Based crisis episodes as well as other standard

Based on asample of 128 countries over 1980–2013, this paper’s analysis showed thatfinancial development boosts growth, but the impacts weaken at higher levels offinancial development, and eventually become negative. Empirical analysis demonstratedthat there was a significant, bell-shaped, relationship between financialdevelopment and growth. The estimation approach addressed the endogeneity problemand controls for crisis episodes as well as other standard growth determinants,such as initial income per capita, education, trade openness, foreign directinvestment flows, inflation, and government consumption.

This relationship wasin line with recent findings in the literature (Arcand, Berkes, and Panizza2012).   Not much isknown about the macroeconomic implications of financial inclusion, with a fewrecent exceptions. Sahay and others (2015a), demonstrated that household’saccess to finance has a strong positive link with growth. The same paperfurther displays that the relationship between depth and growth is bell-shaped(i.e.

the law of diminishing returns), suggesting that the returns to growthfalls with higher depth beyond a certain point. However, financial institutionaccess (FIA), an index of the density of ATMs and bank branches that narrowlydefines inclusion, had a monotonic relationship with growth. Dabla-Norris andothers (2015) used a general equilibrium model to demonstrate how loweringmonitoring costs, relaxing collateral requirements and thereby increasingfirms’ access to credit would increase growth. Buera, Kaboski, and Shin (2012)via an entrepreneurship model found that microfinance has positive influence onconsumption and output.HO1  Sahay et.

al. (2015) examined the linkages offinancial inclusion with economic growth, financial and economic stability, aswell as inequality.  The analysis providedby Sahay et. al. demonstrated the macroeconomic ramifications of the notion offinancial inclusion and its potential impact.

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It shed light on the benefits andtrade-offs of financial inclusion in terms of growth, stability (both financialand macroeconomic), and inequality. They defined financial inclusion as theaccess to and use of formal financial services by households and businesses.The paper drew on several sources of data on financial inclusion. These dataincluded cross-country surveys for two different years, long-time series acrossseveral countries, and other survey-based data on firms’ access to finance. Theadvantage of using a variety of sources was that the analysis can shed light onmany aspects of financial inclusion.

The disadvantage was that the datasets arenot 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 hundredthousand adults. On the users’ side, a number of indicators were investigated:share of businesses and investment financed by bank credit, share of thepopulation with account at a formal financial institution by gender and incomegroups, share of firms citing finance as a major obstacle, share of adultsusing accounts to receive transfers and wages, share of bank borrowers in thepopulation and finally, the use of insurance products. The main challenge in building a relationship betweenlong-run growth and financial inclusion was the lack of long time series offinancial inclusion (FI) data. For example, the Financial Institution Access(FIA) index constructed by Sahay and others (2015a) had time series— number ofATMs, number of bank accounts—from the IMF’s Financial Access Survey (FAS)starting in 2004 at the earliest.

This did not provide robust and usableresults in a standard GMM growth regression with a sample period of 1980–2010and using five-year averages of all variables to smooth out cyclicalvariations. Within this framework, FIA only provided two usable timeobservations (averages 2000–04 and 2005–10)7. For this reason, GMM regressionsof this type cannot test for the impact of FIA—or other financial inclusionindicators, for that matter— as the regressions would not pass the standarddiagnostic tests. This paper used OLS estimation for the growth and inequalityregressions.