Administrative Data

Administrative data is any data collected by national or local governments (i.e. ministries, agencies etc.) outside of the context of an impact evaluation. Examples include national census data, tax data, and school enrollment data. Administrative data is generally not initially collected for research purposes but rather to document or track policy beneficiaries, firm owners and the general population. Researchers should aim not to use administrative data in place of survey data but rather in addition to it.

Overview

Generally, administrative data is collected to document or track beneficiaries of a government policy and the general population, and not for research purposes. Research teams should aim to use administrative data in addition to other sources of data - survey data, geospatial data, remote sensing data, telecom data, and crowd-sourced data. This allows research teams to create sector-specific and country-specific outputs (such as datasets, maps, and figures) that are relevant to a particular policy context.

Case Study

In this section, we look at an example of a project by DIME in Kenya where the research team digitized administrative data to fill in the gaps available data on road safety in Kenya.

In this impact evaluation, the research team obtained administrative data through a data sharing agreement with the National Police Service (NPS) in Kenya, and manually digitized a total of 12,546 crash records for the city of Nairobi over a nine-year period. This data allowed the team to identify the major crash hot spots, that is, regions with the highest number of road crashes. The research team combined this data with crowdsourced data to supplement these records. Further, the research team also accessed private sector data on speed road events, weather conditions, and land use by utilizing the the World Bank Development Data Partnership (DDP) initiative. The administrative data was then combined with primary data collected from 200 hot spots, which allowed the research team to generate more than 100 new variables that determine high-risk locations.

Therefore, in this case study, combining multiple datasets allowed the research team to break down a big problem into a more manageable research question. For instance, it is now clear that just 200 of the 1,400 crash sites across the city are responsible for over half of road traffic deaths. This in turn means that the government should target 150 kilometers of the total 6,200-kilometer road network for road-safety interventions.

Advantages

Using administrative data has various advantages for research teams. Some of these are as follows:

• Quality: It is often more accurate, and therefore of better quality than self-reported survey data. For example, a firm is more likely to accurately report profits to their country's official financial auditors than to a research team.
• Cost: It is often less expensive to collect or acquire, since it does not involve the various steps involved in conducting field surveys. Note that there might still be some costs involved in obtaining access to the data through a data licensing agreement (DLA).
• Time: Using administrative data also saves time since this data has already been collected for a purpose outside of the context of an impact evaluation. For example, in the Kenya case study in the previous section, road crash data from Kenya's National Police Service (NPS) had already been collected over a nine year period. In this case, the research team only had to wait until the DLA was carried out, which was much less than the time it would have taken to conduct a field survey from scratch.
• Frequency: It is also collected on a regular basis. This allows research teams to evaluate past interventions even if no primary data was collected.
• Policy impact: Most importantly, as the Kenya case study showed, administrative data can hugely improve the ability of research teams to improve the efficiency of interventions by making them more targeted.

Challenges

However, it is important to note that administrative data also has its list of challenges. Some of these include:

• Access: Accessing administrative data requires strong relationships with national and/or local authorities. In some cases, authorities may not agree to share the information.
• Merging: After obtaining access, the research team must combine the administrative data with data from other sources. This often involves merging different datasets together, which can be tricky if there are no common unique IDs.

• Quality: Finally, research teams should keep in mind that in some cases, administrative data may be badly reported, incomplete, or not available at all. This is because not all governments have the same capacity to accurately collect this information on a regular basis.

Additional Resources

• Arianna Legovini and Maria Ruth Jones (World Bank), Administrative Data in Research at the World Bank: The Case of Development Impact Evaluation (DIME)
• J-PAL, Handbook on Using Administrative Data for Research and Evidence-based Policy