Page 149 - Special Topic Session (STS) - Volume 1

STS422 Damola Owalade
deconstructed use cases (having experienced an unexpected shock such as
loss of employment or flooding of a farm) drives the usage of financial
services. Accessing customers’ actual history while administering a survey
offers additional demographic and attitudinal dimensions – allowing for a
more nuanced approach to understanding the usage of financial services be it
credit (in Zimbabwe) or digital payment platforms (in Nigeria).
The next section provides some country context, the research design
including sample design, and data collection methodology.

2. Methodology
As mentioned, this paper will focus on quality assessment of merging
administrative and survey data. The following will be the key quality
considerations.
 Quality assessment of supply side data along the quality dimensions
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of relevance, accessibility, interpretability, coherence, accuracy and
institutional environment
 Non-sampling errors based on the quality outcomes of securing
interviews to merge administrative records with survey data.
Zimbabwe
Contextually, the Reserve Bank of Zimbabwe launched a National Financial
Inclusion Strategy (NFIS) for the period 2016-2020 on 11 March 2016 to
facilitate an inclusive, shared and broad-based economic growth. The NFIS
aims to increase overall level of access to formal financial services from 69%
in 2014 to 90% by 2020 . Credit plays a role in the financial lives of
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Zimbabweans creating opportunities for microfinance banks and microfinance
institutions in supporting individuals and businesses mostly earning a living in
the informal economy.
A description of the Zimbabwean study is as follows:
Research objective: Understanding of the borrowing behaviour,
repayment behaviour and to assess potential financial distress or over-
indebtedness.
Sample (demand side): Randomly selected adults in Harare, Bulawayo,
Manicaland, Mashonaland-West and Midlands (n=700).
Sample (administrative): Customer loan repayment data from a
commercial credit bureau from June 2012 to 2017 (n=750).
Sample (merged demand and credit bureau data): Completed interviews
for customers sampled from the credit bureau (n=307).

2 Data Quality Assessment Tool for Administrative Data, Iwig W, Berning M, Marck P, Prell M,
February 2013 available at https://www.bls.gov/osmr/datatool.pdf
3 https://www.rbz.co.zw/index.php/financial-stability/financial-inclusion/financial-inclusion-
strategy

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