In Kenya, Open Contracting Improves Efficiency & Curbs Corruption
On 31st August 2020, President Uhuru Kenyatta directed the Ministry of Health to come up with a transparent, open method and mechanism through which all tenders and procurement done by Kenya Medical Supplies Agency (KEMSA) will be available online. The directive follows allegations of corruption in the procurement of COVID-19 emergency supplies.
With citizens’ lives on the line and government spending at record highs, ensuring accountability to citizens is imperative to maintaining trust and effectively managing KEMSA’s procurement in response to COVID-19. Open procurement data can help in improving the efficiency of emergency procurement and support civil society groups to detect corruption and monitor the effectiveness of service delivery.
“This level of transparency and through the use of technology will go a very long way in ensuring that we have the confidence of our people that those placed in institutions are able to manage the resources of the Kenyan taxpayer plus our development partners in an open and transparent manner” – President Uhuru Kenyatta
Much can be learned from Makueni County in Kenya, a county that publishes and uses open, accessible, and timely information on government contracting to engage citizens and businesses. The Makueni Open Contracting Portal is an interactive site built by Development Gateway (DG) that provides detailed information on each step of the tender, award, and contract implementation process at the county level. These steps are now recorded within the interactive Makueni Open Contracting Portal – making information available for citizens at each step of the process. The county plans to go a step further to publish all implementation data such as community monitoring reports, also known as PMC reports and supplier payment vouchers.
The goal of the portal is to improve the efficiency of public procurement management and support the delivery of higher-quality goods, works, and services for residents of Makueni County through enhanced citizen feedback.
What We Learned from Makueni County
Lesson 1: Public Data Improves Efficiency
The primary role of the Ministry of Health and KEMSA in Kenya during an emergency situation is to provide citizens timely, affordable, and efficient supplies and services. Digitizing and publishing procurement data will provide the Ministry insights on whether funding and services are reaching intended beneficiaries.
Publishing procurement data will also encourage better monitoring from relevant state and non-state actors. The Ministry of Health and KEMSA will have the opportunity to aggregate non-state actors’ feedback and state actor insights. This feedback will enable them to make data-driven decisions that will improve service delivery to citizens, promote efficient allocation of resources and ultimately saving costs.
DG has developed interactive M&E dashboards to support analysis currently used by Makueni County. The series of charts and visualizations provide helpful data insights – such as top suppliers that received contracts and the percentage of awards that go toward the Access to Government Procurement Opportunities (AGPO), which requires tenders to be awarded to women, youth, and people with disabilities.
Since the start of the use of the Makueni open contracting portal in 2019, improved resource utilization and efficiency in procurement has been identified by the County leadership. Governor Kivutha Kibwana cited that the County has saved Kes. 30,000,000 from the Roads department as a result of using the portal.
Lesson 2: Building Trust is Essential to Combating Corruption
The complexity of emergency responses such as COVID-19 requires cooperation between the private sector, national, and county government to ensure timely delivery of supplies. KEMSA publishing data will promote feedback and engagement of business and citizens further building trust and collaboration. Publishing procurement data also equips civil society and citizens with the information needed to help combat corruption. For example, reporting counterfeits, frauds, and scams – which has been a key corruption issue identified globally in COVID-19 response procurement, particularly PPEs.
DG has implemented its corruption risk dashboard in Makueni, which uses high powered analytics and global research to identify risk profiles for potential corruption in procurement. KEMSA can adopt the corruption risk dashboard as a red-flagging tool to assist in identifying procurement activities that merit in-depth auditing of corruption risk – including fraud, collusion, and process rigging – over time. These analytics will allow the organization to address cases of corruption before taxpayer money is lost.
Lastly, publishing Beneficial ownership data can enable governments to quickly perform minimal standards of due diligence on companies they are buying goods and services from. As well as reducing the immediate risk of corruption, beneficial ownership data provides a valuable trail for future audit.
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Farmer-Centric Data Governance Models: Protecting Farmers and Food Systems Today and Tomorrow
As farmers become more reliant on AgTech, they may find that the AgTech providers controlling these technologies (i.e., companies, nonprofits, and governments) are more integrated than ever before, resulting in a few organizations having unprecedented access to and control of farmers’ data. This dynamic results in positive and negative outcomes for farmers. Therefore, farmers face the paradox of using AgTech and adding value to their work, communities, and food systems while giving large amounts of data to AgTech companies that have, at best, limited plans for protecting farmers’ data. In this blog, we identified recommendations and next steps for AgTech providers on how to ensure that their technology benefits smallholder farmers.
Smallholder farmers are increasingly using digital technologies (i.e., AgTech) to increase their profits and production, improve processes, and reduce risks. In doing so, many farmers have significantly increased the value of their work.
After all, AgTech platforms offer many positive features including: openness, interoperability, network effects, control over market entry and participation, and the ability to reshape economic relationships and rationalities.
However, as farmers become more reliant on AgTech, they may find that the AgTech providers controlling these technologies (i.e., companies, nonprofits, and governments) are more integrated than ever before, resulting in a few organizations having unprecedented access to and control of farmers’ data. This dynamic results in positive and negative outcomes for farmers. For example, some AgTech companies document farmers’ data on agricultural planning software and sell it to fertilizer companies—with or without the farmers’ knowledge or consent. The fertilizer companies can then use the data to price gouge.
Therefore, farmers face the paradox of using AgTech and adding value to their work, communities, and food systems while giving large amounts of data to AgTech companies that have, at best, limited plans for protecting farmers’ data.
AgTech providers must address this unbalanced power dynamic by meaningfully involving farmers in the processes of ownership and governance of their data (i.e., the creation and participation of data governance approaches). AgTech providers must prioritize the implementation of fair and equitable data governance models that emphasize farmer participation while guarding farmers against potential disadvantages and exploitation.
To explore potential data governance solutions, I led the Farmer-Centric Data Governance study, which included an extensive literature review and 45 semi-structured interviews with stakeholders and practitioners. The study was co-funded by USAID and Bill & Melinda Gates Foundation, with support from DAI, and in collaboration with Athena Infonomics.
This research informed the conception of user-centric approaches to data governance that place farmers and their communities at the center of data gathering initiatives and aim to reduce the negative effects of centralized power. These data governance models are discussed in our report, Farmer-Centric Data Governance: Towards A New Paradigm.
The data governance models we explored were: data collaboratives, data commons, data cooperatives, data fiduciary models and marketplaces, Indigenous data sovereignty, and data trusts. These user-centric models can strengthen the power of farmers as a cohesive group, transition ownership and control over data to individuals and collectives, and build safeguards against privacy invasion, data misuse, opacity, and other harms. These models have the potential to bring about greater societal and economic equity and contribute towards increased confidence by stakeholders in the use of data overall.
Deep Dive on a Two Data Governance Models
Data Collaborative Model: In 2019, Fairfood and Verstegen Spices & Sauces collaborated to improve the nutmeg spice supply chain in North Sulawesi, Indonesia. The initiative took an inclusive, farmer-centric data collaborative approach through digitization and data governance, with the aim to improve the position and livelihoods of smallholders and their communities. The fiduciary data stewardship role played by Fairfood has resulted in increased trust and confidence in data sharing; better communication of data claims and benefits to stakeholders; improved consent and understanding of data ownership; and stronger negotiating position for farmers and ensuring that farmers’ voices are heard. This case study provides insights into developing transparent value chains; incentives for data sharing; a first step to human, digital, and financial inclusion; data ownership; and data monetization.
Data Cooperative Model: Two initiatives, Abalobi and PescaData, support sustainable fishing communities by co-creating a digital ecosystem for smallholder fishers and key stakeholders and achieving marine conservation outcomes in South Africa, Mexico. The unique, innovative approach of a data and platform cooperative for fisheries aims to collect data, establish a knowledge base, digitally support fishing communities, create market opportunities, and better inform public decision-making. The tools are built through participatory, iterative design to create a usable solution and empower fishing communities. This case study provides insights into data stewardship, participation and collaboration, knowledge sharing, and platform and data cooperatives.
Recommendations
From this project overall, we identified recommendations and next steps for AgTech providers on how to ensure that their technology benefits smallholder farmers. Our recommendations are:
- Farmer-centric models should be integrated into digital agriculture programs given their immense potential to shift the current paradigms of information imbalances to benefit farmers, communities, and societies. Farmer-centric models can empower farmers to gain more control and ownership over their data, create individual or collective agency, obtain negotiation power, and protect against data misuse.
- Trust needs to be fostered throughout the data lifecycle. Efforts that integrate data analysis and data collection tools must clearly explain what activities are being undertaken, their benefits, privacy measures, the process for asking questions, and how concerns are addressed and resolved. Data insights must be communicated in a manner that is accessible to farmers.
- Farmer-centric, participatory data governance pursues more consistent, high-quality data sharing, interoperability, and defragmentation. Its impact is dependent on design, deployment, and implementation done collaboratively and built on a foundation of trust.
- Meaningful participation must strongly tie farmers to data governance. Participation in designing farmer-centric models will help ensure their relevance and fit to the particular context.
- The vital role of data stewards as trusted intermediaries within programs requires better understanding between farmers, data collectors, and data subjects.
- The local context, culture, and practices should determine which governance model is used. Communities often know best how to organize and govern; learning and implementation should follow systematic observation of communities, with different vantage points.
- Farmer-centric models should not be seen as a panacea “one-size-fits-all” solution. The practical implementation of data governance or technology is a lot more ambiguous and variable than theory often suggests. A meaningful farmer-centric data ecosystem should be built on a range of approaches.
- More research is needed to identify training and capacity-building needs and sustainability of user-centric data governance initiatives. There is a need to develop data governance skills training aimed at practitioners and farmers to assess, implement, refine, and continue to share best practices of data governance initiatives.
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