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).
To test the relationship between financial inclusion
and stability, Sahay et
al. (2015) used panel regression with country fixed effects for the
timeframe from 2004 to 2011. Dependent variables were bank Z-score, taken from
the Global Financial Development database. Financial inclusion variables from
IMF’s Financial Access Survey1.
Thevariables were lagged by one year in the regression. The explanatory
variables were also interacted with the variable BCP, which approximates the
quality of bank supervision by measuring the degree of compliance with Basel
Core Principles (BCP). Two measures of BCP were tested: a composite of all the
principles, and a subset of BCP principles relevant to financial inclusion
(Core Principles 1, 3, 4, 5, 8, 9, 10, 11, 14, 15, 16, 17, 18, 24, 25, and 29).
Control variables were the lagged values of the Financial Institutions Depth
index (FID) from Sahay and others (2015a), real GDP per capita, excess of
credit growth above nominal GDP; contemporaneous variables of population,
FDI-to-GDP ratio, trade-to-GDP ratio, inflation, government balance, a dummy
for banking crisis, and the Lerner index. The coefficient on the variable
“number of borrowers per 1,000 adults” was found to be negative and significant
for both X and X2. The coefficient of the interaction with both
measures of BCP was positive. For other variables of financial inclusion, the
relationships were found to be insignificant or inconclusive.
Sahay et al.
(2015) defined inequality by the “ratio of 40″— income share
of the bottom 40% divided by the income share of the middle 40%. After
controlling for measures of human capital development (income, health, and
education), the study found that the ratio of adults obtaining loans has a
significant positive effect on the “ratio of 40” during the period 2007–12.
However, this effect did not hold when considering only loans from formal
financial institutions; thus, pointing out the role of informal modes of
finance, including family and friends, employers, and other sources. This
result (reducing inequality) held for the share of women receiving loans. The
effect was stronger and larger for a subsample that excludes high-income
countries. Finally, the positive effect on income equality was less noticeable
for other measures of inequality, such as the Gini coefficient, in which
changes can be driven by movements in countries with high income levels, with
already high financial inclusion. In general, financial inclusion has a positive
impact on achieving various macroeconomic goals; however, the magnitude of
subject gains diminishes with the rise of both dimensions (financial inclusion
and depth). Furthermore, there are noteworthy trade-offs in terms of financial
stability – i.e. increased inclusion could result financial destabilization. The
paper reaches to the conclusion that greater financial inclusion causes higher
growth but only to a certain extent. Increased access to banking services by
the individuals and businesses leads to higher economic growth. Same holds true
for increasing women users of these services as well. However, there is no
solid evidence on the macroeconomic effects of financial inclusion which is mainly
due to the fact that macro-level data on financial inclusion across countries
were in short supply.
Another paper which investigated the linkage of financial
inclusion and macroeconomic topics was Dabla-Norris et. al. (2015). In this paper, three
indices that embody various fragments of financial inclusion were formed which are
(i) utilization of financial services by individuals, (ii) utilization of financial
services by SME’s; and (iii) access to financing. The paper used three most
widely referred sources including the World Bank Global Financial Inclusion
dataset (Findex – available for two years: 2011 and 2014), which records the
methods of borrowing saving and payment structures in 148 countries; the IMF’s
Financial Access Survey (FAS), which presents the global supply-side data on
financial access in 187 areas, and finally the World Bank Enterprise Survey,
which firm-level data on access to finance for a representative sample of companies
in 135 economies. The authors developed composite measures of individual and company
financial inclusion looking at Latin American countries with both time-based
and cross-country perspectives. The indices were constructed to encapsulate various
aspects such as “access and effective usage of financial services” by
individuals and households. The study also appears to have optimized the use of
most relevant parameters (i.e. the use of accounts, savings, borrowing, and
payment methods but omitting of insurance for household inclusion index) given
data availability. Finally, the authors looked into different aggregation
methods, namely, weights derived from the principle component analysis (Camara,
N., and D. Tuesta, 2014), factor analysis (Amidži? et al., 2014) and equal
weights. The results were similar when using alternative measures.