Page 148 - Special Topic Session (STS) - Volume 1

STS422 Damola Owalade
population data with GPS coordinates. Uptake data can be measured when
there is a unique identifier for customers using financial services (from
regulated entities that report to a regulator like a central bank) which makes
it possible to aggregate the number of unique customers registered with
banks. Uptake can also be assessed through demand side surveys that are
nationally representative where respondents are asked whether they are
registered with formal financial institutions.
Moreover, usage data is usually based on the level of activity or recency of
using of a financial product within a set period – usually – a time period of 3
months. This can either be determined through a demand side survey or
administrative data. Using administrative data to quantify usage data should
be more accurate since actual usage can be observed as opposed to a demand
side survey where data is susceptible to non-sample errors such as recall error
by the survey respondent.
Finally, quality of usage can be assessed using administrative data on
complaints and customer feedback. It can also be assessed using a demand
side survey module with questions designed to understand the customer value
proposition of using financial services. This paper will be focusing on the usage
measurement of financial inclusion highlighting data quality considerations of
linking demand side and administrative data to understand financial inclusion
specifically on credit and borrowing behaviour and the use of digital financial
services in Zimbabwe and Nigeria.
Administrative data takes the form of government or financial service
provider (FSP) records that one can link with survey data to form an integrated
dataset. Examples of administrative data include payment transaction history,
tax, health or employment records. This data is often more reliable as it is
regularly collected and can reduce survey interview burden on the respondent
because they do not have to report sensitive data (e.g. income) or remember
historical health or employment events. (Sakshaug & Kreuter, 2012).
The linking of administrative data with demand side data is not a common
practice in financial inclusion research due to lack of feasibility in developing
countries when considering lack of data sharing culture, rigid implementation
(or lack) of customer data privacy laws, and the unreliability in compiling
information on unique customers. Therefore, this paper will be focusing on the
usage measurement of financial inclusion highlighting data quality
considerations of linking demand side and administrative data to understand
financial inclusion specifically on credit and borrowing behaviour and the use
of digital financial services in Zimbabwe and Nigeria respectively. These
projects in Zimbabwe and Nigeria were implemented as part of the
insight2impact’s mandate in contributing to the discourse of financial
inclusion measurement. The i2i financial inclusion measurement conceptual
framework posits that financial needs (e.g. resilience needs) through

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