural Change, 71(2): 111-149

https://doi.org/10.1086/714050