Difference between revisions of "Administrative and Monitoring Data"
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While impact evaluations most commonly rely on
While impact evaluations most commonly rely on data, secondary datacan for impact evaluation designand analysis. the monitoring data.
== Read First ==
== Read First ==
*Administrative data is any data collected by national
Administrative datais any data collected by nationallocal governmentsministries agencies outside the context of an impact evaluation.
* data in .
== Administrative Data ==
== Administrative Data ==
Revision as of 19:23, 3 March 2021
While impact evaluations most commonly rely on primary data, secondary data can often provide important context for impact evaluation design and data analysis. In some cases, for example administrative data from a program conducted in a district, secondary data is the only source which covers the relevant population for an impact evaluation. Similary, in some cases, monitoring data can help assess who received the treatment, and if this was as per the initial impact evaluation design.
- Impact Evaluations rely on many different [[Secondary Data Sources|sources of secondary data] - administrative, geospatial, sensors, telecom, and crowd-sourcing.
- An important step in designing an impact evaluation is to evaluate which of the available data sources are best suited in a particular context.
- Administrative data is any data collected by national/local governments, ministries or agencies that are outside the context of an impact evaluation.
- Monitoring data is data that is collected to track the implementation of treatment in a given impact evaluation.
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.
Administrative data offers advantages in quality, cost, and time. It is often considered more accurate than self-reported survey data; consider, for example, that a firm is more likely to accurately report its turnover rate to Financial Administrations than to a research team conducting a firm survey. Furthermore, notwithstanding potential access costs, administrative data doesn't pose additional costs as it is collected independent of the impact evaluation. Finally, administrative data can avail information frequently because it is often collected on a regular basis. This makes administrative data especially advantageous and attractive for research teams retrospectively evaluating interventions for which data collection did not occur.
Nonetheless, administrative data doesn't come without a few potential challenges: access, merging, and quality. Accessing administrative data requires strong relationships with national and/or local authorities. In some cases, authorities may not be inclined to share the information. Once accessed, consolidating administrative data with other data often entails merging different databases together: this can be an extensive task when no common unique identifiers exist across the databases. Finally, while in some cases administrative data can provide high accuracy, in others, it may be badly reported, not exhaustive, or not at all existent. Not all governments have the same capacity to collect this information.
Monitoring data is collected to understand the implementation of the assigned treatment in the field. Typically, survey round data helps us understand changes in the outcome variables throughout the duration of the project, and monitoring data helps us understand how these changes are related to the intervention of our treatment. For example, monitoring data could be data on who actually received the treatment and if the treatment was implemented according to the research design. Our analysis might be invalid if we do not have this information and base our analysis only on what was meant by the research team to happen. Monitor data helps us understand what is usually referred to as internal validity.
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