# Based flows, inflation, and government consumption. This relationship

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

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. 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.