Difference between revisions of "Administrative and Monitoring Data"

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While impact evaluations most commonly rely on survey generated data, secondary data collected by governments and/or program teams can prove useful for both impact evaluation design and analysis. This page outlines the advantages and challenges associated with acquiring and using administrative and monitoring data.
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While impact evaluations most commonly rely on [[Primary Data Collection|primary data]], [[Secondary Data Sources|secondary data]] can often provide important context for [[Randomized_Evaluations:_Principles_of_Study_Design|impact evaluation design]] and [[Data Analysis|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 [[Randomized_Evaluations:_Principles_of_Study_Design#Step_2:_Randomization|treatment]], and if this was as per the initial '''impact evaluation design'''.
 
 
 
== Read First ==
 
== Read First ==
*Administrative data is any data collected by national or local governments (i.e. ministries, agencies) outside of the context of an impact evaluation.  
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* Impact Evaluations rely on many different [[Secondary Data Sources|sources of secondary data] - '''administrative''', [[Geo_Spatial_Data|geospatial]], [[Remote Sensing|sensors]], [[Telecom Data|telecom]], and [[Crowd-sourced Data|crowd-sourcing]].
*Administrative data offers advantages in quality, cost and time, but comes with challenges in access, merging, and quality.  
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* An important step in designing an impact evaluation is to evaluate which of the available data sources are best suited in a particular context.
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* '''Administrative data''' is any data collected by national/local governments, ministries or agencies that are outside the context of an impact evaluation.  
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* '''Monitoring data''' is data that is collected to track the implementation of [[Randomized_Evaluations:_Principles_of_Study_Design#Step_2:_Randomization|treatment]] in a given impact evaluation.  
  
 
== 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.

Read First

  • 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

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.

Advantages

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.

Challenges

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

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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This article is part of the topic Secondary Data Sources

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