The readiness of states, citizens and entrepreneurs to secure the benefits of open data has progressed little over the last year, and the data divide between countries with strong open data initiatives, and those without, has grown.
Effective open data policies require a degree of collaboration between the state, private sector and civil society. A balance is needed between governments with the capacity to create, manage, and publish data, and third parties with the technical skills, freedoms, and resources to use data as a tool for change. Governments that focus solely on increasing the supply of open data — without exploring ways to extend access to data literacy and skills, developing approaches to stimulate innovation, or putting in place foundations for data to be trusted — are likely to miss out on many of the benefits of open data.
The maps below illustrate a number of the key readiness variables in the Barometer. They show the existence and strength of support for open data initiatives, engagement with open data from outside government, legislative frameworks that support open data (e.g., Right to Information and Data Protection laws), and the existence of training and support for data use and innovation. Darker colours indicate a higher score on the 0–10 scale.
In comparing expert assessments of the strength of open data initiatives in countries covered by both the 2013 and 2014 ODB, it is striking that, among the capacity constrained countries, early leadership and progress towards open data has not been sustained. Countries such as Kenya and Ghana have failed thus far to institutionalise their open data initiatives, with progress stalling or moving backwards when key leaders or instigators move on. There is growing recognition of the need for open data to rest upon reforms to the wider data infrastructures of the state, as well as upon strong legal foundations. Writing about the Kenya Open Data Initiative that he instigated, Dr Bitange Ndemo, former Permanent Secretary to Kenya’s Ministry of Information and Communications, argued that to revive the initiative, Kenya must “digitise all of our registries and enact two critical bills that are in Parliament, the Freedom of Information (FOI) and the Data Protection Bills.”1
|High capacity||Emerging and advancing||One sided initiative||Capacity constrained|
The cluster of high-capacity countries has seen continued support for data innovation; funding programmes, challenge funds, roundtables, and innovation incubators have become part of business-as-usual for government, creating spaces for collaboration around datasets and stimulating data re-use. However, among countries with emerging and advancing open data practices, support for innovation with data remains ad hoc. In a number of countries where we found evidence of hackathons or other events to stimulate data use in 2013, our researchers could not locate follow-up activities in 2014.
As evidence from the iHub evaluation of the Code for Kenya initiative suggests2, open data hackathons or incubators do not automatically result in scalable products or services, but they can provide a space for re-imagining how government services could be delivered. Governments need capacity to absorb the innovative ideas that are prototyped with open data, and to create an enabling environment where social and economic innovations can scale.
In the countries of the one-sided initiative cluster, limited political freedoms and the low capacity of civil society are joined with low publication rates of the datasets relevant to transparency and accountability, leaving very limited space for the transformative potential of open data. Countries here may have a form of an open data initiative, with portals and some datasets, but little of the functionality of open data as a tool to unlock innovation and create space for civic dialogue.
Many of the day-to-day decisions and actions that could enhance citizens quality of life take place at the local level. In our expert survey was ask about the existence of sub-national open data initiatives. As the map above shows, local initiatives are much more evident in Europe, North America and Australia than elsewhere in the world.
A linear regression analysis of expert survey readiness variables against the social and political impact sub-components of the Open Data Barometer indicates that the existence of city-level initiatives is significantly correlated with perceptions of impact3. This highlights an important area for future research and action, identifying the extent to which government can and should create enabling environments for open data activities at the sub-national level. In the UK, for example, the local open data incentive scheme provides cash payments to local authorities for publishing key datasets, including planning applications, premises licences, and details of public toilets.
The table below presents the three readiness sub-components of the Open Data Barometer. The table can be grouped by region or income level.
There is a strong correlation (0.75) between GDP per capita and overall readiness as ranked by the Open Data Barometer. The correlation is strongest in terms of entrepreneurial readiness and weakest for citizen/civil society readiness.
Drawing on data from the 2013 Barometer, Meng has suggested that “political capital”, as distinct from associational social capital also plays an important role in the readiness of countries to gain social impacts from open data. Political capital is defined as “attitudes supportive of democratic norms and behaviour that engage citizens with the state and each other in channelled ways, conveying interests, preferences, and demands to the regime”4. The first two editions of the Open Data Barometer do not provide a measure of political capital, but this may be an important dimension to consider in future work, and in assessing the potential to secure social change through open data initiatives. Similarly, the open data literature frequently points to the importance of intermediaries in translating data availability into social change activity. Whilst the existence of civil society engaging with open data, and the presence of technical capacity in firms within a country, can act as proxies for the likelihood of intermediaries emerging, further work is needed to track and understand the different kinds of intermediaries and the roles they play in readiness to secure different impacts from open data.
Ndemo, B. (2014). Open contracting format can clean up government procurement. Daily Nation 24th November 2014. http://www.nation.co.ke/oped/blogs/dot9/ndemo/-/2274486/2532264/-/1wpu9kz/-/ ↩
Mutuku, Leonida, and Christine Mahihu (2014) Understanding the Impacts of Kenya Open Data Applications and Services. iHub Research. http://opendataresearch.org/sites/default/files/publications/ODDC%20Report%20iHub.pdf ↩
n = 86. Based on “fit<-lm(Impact_Political ~ WB.NetUsers + FH + WEF.GCI.9.02 + WEF.GITR.8.01 + ODB.2013.C.INIT + ODB.2013.C.CITY + ODB.2013.C.RTI + ODB.2013.C.CSOC + ODB.2013.C.SUPIN + ODB.2013.C.DPL + ODB.2013.C.TRAIN,data=scaled_scores)” which indicates a loading of 0.331785 on ODB.2013.C.CITY at a significance level of 0.01, and and “fit<-lm(Impact_Social ~ FH + WEF.GCI.9.02 + WEF.GITR.8.01 + ODB.2013.C.INIT + ODB.2013.C.CITY + ODB.2013.C.RTI + ODB.2013.C.CSOC + ODB.2013.C.SUPIN + ODB.2013.C.DPL + ODB.2013.C.TRAIN,data=scaled_scores)” which indicates a loading of 0.29795 on ODB.2013.C.CITY at a significance level of 0.01, and a loading of 0.46919 on ODB.2013.C.SUPIN with a significance level of 0.001. See the methods section for further details. (Note - the variable name indicates that a question is drawn from the 2013 study, although the data comes from 2014.) ↩
Meng, A. (2014). Investigating the Roots of Open Data’s Social Impact. Journal of eDemocracy and Open Government, 6(1), 1–13. http://www.jedem.org/article/view/288 ↩