Site Loader
Rock Street, San Francisco

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


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 

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!

order now


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

Post Author: admin


I'm Anna!

Would you like to get a custom essay? How about receiving a customized one?

Check it out