Publication - Working Paper
Livestock agriculture accounts for twelve percent of GDP in Pakistan, and is a key growth sector for the rural poor, as in much of South Asia. However, the market for livestock faces many of the same imperfections in developing countries as do land or crop markets. We implemented in partnership with the Punjab Livestock and Dairy Department a data-driven, state capacity building technological solution to one key public service adoption problem—failure to use artificial insemination (AI) services.
The market for AI suffers from two informational inefficiencies. First, since insemination can fail even when executed perfectly, livestock owners cannot know whether failure is due to poor effort by technicians or due to unavoidable biological causes. Second, while reporting successful insemination and subsequent dairy productivity is low-cost and provides considerable public benefits, citizens have no private incentives to provide this information. These inefficiencies lead to rates of pregnancy and dairy production that are much lower than expected given the technology being used.
We piloted a technology that activates the cellular network to overcome these two informational inefficiencies. We developed a cellular based information clearinghouse—along the lines of yelp.com. This clearinghouse employs Android smartphones with government AI technicians to collect real-time information on all public AI service provision in the district of Sahiwal, Punjab. This data populates an online dashboard and generates phone calls to farmers to verify service provision and to determine if artificially inseminated animals became pregnant. Data on service provision is reported to the Livestock Department, and data on AI success, on cost per AI provision, and on farmer satisfaction is then aggregated by technician. This data was reported to farmers directly as an RCT.
The Department was previously collecting data haphazardly with long delays between collection and potential action and with no attempts to audit the accuracy of the data. It is our hope that generating this simple but previously non-existent data in real-time, aggregating it, and presenting it to the Livestock Department and to farmers lead to an overall improvement in the public provision of artificial insemination and veterinary services more generally in Sahiwal and ultimately improved animal pregnancy and milk production rates.
With IGC funding, we will collect data to measure AI technician non-compliance through omitting to send in smartphone forms after service provision. If our treatment causes strategic non-compliance by low quality AI technicians, such technicians could appear high quality in the data. This type of behaviour is crucial to understand and to measure properly from a policy perspective and it is interesting from an economic perspective. But to measure it, because of the small likelihood of catching cases in which an AI technician does not send in a form when they should, we had to increase our endline survey size. Specifically, we will be able to administer a ten-minute survey meant to detect non-compliance to a much larger number of farmers (2000) throughout Sahiwal than our current in-person sample of 900, and in addition we will be able to rule out non-detected cases of non-compliance across over half of the villages in Sahiwal (240). In addition, we will be able to survey all government AI technicians in Sahiwal after analysing our non-compliance data in order to ask questions probing non-compliance and attempting to determine the strategic or other reasons for it.