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Based on a
sample of 128 countries over 1980–2013, this paper’s analysis showed that
financial development boosts growth, but the impacts weaken at higher levels of
financial development, and eventually become negative. Empirical analysis demonstrated
that there was a significant, bell-shaped, relationship between financial
development and growth. The estimation approach addressed the endogeneity problem
and controls for crisis episodes as well as other standard growth determinants,
such as initial income per capita, education, trade openness, foreign direct
investment flows, inflation, and government consumption. This relationship was
in line with recent findings in the literature (Arcand, Berkes, and Panizza
2012). 

 

Not much is
known about the macroeconomic implications of financial inclusion, with a few
recent exceptions. Sahay and others (2015a), demonstrated that household’s
access to finance has a strong positive link with growth. The same paper
further displays that the relationship between depth and growth is bell-shaped
(i.e. the law of diminishing returns), suggesting that the returns to growth
falls with higher depth beyond a certain point. However, financial institution
access (FIA), an index of the density of ATMs and bank branches that narrowly
defines inclusion, had a monotonic relationship with growth. Dabla-Norris and
others (2015) used a general equilibrium model to demonstrate how lowering
monitoring costs, relaxing collateral requirements and thereby increasing
firms’ access to credit would increase growth. Buera, Kaboski, and Shin (2012)
via an entrepreneurship model found that microfinance has positive influence on
consumption and output.HO1 

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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 absence of long enough time
series of financial inclusion (FI) data. For instance, the index of Financial
Institution Access (FIA) assembled by Sahay and others (2015a) had time series
– number of ATMs and bank accounts – from the IMF’s Financial Access Survey
(FAS) starting in 2004 at the earliest. Since the sample period was between
1980 and 2010, which was combined with a five-year average for all variables
(used in order to smooth out cyclical variations) did unfortunately not provide
robust and usable results in a standard GMM growth regression. Within this
framework, FIA only provided two usable time observations (averages 2000–04 and
2005–10). 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 regressions.

 

In comparison to the FAS data, the Global Findex data are
certainly more comprehensive and would potentially allow for a more robust
analysis. However, the Global Findex data measure FI at only two points in time
(2011 and 2014) with an assumption that relative financial inclusion did not
vary significantly over time. Hence, the Global Findex data could be interpreted
as a ranking rather than an absolute level

 

An ordinary least squares (OLS) estimation was conducted
taking into account a number of countries, relating an FI measure 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 per the early King and
Levine study) to address reverse causality:

 

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

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