Page 155 - Special Topic Session (STS) - Volume 1

STS422 Damola Owalade
objectives, with financial inclusion being a factor in meeting some of those
objectives.
Measuring FI is an on-going process of experimentation and this paper
aimed to show that there is credence to the advocacy of FI data best practices
in developing a robust data management framework to monitor and evaluate
country level FI policies.
This paper provides the findings from two case studies which involve
experimentation with research design specifically on merging administrative
data with demand side surveys. The methodology in question is not error free
but given the concerted effort by regulators and FSPs in ensuring high quality
data management, there is a possibility that merging administrative and
survey data can become a viable model in explaining financial inclusion and
potentially shedding some light on its effects on economic growth and
livelihoods.

References
1. Insight2Impact facility (2019). Advancing financial inclusion ׀ Executive
summary: Nigeria pilot study. Availabe online at
https://i2ifacility.org/insights/publications/advancing-financial-inclusion-
executive-summary-nigeria-pilot-study?entity=blog
2. Chamboko, R & Makuvaza, L. (2018). A needs-based approach to
financial inclusion measurement in Zimbabwe. Available online from
https://i2ifacility.org/system/documents/files/000/000/066/original/A_ne
eds-
based_approach_to_financial_inclusion_measurement_in_Zimbabwe_i2i_J
une_2018.pdf?1530184081
3. Owolade, D. (2016). Revisiting the building blocks: Getting the basics of
financial inclusion demand side data right. Available online from
http://access.i2ifacility.org/Publications/DQ_Innovation_Focus_Note.pdf
4. Sakshaug, J. W. & Kreuter, F. (2012). Assessing the magnitude of non-
consent biases in linked survey and administrative data. Survey Research
Methods, 6(2), 113-122.

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