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About GGLI

The Global Goals for Local Impact programme was established on the premise that the Sustainable Development Goals are best achieved at hyperlocal villages, upwards - as opposed to how a lot of development is done - through national programmes that often do not reach the public.

The foundation of this project is using data to stimulate active citizenship and responsive government - essentially that citizens provide input into government activities and that government develops mechanisms for listening to citizen needs. We did some work in 2015 to try and see what citizens care about enough to act and be engaged with government. One of the biggest observations that we made was that they cared practically about local issues than they did about regional or national issues. They did not feel that they had the capacity to do anything about regional or national issues.

The Pilot

The location in Kenya is the smallest geographic administrative unit, which normally consists of a couple of villages. The administrator in charge of the location is the chief.

Several locations make up a division (manned by a Assistant County Commissioner, formerly a Division Officer) and several divisions make up a sub-county, which is led administratively by the Deputy County Commissioner. The Deputy County Commissioner reports to the County Commissioner, who reports to the Regional County Commissioner, who in turn reports to the President and the Cabinet Secretary for the Interior.

How we did it

We conducted the pilot for this project in Lanet-Umoja location together with Chief Francis Kariuki (famously known as the tweeting chief). Through this pilot, we established a simple process: we foster the buy-in of the administrators and the community then we sensitize the community on the SDGs, the value of data in their lives and how to collect data. We then work with the community to collect data from every household in the location (12,500 households in Lanet’s case).

We then analyse the data and then share it back with the community for them to identify what their priorities and issues are. Based on their discussions and their reviews of the data, they identify who should be tasked to tackle which issues - some are tackled by the community themselves, others require that they advocate for them with government and yet others require that they reach out to other stakeholders as CSOs, for assistance.

In a nutshell, the model which had the following steps, has produced learnings and thoughts on how communities make decisions and share responsibilities.

  1. Buy In - The process is to identify the common and priority issues that the community is facing and can be addressed using data
  2. Data Training - Once the issue is identified, an understanding what data is and what it can do is required for the community to have. Data collection methods are also made known to the community.
  3. Data Collection - The data collection exercise is done by the community. This gives the community an opportunity to learn first hand what it entails and also helps them prepare for similar exercises that might be required in the future. It also builds sustainability by them taking ownership of the process.
  4. Data Analysis - After the data is collected the community delve to interpreting the data and make decisions on what is priority and needs to be planned for. They also know what issues can be done within the community, what issues need to be forwarded to the local government and what can be dealt with other stakeholders.
  5. Data Fair - The release of the data and the analysis is relevant to initiate conversations within the community bringing out priorities and mapping out solutions.
  6. Advocacy - Some issues will require the intervention of the government and they will need to be presented in an official manner. A memorandum as a tool can be used by the community to advocate to the government.

We since scaled up the project to 6 other locations and this platform is useful for showing the lessons we have learnt..

What Worked

We are excited that the communities we worked with achieved numerous outcomes both “soft” and “hard”. These include (In no particular order):

  1. We feel that we have developed a replicable model that can be taken up by other organisations and governments to strengthen citizen engagement, to activate citizen action and to drive responsive government. This platform is designed to showcase the process and timeline of that model.
  2. The people of Lanet Umoja were able to showcase how they could use data to engage with government particularly as they successfully advocated with the county government to establish a medical facility in the location. They also went ahead to engage a non-profit organisation called Start with One, who provided a water filter for every household in the community.
  3. We have seen many examples of Active Citizenship in the community. For example, in Wanyororo, we saw community members work together to clean up a school and build programmes to strengthen unity between them. In Lanet, we saw the community establish a gender committee that constituted 70% women and 30% men (with a good distribution according to age).
  4. We have seen the local administrative officers use the data to better advocate with their seniors for more incisive programmes around the people living with disabilities, the elderly and youth.
  5. We have been able to work with communities to use the household data to dig deeper to gather more niche data - for example, we worked with Lanet Umoja and Civicus to gather anonymised data on SDG number 5 from women groups. We also are working with the farmers of Kirima and Bahati locations to collect data that digs deeper into the farming community’s needs and find solutions that can be implemented to improve their agribusiness.
  6. We have seen greater cooperation between citizens and government officials as trust is built and relationships are strengthened.

What Did Not Work

We have had a number of challenges in the course of the implementation of this programme.

  1. Cohesion:
    In two of the locations, Mereroni and Githioro, there was found to be a lack of quorum of the community leaders when the time of data collection came. Upon investigating further, we found that the community leaders did not have a good rapport with their respective chiefs and that hampered the “good faith” endeavour that would need to be there. Many said that they would only work with him if they were to be paid. Because of this, we found that the chiefs did not have the kind of mobilising power that was evident in other administrators in other locations.
  2. Money and Politics:
    In at least two locations, the work was interfered with because of the political campaigns that were gaining momentum. At the beginning of the community meetings, in which the trainings were happening, a few minutes were given to the prospective candidates for local representative (member of the County Assembly) and the Member of Parliament, for them to make remarks relating to the campaigns. The politicians would then make promises to the people that they would give them “something small” as they did the onerous work of collecting data. As a result, they started to compete with each other as to who would promise the best gifts for the data collection exercise.
    The end result is that the data collection exercise was marred with hastily and improperly filled survey forms. As people turned the forms in, we found various problems that ranged from whole sections not being filled, crucial information being improperly filled and many instances where there was duplicate information submitted for one household (to increase amount of money paid).
    We also saw in Lanet Umoja that the culture of Money for Data has taken root as we saw other organisations go there to collect data and pay up to $3 per survey filled. One of our partners also did so with respect to poverty data and the data that they recieved was wholly unreliable, with all members of the community saying that they earn under Kshs. 5000 ($50) - even where on the ground you can tell this is not true. The motivation for lying is that the community saw these organisations as “Mr. Moneybags” and they want to hedge to get all the benefits they can.
  3. Timing is everything:
    We learnt that during the implementation of the activities, we must continually pay attention to the activities that the community considers important. We also learnt that it is important to have shorter bursts of activity with some breaks to ensure that the community retains focus. In this learning, we understand now that it is better for the purposes to break the activities down to small milestones over small periods of time. One thing that we had not anticipated is the level of citizen activity at the elections. What we found in these past elections, is that the communities in Nakuru fell on one side of the political divide and they felt their candidate was threatened. They therefore mobilised heavily even before the elections to shore up support for their presidential candidate. This moved their collective mind away from development priorities to politics. In some of the farming communities we found that we lost them for a while as they focused and dealt with the army worm scourge that threatened to decimate their maize crop.
  4. Too much data collection:
    In Lanet, we found that conducting the data collection 18 months from the previous data collection was not fruitful because of the fatigue that citizens had. After they had the first round of data collection with us, the community then worked with a couple of other organisations to collect niche data from the households and they were paid. When we went back to collect data from there there was an expectation of pay and the community did not feel adequately motivated.

Lessons Learnt