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 of financial inclusion witheconomic growth, financial and economic stability, as well as inequality. The analysis provided by Sahay et. al.demonstrated the macroeconomic ramifications of the notion of financialinclusion and its potential impact. 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 absence of long enough timeseries of financial inclusion (FI) data. For instance, the index of FinancialInstitution 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 between1980 and 2010, which was combined with a five-year average for all variables(used in order to smooth out cyclical variations) did unfortunately not providerobust and usable results in a standard GMM growth regression. Within thisframework, FIA only provided two usable time observations (averages 2000–04 and2005–10). For this reason, GMM regressions of this type cannot test for theimpact of FIA—or other financial inclusion indicators, for that matter— as theregressions would not pass the standard diagnostic tests. This paper used OLSestimation for the growth and inequality regressions. In comparison to the FAS data, the Global Findex dataare certainly more comprehensive and would potentially allow for a more robustanalysis.
However, the Global Findex data measure FI at only two points in time(2011 and 2014) with an assumption that relative financial inclusion did notvary significantly over time. Hence, the Global Findex data could be interpretedas a ranking rather than an absolute level An ordinary least squares (OLS) estimation was conductedtaking into account a number of countries, relating an FI measure at one pointin time (or averaged over a period) with growth over a period. Ideally, onewould have initial FI related to subsequent growth (as per the early King andLevine study) to address reverse causality: in which i denotes country and X denotescontrols. Additionally, one can alsoinclude 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 inclusionand stability, Sahay etal. (2015) used panel regression with country fixed effects for thetimeframe from 2004 to 2011.
Dependent variables were bank Z-score, taken fromthe Global Financial Development database. Financial inclusion variables fromIMF’s Financial Access Survey1.Thevariables were lagged by one year in the regression. The explanatoryvariables were also interacted with the variable BCP, which approximates thequality of bank supervision by measuring the degree of compliance with BaselCore Principles (BCP). Two measures of BCP were tested: a composite of all theprinciples, 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 Depthindex (FID) from Sahay and others (2015a), real GDP per capita, excess ofcredit growth above nominal GDP; contemporaneous variables of population,FDI-to-GDP ratio, trade-to-GDP ratio, inflation, government balance, a dummyfor banking crisis, and the Lerner index.
The coefficient on the variable”number of borrowers per 1,000 adults” was found to be negative and significantfor both X and X2. The coefficient of the interaction with bothmeasures of BCP was positive. For other variables of financial inclusion, therelationships were found to be insignificant or inconclusive. Sahay et al.(2015) defined inequality by the “ratio of 40″— income shareof the bottom 40% divided by the income share of the middle 40%. Aftercontrolling for measures of human capital development (income, health, andeducation), the study found that the ratio of adults obtaining loans has asignificant positive effect on the “ratio of 40” during the period 2007–12.
However, this effect did not hold when considering only loans from formalfinancial institutions; thus, pointing out the role of informal modes offinance, including family and friends, employers, and other sources. Thisresult (reducing inequality) held for the share of women receiving loans. Theeffect was stronger and larger for a subsample that excludes high-incomecountries. Finally, the positive effect on income equality was less noticeablefor other measures of inequality, such as the Gini coefficient, in whichchanges can be driven by movements in countries with high income levels, withalready high financial inclusion. In general, financial inclusion has a positiveimpact on achieving various macroeconomic goals; however, the magnitude ofsubject gains diminishes with the rise of both dimensions (financial inclusionand depth). Furthermore, there are noteworthy trade-offs in terms of financialstability – i.
e. increased inclusion could result financial destabilization. Thepaper reaches to the conclusion that greater financial inclusion causes highergrowth but only to a certain extent. Increased access to banking services bythe individuals and businesses leads to higher economic growth. Same holds truefor increasing women users of these services as well. However, there is nosolid evidence on the macroeconomic effects of financial inclusion which is mainlydue to the fact that macro-level data on financial inclusion across countrieswere in short supply. Another paper which investigated the linkage of financialinclusion and macroeconomic topics was Dabla-Norris et. al.
(2015). In this paper, threeindices that embody various fragments of financial inclusion were formed which are(i) utilization of financial services by individuals, (ii) utilization of financialservices by SME’s; and (iii) access to financing. The paper used three mostwidely referred sources including the World Bank Global Financial Inclusiondataset (Findex – available for two years: 2011 and 2014), which records themethods of borrowing saving and payment structures in 148 countries; the IMF’sFinancial Access Survey (FAS), which presents the global supply-side data onfinancial access in 187 areas, and finally the World Bank Enterprise Survey,which firm-level data on access to finance for a representative sample of companiesin 135 economies. The authors developed composite measures of individual and companyfinancial inclusion looking at Latin American countries with both time-basedand cross-country perspectives.
The indices were constructed to encapsulate variousaspects such as “access and effective usage of financial services” byindividuals and households. The study also appears to have optimized the use ofmost relevant parameters (i.e. the use of accounts, savings, borrowing, andpayment methods but omitting of insurance for household inclusion index) givendata availability. Finally, the authors looked into different aggregationmethods, namely, weights derived from the principle component analysis (Camara,N., and D. Tuesta, 2014), factor analysis (Amidži? et al., 2014) and equalweights.
The results were similar when using alternative measures. One of the most significant additions of Dabla-Norris et. al. (2015)study to the inclusion literature was the construction of the index offinancial inclusion for SMEs. Didier and Schmukler, 2014 previously analyzedthe individual components of the Enterprise Survey data; however, a compositeindicator was not previously explored. The comprehensive indicator of company’sfinancial inclusion (see table 1.3 below for details) certainly resulted inbetter understanding the relative position of Latin American Countries vis-à-visthe other regions on different aspects of financial inclusion. This is particularlyimportant given the fact that improving SME’s access and use of finance is akey economic policy priority in the subject region.