Sahay a formal financial institution by gender and

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Sahay et. al. (2015) examined the linkages of
financial inclusion with economic growth, financial and economic stability, as
well as inequality.  The analysis provided
by Sahay et. al. demonstrated the macroeconomic ramifications of the notion of
financial inclusion and its potential impact. It shed light on the benefits and
trade-offs of financial inclusion in terms of growth, stability (both financial
and macroeconomic), and inequality. They defined financial inclusion as the
access to and use of formal financial services by households and businesses.
The paper drew on several sources of data on financial inclusion. These data
included cross-country surveys for two different years, long-time series across
several countries, and other survey-based data on firms’ access to finance. The
advantage of using a variety of sources was that the analysis can shed light on
many aspects of financial inclusion. The disadvantage was that the datasets are
not strictly comparable and have shortcomings. 


The indicators included the providers’ and the users’
sides. On the providers’ side, the index of FIA introduced in Sahay et. al.
(2015a) covered the number of commercial bank branches and ATMs per one hundred
thousand adults. On the users’ side, a number of indicators were investigated:
share of businesses and investment financed by bank credit, share of the
population with account at a formal financial institution by gender and income
groups, share of firms citing finance as a major obstacle, share of adults
using accounts to receive transfers and wages, share of bank borrowers in the
population and finally, the use of insurance products.


The main challenge in building a relationship between
long-run growth and financial inclusion was the lack of long time series of
financial inclusion (FI) data. For example, the Financial Institution Access
(FIA) index constructed by Sahay and others (2015a) had time series— number of
ATMs, number of bank accounts—from the IMF’s Financial Access Survey (FAS)
starting in 2004 at the earliest. This did not provide robust and usable
results in a standard GMM growth regression with a sample period of 1980–2010
and using five-year averages of all variables to smooth out cyclical
variations. Within this framework, FIA only provided two usable time
observations (averages 2000–04 and 2005–10)7. For this reason, GMM regressions
of this type cannot test for the impact of FIA—or other financial inclusion
indicators, for that matter— as the regressions would not pass the standard
diagnostic tests. This paper used OLS estimation for the growth and inequality

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A cross-country ordinary least squares (OLS)
estimation was run, relating a measure of FI at one point in time (or averaged
over a period) with growth over a period. Ideally, one would have initial FI
related to subsequent growth (as in early King and Levine study) to address
reverse causality:


in which i denotes country and X denotes controls, and
one can also include FIN, a financial depth/development variable: either privy
(private credit-to-GDP), FD (the broad financial development index), or FID
(index of financial institution depth). 


One can only use FAS data at the initial value during
this period. If one uses the more comprehensive Global Findex data, which
measure FI at only two points in time (2011 and 2014), it makes an assumption
that the relative measure of financial inclusion across countries did not
change dramatically over time. In this case, the Global Findex data are
interpreted as a ranking rather than an absolute level.

Categories: Finance


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