Reducing Corruption in the Use of Development Aid: The Payment by Results Model

Corrupt diversion of development aid in recipient countries affects both the efficacy of the intended development programs and the willingness to supply aid in donor countries. Mismanagement of development funds has spurred debate over the ability of our current aid models to achieve development goals (improved healthcare, poverty alleviation, etc.). Many possible solutions for reducing corruption’s effect on development have been tested over the years with varying degrees of success. Various approaches have been tried, including conditioning aid or loans on “good governance” policy reforms, allocating development aid to local governments or local NGOs rather than national institutions, improving oversight and tracking of aid money, and supplying loans exclusively to countries that already have relatively favorable corruption scores (called performance-based lending). Each of these models has its own limitations: Conditionality is often viewed as an affront to sovereignty and has not been terribly effective. The local approach does not address governance issues, and local actors have not always proved to be less corrupt. Oversight of funds is important but costly and imperfect. Performance-based lending seems to leave behind many poor countries that cannot jump the corruption “hurdle.”

In searching for alternative models for distributing aid in light of the aid-corruption paradox, some donors have turned to yet another approach: payments by results (PbR). PbR has been supported by the Center for Global Development (see here and here) and has gained significant traction in the past two years by bilateral donors, such as the UK and Norway, and multilateral donors, such as the World Bank. The basic premise of PbR is that payment to the recipient depends on achieved results. The donor and recipient first define the desired outcomes (e.g., increased TB vaccinations, construction of an infrastructure project, etc.) and determine the amount that the donor will give once the desired outcome is met. The donor may provide some money up front to implement the program, but the rest of the payment is contingent upon performance: The recipient carries out the project independently, the donor measures the results, and, if the results meet the agreed-upon objective, the donor releases the remaining funds. This approach stands in contrast to the traditional input model, in which a donor gives the recipient money for inputs and provides a detailed action plan along with significant oversight for achieving results. Continue reading

Shedding Sunlight on Procurement

In a previous post, I extolled the virtues of Big Data in the fight against corruption, including in the important realm of government procurement. From the UK to Georgia to the Czech Republic, government procurement agencies have been collaborating with civil society groups to analyze their data, uncovering inefficiencies that range from the mundane to the outright corrupt. Governments are not alone: international development agencies like the World Bank are embarking on similar projects.

But there’s a problem. Big Data needs lots of data to work, entailing a high degree of government transparency and massive disclosures — sometimes called Open Government — that are sometimes at odds with the goals of anticorruption. In the case of government procurement, public data watchers need to know which firms bid for the project, at what price, and who won on what terms before they can play a useful watchdog role. However, as Rick has pointed out on this blog, public disclosure rules in procurement has the perverse effect of enabling private collusion. Cartels of contractors can agree amongst themselves to inflate their prices and select which among them will receive the contract, and are able to enforce their shady agreement because, of course, all offers are public.

Rick’s concerns seem to be directly implicated by the newly-proposed Open Contracting Data Standard, a push to “enhance and promote disclosure and participation in public contracting.” The project essentially asks every procurement agency in the world to upload their contracting documents onto the internet in a standardized manner that would encourage public oversight, including through the use of Big Data tools. So, is the push for open government procurement data doomed to backfire, creating collusion where perhaps it did not even exist before? Fortunately not. The increased risk of collusion is completely outweighed by the potential for the use of Big Data and other civil society monitoring techniques. Continue reading