Credit in developing countries

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FINANCIAL INCLUSION INDICATORS FOR DEVELOPING COUNTRIES*:The Peruvian Case

Giovanna Prial Reyes and Edgar Salgado Chavez**Instituto de Finanzas Personales

Abstract

This paper documents recent development of financial inclusion for thePeruvian case. We propose a set of financial inclusion indicators and

explore their performance for the last decade. Despite the remainingspatial disparities in the deepening of the financial system, it hasincreased its coverage. Besides the indicators, we develop a simpleeconometric framework to investigate the microeconomic impacts ofgreater credit availability. We find that households, on their part aremore likely to take credits, cope better with shocks, register theirbusinesses and smooth their consumption and income in regionswhere credit availability has become more apparent.

* This paper is based on a previous study done by Giovanna Prial and Daniel Allan.

** The authors work at the Instituto de Finanzas Personales, http://www.ifp-finanzas.com. Please send questions andcomments [email protected]@ifp-finanzas.com.. This papers findings interpretations andconclusions are enterely those of the authors. All errors are responsibility of the authors.
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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I. INTRODUCTIONThe importance of sound and effective social inclusion policies has been widely accepted for more than acentury. However, while the first hints of social inclusion policies were focused in the lack of participation of thepopulation in the economic activities of the country and in governments efforts to fight against poverty; it was notuntil the last couple of decades that the term inclusion became part of the sociopolitical vocabulary. This termwas first related to education and diversity, and then broadened to cover issues as diverse as disability, health,race or gender discrimination, geographic location, religious or cultural backgrounds and globalization.Nowadays, social inclusion is generally perceived as a human right by itself, being widely conceived as apractice of ensuring that people in organizations and communities feel they belong, are engaged, and connectedthrough their activities to the goals and objectives of the organization or community they are part of.

However, one aspect of social inclusion has been left out until recent years, and that is financial inclusion, orinclusion of the population into the financial system. Issues relating to financial systems have been more focusedin the soundness of the system itself, in providing the right incentives for financial institutions (FIs) to engage intheir business but also taking into account and controlling their risk exposure. Credit risk, market risk andoperational risk are terms no one in the financial business is unaware of. Regulators and supervisors have

developed better techniques to understand the financial situation of any FI or financial conglomerate, and havebeen granted resources to take corrective action in case there is evidence of unmanaged or irresponsible riskbehavior in any institution. The amount of data that both regulators and the market use to track the soundness ofthe financial system is enormous and it keeps increasing, as the complexity of financial markets also increases.Nevertheless, data related to the availability of financial services to everyone who could need and afford it underreasonable circumstances had been scarce until the first wave of efforts to promote microfinance raisedawareness of the needs of the poor for financial services. Nonetheless, despite the effort toward the filling of thisinformation gap, data collected on financial access and inclusion remains fragmented and incomplete, whichmakes it difficult to understand the size of the gap in the provision of financial services and the best policies thatgovernments can put into operation to reduce it.

The importance of financial inclusion, meaning broad access to financial products and services for a significant

proportion of the population, is currently being debated. While its impact on economic development and povertyalleviation has not been clearly demonstrated (i.e. it has not been yet proved that increasing financial inclusionlevels only in a population, keeping other factors controlled and constant, has caused a noticeable andsignificant, long-lasting impact in income and wealth1), there has been some evidence showing that the benefitsdrawn from the greater availability of financial products and services, helps stabilize poor peoples income cashflows and consumption. The availability of adequate credit sources and deposit accounts directed to people withlow income allows them, to cope with unexpected expenses (i.e. a health emergency) without having to wait untiltheir next payment. Well-developed, accessible financial systems allow businesses and households to financeinvestment projects outside their current budgets and promote a more competitive environment as they providethe resources needed for introduction of new projects and businesses into the market 2. This is especiallysignificant in environments where alternative channels (direct investment or capital market access) are notavailable or are prohibitive for low-income economic agents without collateral or a previous credit history.

Availability of deposit accounts allow households to save resources for future use which is a first approach toinsurance of their household assets and provides access to alternative payment channels. Furthermore, broadaccess to insurance coverage and pension funds allows the population to save for special needs (for uncertainevents and retirement), which have a positive impact on households ability to accumulate wealth and conductlonger-term financial planning.

The objective of this paper is to produce, estimate, and analyze a set of financial inclusion indicators aimed toassess is the profundity of current financial access; and to map out how financial inclusion has evolved duringthe past decade. Furthermore, we also analyze what measures can be taken in order to deepen financial access.In that sense, we have focused our work on developing financial inclusion indicators that can be measurable andmeaningful for developing countries.

1 See Banerjee et. al (2010)2 See Pages (2010).


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One of the first steps in producing effective indicators of financial inclusion levels is defining what we mean byfinancial inclusion in the first place. There has been some debate among specialists (Kendall et. Al 2010, ) andyet there is not a widely approved definition of financial inclusion, but every definition embodies certain coreelements: broad access to a portfolio of financial products and services, financial education and a consumerprotection framework. Also, there is a general definition of the minimum requirements for these financial productsand services in terms of availability, quality, cost and sustainability. In this first approach, we have decided to usethe following definition, as shown in box N 1.

We believe that developing a broad set of qualitative and quantitative financial inclusion indicators measuringaccess, use and geographical distribution of financial products and services will help to identify adequatemeasures to incentive the market; therefore financial institutions can provide better targeted products andservices in terms of characteristics, distribution channels and prices.

However, it is important to distinguish between two groups inside the population who are financially excluded.One of these groups is comprised of people who do not use the financial system due to the presence of barriersthat prevent them from contracting with existing financial institutions, like geographical barriers, cultural barriers,

trust issues or inadequate products and services for a specific environment but who would be able to access anduse the financial products and services offered by financial institutions otherwise. The second group is describedby people who do not use the financial system because they do not have the means or the resources to use thefinancial system; that is, their economic condition is so critical that they would not have any means of repaying acredit, nor generating enough resources to cover even their most basic needs. The first group of people,however financially excluded, is financially capable, which means that they could engage in a productive andbenefiting use of the financial system if some conditions are met. The second group is financially incapable andthere will be no financial inclusion policy that will help these people. Instead, they would need a more directapproach to combat their poverty issues: direct transfers or subsidies of resources, social programs to enhancelabor and other actions that would have a direct impact in their income until their basic needs are met.. While weseek to improve financial inclusion, we are aware that only those people who are financially capable will be ableto be included, whereas people who do not meet financial capabilityconditions will need other type of support

before being able to become financially included.

Box N 1Definition of Financial Inclusion

Financial inclusion means that the majority of the population has broad access to a portfolioof quality financial products and services which include loans, deposit services, insurance,pensions and payment systems, as well as financial education and consumer protectionmechanisms. Promoting financial inclusion requires creating or enhancing market incentives todevelop and provide financial products and services focused in population with low levels ofaccess or use of other types of financial products and services, as well as empoweringfinancial users with the tools needed to better understand financial products and servicesoffered and the channels required to enforce their consumer rights.

Greater financial inclusion will promote economic development that contributes to a greaterwell-being of the society. This will be achieved through the establishment of mechanisms thatallow for greater access to products and services of financial institutions; deeper knowledgeabout banks and microfinance institutions, insurance companies and private pension funds;and improved information disclosure regarding financial products and services features,benefits and costs for consumers.


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We must point out that our analysis excludes new channels for accessing financial services, such as e-bankingand mobile banking, or mobile-initiated transactions, because of a lack of separable user data. Instead, we focuson more traditional financial instruments like agencies and ATM coverage. We also make a first attempt toprovide microeconomic evidence of the impact of financial deepening on Peruvian households. For that purposewe merge geographic data available from the Superintendence of Banking and Insurance (SBS in Spanish) andthe National Household Survey (ENAHO in Spanish). Moreover, we construct a panel data set for the last threeyears covered in the analysis of this paper. The strategy consists on relating the greater credit access throughPeruvian regions with greater take-up of credits at household level, better absorption of shocks and consumptionand income smoothing. Preliminary results suggest that greater credit availability in Peruvian regions, aftercontrolling for demographic and aggregate variables, holds a positive relation with greater credit take up.Moreover, households located in regions with greater financial access are more capable to cope with shocks,formalize their businesses and smooth their consumption.

The rest of this document will be organized as follows. Section II presents the current economic environment inPeru and its recent evolution, in order to adequately interpret the proposed indicators. Section III discusses themethodology used to calculate our proposed set of financial inclusion indicators as well as the particular

considerations about the data we have worked with, define and group those indicators into categories, andpresent our results both individually for each indicator and in aggregate. Section IV introduces a simpleframework to test the microeconomic implications of financial deepening. Finally, Section V presents ourconclusions and closing remarks.


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II. CURRENT MACROECONOMIC AND FINANCIAL ENVIRONMENT IN PERUThe last decade has been one of consistent economic growth for Peru. With an average annual GDP growth of5.1% between 2000 and 2009 despite the global financial crisis of 2008 2009, Peru has been able to adjust tothe negative global economic environment without having to sacrifice its fiscal balance or resort to excessiveexpansionary monetary policy. As a result, consumer price growth has been modest in the last decade, withaverage annual inflation for that period being 2.6%. This result, together with the improving economicenvironment, allowed for sustained growth of the financial system while maintaining internal price stability duringthe last decade. These factors promoted an increase of the GDP per-capita from US$ 2035 in 2000 to US$ 4533in 2009, which means a bit more than a 9% year-to-year increase in average.

Figure 2: Gross domestic product and consumer prices growth, 2000 2009

Figure 3: Gross domestic product per-capita, 2000 2009

These results are reflected in increased financial intermediation through regulated FIs, which include private andstate-owned banks, finance companies and microfinance institutions (municipal savings and loans institutions,rural savings and loans institutions and entities for the development of micro and small enterprises), all of whichare regulated and supervised by the SBS. Undoubtedly, the last decade has shown an explosive growth of loansand deposits, which have increased their size by more than 200% between 2000 and 2009, reaching US$37,371 and US$ 43,394 million by the end of 2009, respectively.

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Figure 4: Direct Loans and Total Deposits in Financial Institutions, 2000 2009

However, these economic and financial results have not translated into a deeper penetration of financial productsand services in relation to the size of the economy, compared with other Latin American countries. Despite theratio total deposits as a percentage of the GDP (a commonly used ratio for measuring financial penetration)having increased during the last five years, Peru is still below average (26.8% as of June 2009), far below Chileand Bolivia (who show ratios of 66.4% and 40.2%, respectively), considering deposits in banking institutionsonly.

Figure 5: Financial penetration in Latin America, June 2009

In an environment of economic growth and increasing financial intermediation, the low financial penetration levelof Peru compared to its peers means that there is still a significant portion of economic transactions taking placeoutside the financial system. This can only be explained by assuming that either: some individuals have noaccess to financial products and services (offered by regulated FIs); access is limited because of physical oreconomical barriers; or some economic agents are not willing to use existing financial products and services. Allof these factors help explain why, despite strong economic growth, some individuals still resort to informal oralternative financial services providers.

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Each of the above limits to overall use of formal financial services in Peru can be addressed through adequatepolicy actions such as fostering creation of low-cost, high-quality, simple products and services which couldappeal to a broader part of the population; stimulating the creation and strengthening of alternative channels likebanking agent, e-banking and mobile banking networks; investing in financial education initiatives; orstrengthening recourse mechanisms and consumer protection regulation so economic agents can make informedchoices and take proper action in case they have complaints about financial products or services provided byfinancial institutions.

Even if there is still a significant portion of the population that is unbanked, financial intermediation levels of Peruhave been growing since 2005 onwards, considering not only private and state-owned banks but also regulatedFIs total deposits and direct loans as percentages of annualized GDP. Total deposits have increased from26.2% of GDP in 2000 to 32.9% in 2009, while direct loans have risen from 22.3% to 28.3% in the same period,coinciding with the strengthening of the economy and the regulated financial system.

Figure 6: Financial Intermediation in Peru, 2001 - 2009

It is in this context that we define and measure a first set of financial inclusion indicators, whose main objective isto provide us with information regarding access and use of financial products and services, so adequatemeasures can be taken to correct and improve any identified weaknesses, and also to allow us to measure theimpact of any policy action taken on financial inclusion levels, so we can identify policy actions that are effectivein boosting financial intermediation.

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III. METHODOLOGY AND RESULTSThe financial inclusion indicators estimated in this document follow the work of Thorsten Beck et al. (2006)3,applying the indicators proposed in that paper to Peru, as well as expanding its set with five additional indicators,adding up to 13 financial inclusion indicators classified in three groups: indicators of access to financial productsand services, indicators of use of financial products and services and indicators of geographical distributioninequality.

a. Methodological considerationsIn order to understand the following indicators, there are some considerations that have to be taken into account:

Population and territory size was obtained from the National Institute of Statistics and InformationTechnologies (INEI, in Spanish). Macroeconomic data was obtained from the Reserve Central Bank ofPeru (BCRP, in Spanish) and financial data was obtained from the SBS, except for financial penetrationdata presented in Section II, which was obtained from the Banking Association (ASBANC).

As mentioned before, the SBS supervises and regulates not only the banking system, but also othernon-banking financial institutions, so we have credit, deposit and branch data for them. In order to showa more detailed picture, indicators forprivate banks (Multiple-operation banks, orBM), private banksand microfinance institutions (Multiple-operation institutions, orOM) and the whole financial system,including state-owned banks (Financial system, orSF) are presented.

We have considered, following the recommendations made by Thorsten Beck et al., the number ofdebtors and depositors (users) instead of number of loans and deposits. These measures affect ourcalculations when estimating indicators of use of financial products and services. Additionally, data fornumber of depositors was added up from raw data of depositors by type of deposit for 2000 2004,instead of taking into account depositors with more than one type of deposit, which causedoverestimation of the total number of depositors. This error was corrected from 2005 onwards, which

has caused an artificial decrease in the number of depositors and an artificial increase in the averagesize of deposits per depositor, but it does not alter the overall tendency of these series.

The SBS classifies loans and creditors by use, identifying four types: commercial loans, small businessloans, consumer loans and home mortgage loans, distinguishing between commercial and smallbusiness loans by total loans granted to a debtor, so a commercial loan implies previous or availableaccess to the financial system for a debtor (see box N 2). For that reason, we have decided to excludeloans and debtor data from commercial loans and debtors, since them are already financially includedin the system.

In order to focus on financial inclusion of the population, we have considered deposits and number ofdeposit accounts from individuals only.

In this third draft, we have developed two different groups of indicators regarding geographical financialinclusion, both of them considering total loans and total deposits (including commercial loans and bothprofit and non-profit institutions as well):

o Indexes of loan and deposit growth by location (separating between the capital city, Lima, andother cities). Since we are also interested in showing financial institutions ability to fosterfinancial inclusion by providing easier access to their products and services, for thesegeographical indicators we have only considered municipal and rural financial institutionswhich have their main offices in a city other than Lima but branches both in Lima and in otherprovinces, making them ideal to study this effect.

o Gini indexes comparing loans, deposits and number of branches against population by region.For this calculation we have used loans, deposits and branch data at the municipalities level,

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aggregating it by departments. Gini indexes give us an idea of distribution inequality, where thelower the index, the lower the inequality in the distribution.

Our indicators have been calculated for the period 2001 2009, except for financial institutions branch data,which was available from 2000 to 2009; financial institutions ATMs and banking agents, which was onlyavailable from 2008 to 2009; and loan and debtor-related data, which covers the period 2002 2009.

b. List and classification of indicators of financial inclusionThe indicators of financial inclusion we have constructed and calculated are the following:

I) Indicators of Access1. Number of branches per 1000 km2. Number of branches per 10,000 pop.3. Number of ATMs per 1000 km4. Number of ATMs per 10,000 pop.5. Number of agents per 1000 km6. Number of agents per 10,000 pop.

II) Indicators of Use7. Number of depositors per 1000 pop.

8. Number of debtors per 1000 pop.9. Average size of total deposits per depositor to GDP per capita

Box N 2

Classification of Credit Type by Use in Peru

According to the current Regulation for Debtor Classification (approved by Resolution N 808-2003), financial institutions should follow these directives for loan classification:

Commercial Loans: Direct or indirect loans granted to people or businesses for financingproduction and commercialization of goods and services, in their different phases.

Small Business Loans: Direct or indirect loans granted to people or businesses for financingproduction and commercialization of goods and services, with a total level of debt with thefinancial system not exceeding US$ 30,000. In case of loans granted to people, these debtorsshould have business activities as their main source of income. In case of people orbusinesses which are part of a financial conglomerate or an economic group, the limit will bemeasured against the conglomerate or the group, and not individually.

Consumer loans: Loans granted to people for use in products, services or expendituresactivities not related to a business activity, including credits granted by credit cards, leasingarrangements and any other financial operation not related to business activities.

Home mortgage loans: Loans granted to people for use in acquisition, building, reparation,remodeling, enlargement, improvement or subdivision of an owned housing, if such loan isguaranteed by a properly registered mortgage, including loans of such characteristics grantedto directors and officials of the granting financial institution.

If a debtor has more than one type of loan granted by the same financial institution, theirclassification should be based on the riskier type of loan, not taking into account consumerand small business loans lesser than twenty Nuevos Soles (S/. 20.00).


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10. Average size of total loans per debtor to GDP per capita

III) Indicators of Geographical Inequality Distribution11. Difference between participation of loans in provinces and participation of deposits in provinces (inpercentage points)12. Total loans in provinces to total deposits in provinces (index)13. Gini indexes between population, loans, deposits and branch offices per region

c. Resultsi. Indicators of Access

The following group of indicators provides a broad figure for estimating the existence and quantities of provisionchannels available to the population; that is, how many locations providing access to financial products andservices are available.

1) Number of branches per 1000 kmThe following indicator has been calculated considering the total number of physical branches by both banks andall regulated FIs, and measures geographical penetration of FIs branch networks, as a proxy of the averagedistance to a branch, if branch distribution was geographically uniform (which it is not). This indicator shows aconstant increase during the past decade, reaching more than 2.3 branches per 1000 km 2 in 2009, consideringall FI branches (and almost 2 branches considering private FIs only). According to the Financial Access 2009report by CGAP4, the World median is approximately 9.2 branches5 while the South American average is 1.99,so this figures show that Peru, despite being still under the 50th percentile, it is not far away from its peers.

Figure 7: Financial institutions branches per 1000 km2, 2000 - 2009

2) Number of branches per 10,000 pop.This indicator measures demographical penetration of the branch network, estimating how many people eachbranch would have to serve, so a higher number of branches would indicate than less people have to be servedby each branch, which would imply easier access. In Peru, there are just over 1 FIs branch per 10,000 people

4 See CGAP (2009)5 While the reported average on the 139 countries surveyed is 40.02 branches per 1000 km2, this figure is strongly biaseddue to the presence of two outliers: Singapore (599.4 branches) and Hong Kong (1386.8 branches).

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(0.87 branches belonging to private FIs), while the World median estimated by the Financial Access 2009 reportis 1.68 and the South American average is 1.23. These figures mean that Peru is still behind the average branchnetwork size for its population, although this indicator also shows a constant increase in recent years.

Figure 8: Financial institutions branches per 10,000 pop., 2000 - 2009

3) Number of ATMs per 1000 kmThis indicator provides information about geographical penetration of the ATM network. In Peru, there are 3.5ATMs per 1000 km2 (2.97 of them belonging to private FIs networks). Again, the World median according to theFinancial Access 2009 report was 15.5 ATMs per 1000 km 2 while the South American average is 6.6 ATMs per1000 km2, which means that Peruvians ATM network development, while expanding, is still very immaturerelated with other countries.

Figure 9: Number of ATMs per 1000 km, 2008 - 2009

4) Number of ATMs per 10,000 pop.This indicator estimates how many people should be served by each ATM, so the bigger this indicator is, thelower the number of people using the same ATM will be, thus providing easier access to the ATM network. In

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Peru, there are 1.53 ATMs per 10,000 people (1.3 ATMs belonging to private banks and microfinanceinstitutions), which is also under the median estimated in the Financial Access 2009 report: 3.34. South Americaaverage, in comparison, is a bit higher: 3.75 ATMs per 10,000 people.

Figure 10: Number of ATMs per 10,000 pop., 2008 - 2009

5) Number of agents per 1000 kmInformally called banking agents, these independent businesses work with financial institutions by offeringservices connected to a financial institutions network, thus being able to provide some of that financialinstitutions products or services to their surroundings. The presence of these agents has greatly increased thegeographic area and number of people FIs can serve. In Peru in 2009, there are 4.69 agents per 1000 km2, morethan double the figure for FI branches. There are no banking agents established by state-owned banks.

Figure 11: Number of agents per 1000 km, 2008 - 2009

6) Number of agents per 10,000 pop.The total population covered by correspondent tellers (the Peruvian Governments official name for thesebanking agents) has also increased in the past years, and in 2009 there were 2.1 agents per 10,000 pop.

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Figure 12: Number of agents per 10,000 pop., 2008 - 2009

ii. Indicators of UseThe following group of indicators provides broad estimates of the portion of the population who actually usefinancial products and services through any available channel; that is, who decide to use those products,services and access channels made available to them by FIs.

7) Number of depositors per 1000 pop.This pair of indicators (this and the next one) estimate the proportion of the population using the most demandedfinancial services: deposits and loans. For deposits, there has been a constant growth of the number of peopleusing them, and there are now more than 425.2 people with a deposit account per 1000 people, or a little lessthan 3 in 7 people.

Figure 13: Number of depositors per 1000 pop., 2000 - 2009

8) Number of debtors per 1000 pop.

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This indicator, as the previous one, estimates the proportion of the population who has access, has decided toask for and was granted a loan. In Peru, the figure has grown between 2002 and 2009 from a little less than 90people to 247.5 people per 1000 people (or about 1 in 4 people). This means that in recent years FIs haveextended their reach to those previously outside the financial institutions sphere of influence with microfinanceand low-income, consumer-oriented products and services. The fall in the private bank (BM) series observed inthe graph is not a change of tendency in 2009, but the consequence of a consumer-oriented bank turning into afinance company (which is included in the OM series).

Figure 14: Number of debtors per 1000 pop., 2002 - 2009

9) Average size of total deposits per depositor to GDP per capitaThis indicator estimates a rough proxy of the savings/income ratio of the population, but it has to be interpretedwith care. The graphic shows a constant decrease of the total deposits per depositor/GDP per capita ratio overthe last decade, going from about 48% to 29% in 8 years, which could be seen as a tendency to save less.However, if we take into consideration that both GDP per capita and deposits in financial institutions have beengrowing over the last decade, as well as the total deposits/GDP ratio, and the number of depositors per 1000people has been also growing, we will see that this indicator shows that the average size of total deposits perdepositor have reduced because new depositors with smaller deposit accounts have entered the financial systemduring recent years. That means we now have a less-concentrated deposit base, with more depositors and

smaller deposits in average, which probably come from regions or groups previously unattended by FIs.

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Figure 15: Average size of total deposits per depositor to GDP per capita, 2000 - 2009

10)Average size of total loans per debtor to GDP per capitaSimilar to the previous indicator, this measure provides an estimation of the average debt size per debtor (that is,their loan portfolio as a whole) against income. This indicator has also fallen from 58.4% to 50.3% in the lastdecade, although it shows a growing tendency since 2007. In a context of growing GDP and GDP per capita,growing financial institutions total loan portfolio size and growing number of debtors per 1000 people, this meansthat, on average, the average debt size of the population has been increasing in the last two or three years. Morepeople are having access to credit and those with previous access are using more of it, something that isreasonable in a context of economic growth like the one observed in Peru in the last years. It is also probably asign that financial institutions (specially private banks) are feeling more comfortable giving loans now than at thebeginning of the decade, in order not to repeat the explosive consumer-oriented loan growth that was observedduring 1993 1997 and which ended in over-indebtedness for a significant portion of the population many ofthem people recently included in the financial system due to a consumer loan or a credit card and a creditcrunch for them at the end of century.

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Figure 16: Average size of total loans per debtor to GDP per capita, 2000 - 2009

iii. Indicators of Geographical Distribution InequalityThe following section presents two groups of indicators which provide a first approach to geographicaldistribution and impact of FIs branch networks in different regions. In the first group, we try to focus on the effectof having regional FIs participating outside their base cities. Specifically, we are interested in analyzing thebehavior of municipal and rural savings and loans institutions, whose main offices are located outside of Lima, tostudy if they are using their branches in Lima to siphon resources out of its population and finance projects inlower-income cities with them; that is, if the presence of a bigger branch network allows for resource transfersbetween high-income and low-income regions. The hypothesis here is that municipal and rural savings and loansinstitutions are financing the creation of loans in Peruvian provinces with deposits raised in Lima, effectivelycontributing to a redistribution of resources. As a first approach, we will follow the evolution of credits anddeposits generated in Lima and in other regions considering that Lima average levels of well-being are superiorto those of other cities.

The second group of indicators has been calculated from the distribution of loans, deposits and FIs branches,comparing them against the population distribution in Peru, at the departmental level. In this case we haveestimate Gini indexes, which compare the cumulative distribution of resources against the cumulative distributionof population (the Lorenz curve), thus measuring deviations in resource distribution from the equality distribution.These indexes does not take into account other factors like regional production levels or initial wealth of thepopulation in each department, thus allowing us to see the pure statistical distribution, which then can be used as

a starting point to find some answers about financial inclusion in each department.

11) Difference between participation of loans in provinces and participation of deposits in provinces (inpercentage points)

This indicator (shown in the graphic below as a blue bar) provides a broad picture of the evolution of thedynamics of loan and deposit creation in Peru, between Lima and other cities, considering that if the gapbetween the proportion of loans not in Lima and the deposits not in Lima grows (or the negative gap falls), thosenew loans not in Lima are probably being financed by deposits or funds from Lima or abroad. Normalizing theindicator to zero in 20016 (so we dont have to deal with a negative indicator), we observe an increase in theparticipation gap of 15.9 percentage points, which means that the participation of loans in provinces has grown

6 The actual difference of between participation of loans in provinces (94,6%) and participation of deposits in provinces(97,9%) was 3,3 percentage points in 2001.

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faster (or decreased slower) than participation of deposits in provinces, thus suggesting a transference ofresources from Lima to other cities via the these specialized, province-based financial institutions.

12) Total loans in provinces to total deposits in provinces (index)This indicator (the red line in the graphic below) is the normalized ratio of total loans outside Lima over totaldeposits outside Lima, considering municipal and rural savings and loans institutions. We can see that thisnormalized ratio has also grown during the past decade, reaching a value of 1.25 in 2009, which means thatloans in provinces outgrew deposits in provinces by near 3% per year. This also suggests that funding for thoseloans should have come from other sources of financing not located in these provinces, like deposits from Limaor credit lines from banking institutions from Lima or abroad.

Figure 17: Indexes of Geographical Distribution of loans and deposits, 2001 - 2009

13) Gini indexesAs it was mentioned at the beginning of sub-section III.c.iii, we have estimated three series of Gini indexes,comparing the cumulative distribution of Peruvian population per department against the total value of the loans,the total value of the deposits and the number of financial institutions branches opened in each department.Each series allows us to study the evolution of the inequality in the distribution of loans, deposits and FIsbranches, while the three of them combined allow us to investigate the relationship between these threedistributions.

During the past decade, the Gini index for the distribution of total loans decreased from 0.60 to 0.46, due to thereduction in the participation of loans created in Lima from 85% to a little over 70% of the total portfolio of loansin the financial system. This process has been steady during the decade, with each year registering a constantincrease of the participation of loans in provinces over the total loan portfolio and a constant decrease of the Giniindex of about 0.02 points.

0

3

6

9

12

15

18

1.00

1.05

1.10

1.15

1.20

1.25

1.30

2001 2002 2003 2004 2005 2006 2007 2008 2009

%Part.

LoansinProv-%Part.Deposits

inProv.(2001=0)

LoansinProvinces/Depositsin

Provinces(2001=1)

%CredProv -%DepProv

CredProv/DepProv


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Figure 18: Lorenz curves of the distribution of loans against population, 2001 and 2009

Regarding deposit distribution, however, we dont observe the same process. The Gini index of deposits againstpopulation decreased from 0.58 to 0.53 between 2001 and 2009, but most of that decrease was achievedbetween 2008 and 2009 (0.04 points). In fact, this Gini index increasedduring 2007, due to a significant raise ofdeposits in Lima. Total deposits in Lima have accounted for 82% - 83% of total deposits during most of thisperiod, which could mean that people in Lima have a greater capability for create savings, but could also be theresult of a strategy in the financial system for raising resources in Lima in order to finance their lending in otherregions.

Figure 19: Lorenz curves of the distribution of deposits against population, 2001 and 2009

Analyzing the Gini index of branches against population, we come to an unexpected result. One of the mainsuppositions regarding financial exclusion is lack of access to branch offices, but in the Peruvian case this hasnot been the case. The Gini index has been very low during the decade, ranging from 0.23 in 2001 to 0.18 in2009, which means that inequality in the distribution of branches against population of each department hasbeen low. Even more surprising, Lima is not located at the top the branch distribution, but Moquegua, and thereare two other departments before Lima in 2009 (Lima has 1.41 branches per 10,000 people while Moquegua has1.77 branches per 10,000 people in 2009). Lima branches have decreased their participation from 46% to 42% ofthe total branch network.


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This result indicates that the observed inequalities in the geographical distribution of loans and deposits could beless related to lack of access and more related to other factors, like lack of knowledge about the products andservices offered by the financial system, low levels of trust in the financial system or a perception that existingproducts and services are not adequate for part of the population (especially in the case of deposit accounts).

Figure 20: Lorenz curves of the distribution of branches against population, 2001 and 2009

The following figure shows the evolution of the Gini indexes discussed in this section, and its clear from it thatthe branch distribution has never been as unequal as the other two, and its also visible that the loan distributionhas been reducing its inequality levels in a slow but constant rate, while the deposit distribution is still veryconcentrated in Lima. We are seeing a financial inclusion process from the credit side (retail or small-businessloans).

Figure 21: Evolution of Gini indexes of loans, deposits and branches, 2001 2009

Summarizing all the results, we can see that financial institutions have been doing an effort to increase theirnetwork reach, especially in later years, with agents being one of their most potent tools in doing so. While it istrue than their network is still very small compared with other countries, which could explain in part the lowfinancial penetration levels observed in Peru, these efforts have shown some results, both in number of clientsand in the source of those clients, being probably added from sectors with previous low or nil access to financial

0.10

0.20

0.30

0.40

0.50

0.60

0.70

Loans

Deposits

Branches


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services. We have also shown that presence of financial institutions not based in Lima can foster creditopportunities and financing for families and small businesses in provinces, which have been happening over thelast years since municipal and rural savings and loans institutions were granted permission to operate in Lima in2003; however, it has been also shown that while more funding is available for people living outside Lima, thereis not a deposit-creation process of the same level going on, despite the existence of financial institutionbranches (often named as one of the most important drivers of deposit creation), which could mean that peopleoutside Lima face other barriers besides lack of physical access to become financially included, at least fromthe savings side.

IV. MICROECONOMETRIC EVIDENCEIn this section we provide econometric evidence on the relationship between financial access and a set ofvariables related to credit take up, shock resilience, business initiative and consumption and income smoothingat household level.

In doing so, we rely on two data sources. First, we use the financial inclusion indicators developed in the abovesections with SBS data. Our second data source is the ENAHO. Since the national survey is a very

comprehensive data set covering all departments of Peru, data of financial access provided by SBS atdepartmental level can be easily matched to the data covered in ENAHO.

In a similar vein, the work of Aguilar (2011) assesses the relation between the availability of microcredit andeconomic growth at the regional level. Using regional-level production data to estimate the rate of economicgrowth and the provision of loans, also at the regional level, the author finds evidence that suggests a positiverelationship between economic growth and the expansion of microcredit availability. Interestingly, using analternative measure of financial deepening such as bank intermediation, the author does not consider any effectof this variable on economic growth at the regional level. As a simulation exercise, it is shown that if the provisionof loans from rural banks, municipal banks and banks specialized in microcredit reaches 10 percent of GDP, thatwould imply a 4 percentage point increase in the GDP per capita growth rate. An important drawback of thisstudy is the assumption of homogeneity of the relationship between credit expansion and the development of

regions. While it seems likely that certain activities with different credit requirements are concentrated in differentregions, this possibility is not taken into account in the analysis.

The strategy adopted in this paper consists on exploiting regional and time variability in financial access to gaugethe effect of greater credit availability on households indicators. In the same token, the rich data provided byENAHO allows us to control for demographic and geographic variables when modeling the impact.

We construct a panel of households spanning from 2007 to 2009. The sample is constituted by an unbalancedsample of households. There are four groups of variables we are interested in:

a. Take upIn this first group we aim to measure whether households are more likely to take credit within

regions with greater credit availability. The variables we look at are related to taking a credit forhousehold repairs, in general, and differentiated by the type of institution: commercial bank orBanco de Materiales (a major source of funding for construction, managed by the government). Wealso look at the amount of credit taken.

b. Shock reactionsIn this section we group variables indicating the capability to cope with unexpected events. Moreprecisely, we assess the relationship of greater credit availability with the absorption of shocksunderwent by the households.

c. BusinessHere we have variables indicating whether the independent business of the household took certainactions under the context of greater credit availability. We have variables indicating businessregistration as a formal entity, the starting up of new businesses and the profits earned.


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d. Welfare SmoothingIn this last group of variables we explore the capability of the household to minimize the variabilityof the transitory component of both consumption and income.

For groups a, b and c we follow a model as in equation (1)

(1)

Where i represents the household, c is the geographic location of the household, and t is the timedimension. is the fixed effect for the household whereas is a time dummy. The vector has controlvariables as household size and the consumption decile7. FI is the financial inclusion indicator (in logs) per regionc. In the estimation we have relied in the access indicators: number of agencies per 10,000 individuals (laggedone year8). In order to provide more results, we have considered two types of institutions, banks and a secondgroup comprised by Cajas Municipales and Cajas Rurales (termed cajas). is the error term. Finally, wepresent results for the national sample, and disaggregated by urban or rural status.

The estimation procedure for group c required some assumptions. Since we are interested in evaluating thecapability of smoothing transitory outcomes, we have to assume a structure that allows for permanent andtransitory outcomes9. Equation (2) presents the model;

(2)

is the output variable we are interested in: consumption or income (per capita and logs). The first threeelements in the right hand side of the equation represent the permanent component of the output. As in equation(1) captures the household fixed effect. With the element we are assuming each household follows anidiosyncratic trend10; and with the vector we are controlling for the variables that also capture permanentcomponents of variable of interest (consumption or income): the household size and the number of adults in thehousehold. Finally, the error term represents the transitory component of the variable of interest. For the

purpose of the estimation of model in equation (2) we constraint the sample to a balanced panel of three years.In that sense, we end up with three transitory outputs for each variable of interest, per household. Since we areinterested in consumption or income smoothing, we need to come up with a measure of transitory variability. Thesmaller the variability, the more stable the income or consumption of the household over the period. Thus, weestimate the standard deviation per household through the 2007-2009 period. That standard deviation is thenassessed in the face of greater credit availability. Equation (3) shows the relationship:

(3)

is the standard deviation of the transitory output, per household. is the average variability of the transitorycomponent of the sample, whereas is the growth rate of the financial inclusion indicator for the 2007-2009period. We are interested in assessing whether households located in a region with greater growth in financialinclusion experienced a smaller variation in their transitory components of consumption or income.

Results are shown in the appendix. Tables 1 to 3 present the results for model (1), whereas table 4 does so formodel (3). As stated above, we have considered two types of variables representing credit access perdepartment: number of bank agencies per 10,000 individuals and number of caja agencies per 10,000individuals. Both variables in logs. Finally we have considered three types of estimations to investigate whether

7 That is, we divide the complete consumption per capita distribution between ten groups, from the poorest to the richest.This is a wealth control variable.8 Data from ENAHO is collected through all the year, thus we have to allow for some time between the arrival of newagencies and their impact in the cities.

9 See Deaton (1998).10 As stated in Wooldridge (2002), this is sometimes called a random trend model, as each individual is allowed to have itsown trend. The additional individual-specific trend is another source of heterogeneity.


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there is some heterogeneity driven the results: besides the national estimation, we present results for urban andrural samples.

Looking at the results, In table 1 we show some credit take-up variables. First, there is no correlation in theoverall credit up-taking for housing repairs for any of the samples, although the coefficients are suggestivelypositive. When looking at the credit up-taking from Banco de Materiales, we dont find any significant result,although the coefficients turn to negative. It is in the third variable, credit from a commercial bank that we see apositive and significant correlation. Interestingly, the correlation seems to be driven by credit up-taking in ruralhouseholds. In general, a 10% increase in the number of bank agencies per 10,000 inhabitants is related to a 1%increase in the likelihood of taking a credit for housing repairs. Data from ENAHO also allows us to see theamount the household requested in the loan for housing repairs. When assessed, we see a positive relation heretoo. Suggesting the major credit availability at regional level is related to an increase in the amount householdrequests. Although the national result seems to be driven by the coefficient in urban households. Thus, if there isa suggestive relation pointing out not only to an increase in the likelihood of credit take up, but also an increasein the amount the household requests. Finally within this group we assess a more general take up variable.Within the perceptions module in the ENAHO, the head of the household is asked for any type of financialservice he/she took during the last year. This variable, then, could embody any type of financial service 11, not

only credits. The result suggests there is a positive correlation, specially driven by urban households, and theeffect is stronger for the banks variable.

Once stated that there is a significant correlation between the greater credit availability and credit take up, it isworth asking about the implications this credit availability has on other variables. Table 2 relates credit availabilityto a set of variables that explain the capability of the household to cope with shocks12. The first variable indicatesthat the household suffered a shock13 in the last 12 months and had to use its own saving to cope with it. We seethat in locations where credit availability has increased, the likelihood of depleting own savings is smaller. Thus,suggesting households have more choices when faced with a shock. The second variable tries to confirm this,but we have an unexpected result. The likelihood of using a loan to cope with a shock is smaller in locations withgreater credit access. We suspect some error measurement in the variable, since the head of the household isnot asked to specify what kind of loan he/she requested. So, it might be the case of informal loans, and the

greater credit availability is operating here through a substitution effect. However, this cannot be confirmed withENAHO data. The final column of table 2 shows a more extreme measure to cope with the shock: reducingconsumption. Here we find a strong significant effect for cajas. Households that suffered a shock are less likelyto reduce consumption in those regions where cajas have increased more. It is worth noticing the extent of thecoefficient is higher for rural households.

Another interesting dimension to look at is the business behavior of the households. Table 3 presents someresults in this regard. Based on data from the independent business module in ENAHO, we construct threevariables to investigate their relation to greater credit availability. The first column shows the likelihood thebusiness is registered (opens up a tax payer registration number, RUC in Spanish), and we see that there is apositive overall correlation for cajas, which in turn is confirmed in the urban and rural subsamples. Also, greatercredit availability measured by bank agencies has an impact only in the rural sample. This results is aligned with

the within firms channel leading to formalization of firms, as explained in Moron, Salgado and Seminario (2012):

The within (or intensive) channel operates by encouraging formalization within the firm's size category. The ideais that access to credit requires compliance with tax and employment legislation. Thus, firms are more likely toincur such costs of formalization once bank credit is more widely available at a lower cost

As in their work, we find here a positive relation between greater credit availability in regions, and the likelihoodof registering the business. More interestingly, we find this effect to be particularly strong in the rural subsample.When we look, however at the likelihood of starting new businesses, there is no statistically significant

11 Unfortunatelly, there is no a detailed explanation about the definition offinancial service.

12 We are interested in shocks with big impacts. Thus, the shock variable used in this estimation is defined as an incomeshock that had big consequences on the household.13 Income shock.


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association. This result, suggestive enough, points out to the fact that starting a business in Peru is not toodifficult, thus credit access does not affect this behavior. Nevertheless, when it comes to improving the business,through the registration, credit becomes important. Finally in table 3 we see that businesses profits are higher forhouseholds located in regions where credit has expanded more intensively. This result is strong for all thesamples.

Finally, table 4 presents the results for the model explained in equations (2) and (3). We find that the growth incredit availability measured as the number of agencies per 10,000 inhabitants has a negative impact in thevolatility of transitory consumption and income. Households located in regions that experienced a higher growthrate have less volatile consumption and income. This result confirms one of the major advantages of credit: thesmoothing of consumption and income. The recent period of financial development in Peru is not the exception inthis regard.

V. CONCLUSIONSFinancial inclusion is not only important because it can potentially enhance economic growth and reduce external

shocks on households and business alike (by allowing people to stabilize their cash flows), but because likeother types of inclusion it makes individuals feel part of a group, thus raising their self-esteem and their well-being, finally having an impact in overall welfare. It is a human right; in the sense that anyone who needs aservice provided in a region and is able to afford it under normal market circumstances should be able to accesssuch a service. In this case financial inclusion guarantees the ability of people to transform wealth over time,accessing future wealth before time or storing wealth for the future (and future generations), which makesdevelopment planning over time feasible, reduces wealth fluctuation and contributes to population overall well-being. We believe that this paper, by providing a first set of tools with which to study and evaluate financialinclusion, contributes in a small way to this ultimate goal.

In this paper we have defined and developed a set of financial inclusion indicators to provide effectiveinformation about the access and use of financial services by the population and complement other financial

indicators commonly used in the literature. We have also estimated those indicators and provided a firstexplanation of their evolution over the last decade, and what it could suggest for the financial system and theregulator in order to increase both access and use of financial products and services. As a first step, we haveprovided broad indicators of access and use, and we have estimated a first group of indicators of geographicalinequality in financial inclusion. Finally, we have merged some of these indicators with household data in order toevaluate its microeconomic relevance. Results suggest at households are reacting to the greater creditavailability by taking up more credits. The greater credit availability has also different effects over householdpopulation. It helps them to better cope with shocks, fosters business registration and increases business profits.Finally we showed financial deepening is also related to a more stable consumption and income stream for thehouseholds.

We believe that this data, aggregated over time, will help financial institutions, policymakers and researchers

alike to understand the problem of access to financial products and services, propose adequate measures toenhance financial inclusion and evaluate their impact, both in the short and in the long term.

Financial institutions have been doing an effort to increase their network reach, especially in later years, withagents being one of their most potent tools in doing so. While it is true than their network is still very smallcompared with other countries, these efforts have shown some results, both in number of clients and in thesource of those clients. We have also shown that presence of financial institutions not based in Lima can fostercredit opportunities and financing for families and small businesses in provinces, which have been happeningover the last years since municipal and rural savings and loans institutions were granted permission to operate inLima in 2003; however, it has been also shown that while more funding is available for people living outsideLima, there is not a deposit-creation process of the same level going on, despite the existence of financialinstitution branches (often named as one of the most important drivers of deposit creation), which could meanthat people outside Lima face other barriers besides lack of physical access to become financially included,at least from the savings side.


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Despite this spatial inequality, financial deepening experienced during the last years has some positiveoutcomes. Matching household survey data with aggregate data on financial deepening provides a useful way toinvestigate the microeconomic implications of greater credit availability. In this paper we have shown that credittake up increases for households located in regions where credit has become more available in the form of moreagencies. But not only the likelihood of taking a loan has increased, but the amount the household takes also isgreater for households located in regions with more financial deepening. Rural households are most responsivein terms of taking credits from commercial banks.

We also evaluated households capability to absorb shocks and we found households located in regions withgreater financial deepening are better prepared to endure shocks: they are less likely to use their own savingsand reduce consumption in the event of an income shock. However, when asked whether the household took aloan to cope with the shock, our results show a decrease in the likelihood of doing so. However counterintuitivethis result, it might be a consequence of error measurement in the took a loan variable to cope with the shock.There is no specification of the type of loan, thus it might also be an informal loan. Thus the effect of financialdeepening is less clear in this respect.

Business behavior was also assessed, and we found independent small businesses being more likely to formallyregister when credit becomes more available. This might be a consequence of formal requirements for accessingformal credit. This result is stronger for rural businesses. Also, there is a significant increase in profits reportedby independent businesses operating in regions with greater credit availability. This result is strong for allsamples.

Finally, since one of the most important features of financial development is that it allows people to smooth theirconsumption and income, we developed a simple framework to test this. After estimating temporary componentsof consumption and income per capita, we find that higher financial deepening is related to a significantly smallervolatility of temporary income and consumption at household level.


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References

Aguilar, G. (2011): Microcrdito y Crecimiento Regional en el Per," PUCP Documento de Trabajo,Departamento de Economa, 317.

Banerjee, A., E. Duflo, R. Glennerster, y C. KinnanThe (2010). The Miracle of Microfinance?: Evidence from arandomized evaluation MIT, Departamento de Economa.http://econ-www.mit.edu/files/5993

CGAP (2009), Financial Access 2009: Measuring Access to Financial Services around the World The WorldBank.

Deaton, A. (1998) Economics and Consumer Behaior. Cambridge University Press.

Kendall, J., N. Mylenko, and A. Ponce (2010) Measuring Financial Access around the World. The World Bank,Policy Research Working Paper Series, 5253.

Morn, E., E. Salgado y C. Seminario (2012) Financial Dependence, Formal Credit and Firm Informality:

Evidence from Peruvian Household Data Inter American Development Bank Working Paper Series, 288.

Pages (2010) The Age of Productivity. Inter American Development Bank.

Prial, G., L. Allan y R. Mazer (2012) Financial Inclusion Indicators for Developing Countries: The PeruvianCase

Thorsten Beck, Asli Demirguc-Kunt and Maria Soledad Martinez Peria (2006) Reaching out: Access to and useof banking services across countries. World Bank.

Wooldridge (2002) Econometric Analysis of Cross Section and Panel Data MIT Press.
http://econ-www.mit.edu/files/5993http://econ-www.mit.edu/files/5993http://econ-www.mit.edu/files/5993http://econ-www.mit.edu/files/5993


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Table 1. Credit take up

Requested a loan

for housing repairs

Requested a loan for housing

repairs - Banco de Materiales


repairs - Commercial Bank


repairs - Amount (S/.)

Household has bought any

financial service

All Urban Rural All Urban Rural All Urban Rural All Urban Rural All Urban Rural

Bank 0.006 0.007 0.004 -0.001 -0.000 -0.001 0.009** 0.008 0.012*** 25.613 38.026 18.150 0.014** 0.026** -0.003

(0.006) (0.010) (0.007) (0.002) (0.003) (0.001) (0.004) (0.007) (0.004) (69.647) (107.173) (69.735) (0.007) (0.011) (0.004)

Cajas 0.003 0.003 0.003 -0.001 -0.001 -0.001 0.006** 0.006 0.008*** 27.698 23.404 42.942 0.005 0.011 -0.003

(0.004) (0.006) (0.004) (0.001) (0.002) (0.001) (0.003) (0.005) (0.002) (45.420) (71.805) (43.822) (0.004) (0.007) (0.002)

Obs 21455 12669 8786 21455 12669 8786 21455 12669 8786 21455 21455 21455 21455 21455 21455

Own estimations using data from ENAHO and SBS.

Table 2. Shock Reactions.

Suffered a shock and had to use

savingsSuffered a shock and had a loan

Suffered a shock and had to reduce

food consumption

All Urban Rural All Urban Rural All Urban Rural

Bank 0.002 -0.005 0.012 -0.012 -0.022* 0.004 -0.000 -0.009 0.012

(0.008) (0.011) (0.013) (0.009) (0.012) (0.011) (0.009) (0.009) (0.016)

Cajas -0.001 -0.001 -0.001 0.000 -0.006 0.010 -0.005 -0.005 -0.005

(0.005) (0.007) (0.008) (0.006) (0.008) (0.007) (0.006) (0.006) (0.010)

Obs 21455 12669 8786 21455 12669 8786 21455 12669 8786

Own estimations using data from ENAHO and SBS.


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Table 3. Business

Registered household businessStarted a new business in the

householdBusiness profits (S/.)

All Urban Rural All Urban Rural All Urban Rural

Bank 0.009 0.009 0.009* 0.003 0.006 -0.002 80.384*** 107.116*** 46.748**

(0.007) (0.012) (0.005) (0.005) (0.007) (0.006) (24.451) (38.328) (21.953)

Cajas 0.015*** 0.025*** 0.003 0.003 0.001 0.006 56.345*** 79.770*** 30.935**

(0.005) (0.008) (0.003) (0.003) (0.004) (0.004) (15.944) (25.676) (13.796)

Obs 21455 12669 8786 21455 12669 8786 21455 12669 8786Own estimations using data from ENAHO and SBS.

Table 4. Smoothing

Standard deviaton of temporary

consumption

Standard deviaton of temporary

income

All Urban Rural All Urban Rural

Bank -0.035*** -0.018 -0.031* -0.017 0.009 -0.018

(0.011) (0.014) (0.018) (0.017) (0.023) (0.024)

Cajas 0.030*** 0.027** 0.017 0.014 0.014 -0.007

(0.009) (0.011) (0.015) (0.013) (0.017) (0.020)

Obs 4429 2507 1922 4429 2507 1922Own estimations using data from ENAHO and SBS.


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Documents

Credit in developing countries