Status: Available Now

conTEXT, Laterite's new SMS-based survey platform in Rwanda, combines fast and affordable data collection with expert support on survey sampling, design and analysis. It is the first commercially available SMS survey tool in Rwanda, connected to all 3 MNOs using a short code, with reverse billing & airtime incentives enabled. Clients also have the opportunity to view the data coming in live through an online client portal. By reaching out to many more people, much more frequently and at an affordable cost, this tool will enable your organization to better understand the context it operates in. conTEXT can be used to collect data very quickly, frequently & affordably from project beneficiaries and support staff, country wide. SMS-based surveys enable the collection of a different type of data – small bits, very frequently. It is not a substitute for face-to-face data collection, but a very useful complement.
 

Status: Design Stage

The Lab is currently working on developing a representative household panel of Rwanda. Our target is to have at least 10,000 households registered on this panel by the time we launch it. In parallel to this, Laterite is developing the capacity to conduct rapid SMS surveys for clients, drawing from households in the panel.

This will enable organizations (both public and private) to conduct faster and cheaper surveys, thereby opening-up new possibilities for social, political and market research in Rwanda. Our hope is that this tool will enable decision-makers to make evidence-based almost on the same day that a research question is asked. It will also enable companies and development organization to easily and regularly monitor their KPIs.

Status: Design Stage

The education sector can be conceptualized as a complex system consisting of stocks and flows. Children that enter the education system in the first grade of primary school, enter a long term process that might combine events such as repetition, dropout, temporary periods out-of-school, re-entry and promotion. What happens at the beginning of a child's education has dynamic effects many years later. As such, the current structure of the education system (in terms of its age-grade composition) contains latent information on the future structure of the education sector. Using markov-chains we are trying to model Rwanda's education sector and predict the future age-grade composition of classrooms under various scenarios. The objective of this project is to provide the Ministry of Education with informed medium-term predictions on the number of students that will be enrolled in each grade (under various scenario) to guide policy decisions on resource allocation and resource requirements in terms of teachers and classrooms. 

Status: Design Stage

Both the government and the private sector in Rwanda collect large amounts of data on tax-payers, consumers, financial transactions, social security, insurance claims, etc. These rich datasets are however under-utilized. Laterite is looking to work with government agencies and the private sector to apply data analytics and predictive analytics techniques  to existing datasets, to help organizations find better responses to some of their most pressing problems. 

This lab project will involve reaching out to clients to explore what can be achieved by: (i) better linking and utilizing existing datasets; (ii) improving corporate reporting;(iii) forecasting; and (iv) generating insights about potential policy and business questions. The potential gains are large and span over many domains: health, tax monitoring, economic  & monetary forecasting, energy & water provision, public transports, agriculture, mobile telephony, Internet, banking and insurance.
 

Status: Design Stage

Laterite is exploring the use of innovative metrics to transform the way certain development indicators are measured, including for example household wealth, the nutritional-mix of households or child labor. The objective is to use complexity methods, that take into account the structure of the data, to create more powerful, relevant and context-specific metrics. We aim to prove that the indicators we develop provide better predictions than standard approaches, including for example principle component analysis. Our hope is that these metrics will provide policy makers with better and more granular information when making decisions about development issues.