financial development and income inequality in indonesia

Economics and Finance in IndonesiaVol. 64 No. 2, December 2018 : 111–130p-ISSN 0126-155X; e-ISSN 2442-9260 111

Financial Development and Income Inequality in Indonesia: ASub-national Level Analysis

Harry Agintaa,∗, Debby A. Sorayaa, and Wahyu B. Santosoa

aBank Indonesia

Abstract

This study constructs financial inclusion indicator and analyzes the link of financial inclusion and incomeinequality for 33 provinces in Indonesia. By using Fixed Effect Panel Model, we find financial inclusionappears to have insignificant effect to on inequality at national level. While at sub-national level, addingother variables such as GRDP, years of schooling, and trade openness, we find financial inclusion appearsto have negative and significant impact on income inequality in manufacture and mining-based provinces,not in agriculture-based. The results suggest that financial inclusion helps to lower income inequality wheneconomic condition encourage people to utilize financial access for productive purposes.Keywords: financial development; income inequality; Fixed Effect Panel Model

AbstrakMakalah ini menyusun indikator inklusi keuangan seluruh provinsi di Indonesia dan menganalisis keterkaitaninklusi keuangan dengan kesenjangan pendapatan. Di tingkat nasional, hasil estimasi menggunakan FixedEffect Panel Model menunjukkan bahwa inklusi keuangan tidak berdampak signifikan terhadap kesenjanganpendapatan. Dengan menambahkan variabel lainnya seperti PDRB, lama sekolah, dan keterbukaanperdagangan, model estimasi menunjukkan hasil yang bervariasi. Inklusi keuangan berdampak signifikandalam mengurangi kesenjangan pendapatan pada provinsi yang didominasi oleh sektor industri pengolahandan pertambangan, tidak di sektor pertanian. Hal tersebut mengindikasikan bahwa inklusi keuangan akanmengurangi kesenjangan pendapatan jika kondisi ekonomi setempat mendukung masyarakat memanfaatkanakses keuangan untuk kegiatan produktif.Kata kunci: inklusi keuangan; kesenjangan pendapatan; Fixed Effect Panel Model

JEL classifications: E5; G21; G28; R11; R12

1. Introduction

After the 1997 to 1998 Asian financial crisis, thegrowth of Indonesia’s economy has been relativelyhigh. In the past 17 years, the nation’s gross do-mestic product (GDP) rose on average by almost5.4 percent annually, making it as a newly globaldarling. The relatively stable political and macroe-conomic conditions make Indonesia able to attractforeign investors to enter domestic market.

∗Corresponding Author: +628111684369. Email: [email protected].

However, the growing national economic growthhas been accompanied by widening income gapbetween rich and poor. The gap, as measured bythe Gini coefficient, shows an upward trend overthe past 27 years, both in national and sub nationalbasis.

On the other hand, financial markets in Indonesiaalso continue to grow in line with economic growth.While it is normal for a country to experience an uprise of unequal distribution of income at the startof their development stages, many countries suchas Japan and South Korea shows economic growthis possible to achieve with only a small increase of

Economics and Finance in Indonesia Vol. 64 No. 2, December 2018

[email protected]

[email protected]

AGINTA, H., SORAYA, D.A., & SANTOSO, W.B./FINANCIAL DEVELOPMENT AND INCOME INEQUALITY...112

inequality. Therefore, the challenge for the policy-makers is therefore to the reach the optimal socioe-conomic benefits associated with rapid economicexpansion.

To promote economic growth and inclusive finan-cial system for all, in 2012 the Government of In-donesia released the National Financial InclusionStrategy (NFIS) by putting financial sector as ananchor for economic growth and poverty reduction.In 2015, NFIS was later revised to align with Na-tional Development Plan (NDP) 2015–2019. It aimsto enhance the integration of pre-existing financialinclusion programs through 6 strategies; promotingfinancial education, public finance facility, mappingof financial information, supporting regulation, inter-mediary facility and customer protection. In 2016,the government published Presidential Decree No.82 about NFIS to support financial inclusion de-velopment in Indonesia. The main purpose of thisprogram is to provide access to financial servicesinstitutions for 75 percent of the adult population inIndonesia by the end of 2019. The program couldbe considered as a successful initiative. Accordingto Global Financial Inclusion Index (Findex) 2017released by World Bank in April 2018, Indonesia’sfinancial inclusion has made the most progress inEast Asia and the Pacific region. The report men-tions that the share of adult population with a bankaccount in Indonesia now is 49 percent, consider-ably higher than 20 percent and 36 percent in 2011and 2014 respectively. Some social programs likenon-cash food subsidy has successfully promotedlower class society to register a bank account.

To ensure the effectiveness of the program, numberof indicators are needed as a guideline to establishbenchmarks for the development of the programs;to identification barriers of the programs; and tomonitor the achievement of the programs, both innational and regional levels. Those indicators aregrouped into three types of dimension. First is ac-cessed, which is the ability to use formal financial

services. Second dimension is usage, which is theactual usage of financial services and products.Last dimension is quality, which is providing finan-cial services and products that can meet the needsof the people.

In 2016, the government also established NationalCouncil of Financial Inclusion (NCFI) to supervisethe implementation of the programs. NCFI consistof President, Vice President, Coordinating Ministerfor Economic Affairs, Bank Indonesia, Financial Ser-vices Authorities (FSA) and several other ministries.To achieve its purpose, NCFI has 7 working groupsin the financial education, community property right,inter mediation facilities and financial distributionchannels, financial services in the government sec-tor, consumer protection, policy and regulation andinfrastructure and financial information and technol-ogy.

In the view of many policymakers, there exists con-ventional wisdom about the role of financial inclu-sion: a more inclusive financial market support eco-nomic growth by providing financial aids for society –both wealthy and poor people – and thereby ensurethat capital is efficiently distributed. The logic goesas follow: easily accessible and more developedfinancial markets would pave the way for unbankedsociety to borrow and set up their businesses, in-crease income and climb the social ladder. Thisargument is arguably correct in many developingcountries, where micro credits for the poor help aless developed society to supplement their incomeafter obtaining a loan to build a business.

Despite abundant empirical findings of the benefi-cial role of financial inclusion in economic growth,the conclusion on the nexus of financial inclusionand income distribution, however, is still incipient.There have been somewhat conflicting predictionsabout the effect of financial inclusion on incomedistribution. At one end is the view that proposesan inverted-U relationship between finance and in-


AGINTA, H., SORAYA, D.A., & SANTOSO, W.B./FINANCIAL DEVELOPMENT AND INCOME INEQUALITY... 113

Figure 1: National Financial Inclusion Strategy

come inequality. While at the other end is the viewthat predicts a linear relationship.

Our study aims to go beyond the financial inclusion-growth nexus and empirically assess the link be-tween financial inclusion and the distribution of in-come in a society. Following the methodology ofSarma (2008), we constructed financial inclusionindicator for each province in Indonesia. This studyasks the following questions: 1) Does financial in-clusion always reduce income inequality in a com-munity? 2) Are there significant differences amongregions in one monetary union based on their eco-nomic structure, or is the influence the same in allareas? 3) Is the impact of financial inclusion to in-come inequality within all provinces different basedon income level? We analyze the link of financial in-clusion and income inequality using standard prox-ies in the financial inclusion literature and the Ginicoefficient of income distribution for all provinces inIndonesia.

This paper contributes to the existing literature by

(1) developing a financial inclusion index which uti-lizes available provincial data, (2) focusing on sub-national level data, and (3) understanding the linkbetween financial inclusion and income inequalityacross Indonesia. By creating our own measure offinancial inclusion based on existing methodology,we can increase our sample for all provinces aswell as utilize all available data for each province.By focusing all provinces, we cover diverse samplesranging from large growing provinces like those inJava islands to small provinces like those in easternpart of Indonesia, and consider the economic struc-ture of each province like manufacturing based tonatural resource based. Lastly, in addition to ourown financial inclusion indicator, we tested the im-portance of trade openness in lowering income in-equality across all provinces in Indonesia.

We extended the existing literature by using a moreextensive database covering a longer time horizonand more provinces. We also further controlled foryear effects and potential endogeneity problems.



Finally, we conducted various robustness checksfor our benchmark specification, including a samplesplit of the data set into sub-samples according toincome levels and economic structure.

The result shows that in all sub-samples and fullsample financial inclusion appears to lower incomeinequality and the effect is strongest in mining-oriented provinces. Other variables which are GrossRegional Domestic Product (GRDP), years ofschooling and trade-openness varies across sub-sample. In full sample and agriculture economies,GRDP has a negative impact to inequality whereasit is positive in mining sectors. Years of schoolingis not significantly increase inequality in Indonesia.However, in agriculture provinces a longer year ofschooling tend to widen inequality but in miningand manufacture economies it narrows inequality.Trade openness in all estimations appear to have apositive significant impact to inequality.

This paper is organized as follows. Section 2presents an overview of literature review and whatwe contribute to the literature. Section 3 providesthe methodology for the construction of our finan-cial inclusion indicator and data sources. Section 4discusses some stylized facts and empirical results.Section 5 highlights the key findings. Lastly, section6 summarizes and offers some policy recommen-dations.

2. Literature Review

The term financial inclusion became a trend in post-crisis 2008, mainly based on the impact of the cri-sis on the bottom of the pyramid (low income andirregular income, living in remote areas, the dis-abled, workers with no legal identity documentsand marginalized communities) which is generallyunbanked with high numbers in developing coun-tries.

At the G-20 Pittsbugh Summit 2009, the G20 mem-bers agreed on the need to improve the financialaccess for this group as highlighted at the 2010Toronto Summit, with the release of 9 Principlesfor Innovative Financial Inclusion as guidelines forthe development of inclusive finance. The princi-ples are leadership, diversity, innovation, protection,empowerment, cooperation, knowledge, proportion-ality, and framework.

Despite extensive discussions on the issue, thereis no standard definition of financial inclusion. How-ever, several institutions have proposed some defi-nitions of financial inclusion which lead to a consen-sus. The World Bank mentions that “financial inclu-sion means that individuals and businesses haveaccess to useful and affordable financial productsand services that meet their needs – transactions,payments, savings, credit and insurance – deliveredin a responsible and sustainable way”. ConsultativeGroup to Assist the Poor (CGAP) describes that“financial inclusion is state in which all working ageadults have effective access to credit, savings, pay-ments, and insurance from formal service providers.Effective access involves convenient and respon-sible service delivery, at a cost affordable to thecustomer and sustainable for the provider, with theresult that financially excluded customers use for-mal financial services rather than existing informaloptions.” Meanwhile, Financial Action Task Force(FATF) states that “financial inclusion involves pro-viding access to an adequate range of safe, con-venient and affordable financial services to disad-vantaged and other vulnerable groups, includinglow income, rural and undocumented persons, whohave been under served or excluded from the for-mal financial sector.”

Existing literature on financial inclusion also hasvarying definitions of the concept. Amidžic, Mas-sara & Mialou (2014) and Sarma (2008) directly de-fine financial inclusion. Amidžic, Massara & Mialou(2014) stated that financial inclusion is an economic



state where individuals and firms are not denied ac-cess to basic financial services. Another definitionis proposed by Sarma (2008) – and we follow thisdefinition – which views financial inclusion as a pro-cess that ensures the ease of access, availability,and usage of financial services of all members ofsociety. Unlike the definition of Amidžic, Massara& Mialou (2014), the advantage of Sarma’s (2008)definition is that it builds the concept of financialinclusion based on several dimensions, includingaccessibility availability, and usage, which can bediscussed separately.

Another issue about financial inclusion is thatthere is no standard method by which it can bemeasured. Consequently, existing studies proposevarying measures of financial inclusion. Honohan(2007,2008), for instance, constructed a financialaccess indicator for 160 economies by comparingthe fraction of adult population in a given economywith access to formal financial institutions. Whenavailable, he used household survey data on finan-cial access to construct composite financial accessindicator. For those without household survey onfinancial access, the indicator was derived usinginformation on bank account numbers and GDP percapita. The data set was constructed as a cross-section series using the most recent data as the ref-erence year, which varies across economies. How-ever, Honohan’s (2007,2008) measure providesonly a snapshot of financial inclusion and there-fore has limitation in capturing the dynamics overtime and across economies.

Amidžic, Massara & Mialou (2014) proposed thatfinancial inclusion indicator can be constructed byusing variables pertaining to three dimensions offinancial inclusion; outreach (geographic and demo-graphic penetration), usage (deposit and lending),and quality (disclosure requirement, dispute reso-lution, and cost of usage). Each measure is nor-malized, statistically identified for each dimension,and then aggregated using statistical weights to be

a composite indicator. However, a drawback fromthis approach is that it uses factor analysis methodto determine which variables are to be included foreach dimension. Therefore, it does not fully utilizeall available data for each country.

Sarma (2008), on the other hand, follows a differ-ent approach to construct the indicator. He firstcomputed a dimension index for each dimension offinancial inclusion and then aggregated each indexas the normalized inverse of Euclidean distance,where the distance is computed from a referenceideal point, and then normalized by the number ofdimensions included in the aggregate index. Theadvantage of this approach is its ease of compu-tation and it does not impose varying weights foreach dimension. For this reason, this paper closelyfollows Sarma’s (2008) approach.

Studies on income inequality have also been con-ducted intensively. As one of the most influentialscholars in this field, Kuznets (1955) has succes-fully explained income inequality phenomena in re-lation with income growth and economic develop-ment stages. Earlier studies also have found sev-eral factors that contribute significantly to incomeinequality. Among others, most studies found edu-cation to be an important factor that creates widerincome gap between the poor and the rich (Chongvi-laivan & Kim 2016; Contreras et al. 2009; De Silva &Sumarto 2013; dos Santos & da Cruz Vieira 2013;Morduch & Sicular 2002; Sapelli 2011). More re-cent study by the World Bank (2016) concludes thatthere are several main causes of income inequalityin Indonesia: (i) unequal opportunity, (ii) unequaljobs, (iii) high wealth concentration, and (iv) low re-siliency. Unequal access to education can give riseto inequality in the future since those who are lesseducated tend to engage in low-wage jobs, whichare typically in the informal sector. Differences inwealth accumulation also matters in determining ac-cess to both education and health services, whichin turn affect the potential earning of household



members in the future. Some studies, on the otherhand, find that access to finance matters in explain-ing income inequality (Wan & Zhou 2004; Bae, Han& Sohn 2012).

Previous studies have also investigated the impactof financial inclusion on income inequality. Mooker-jee & Kalipioni (2010) studied the impacts of finan-cial services availability measured by the numberof ban branches per 100,000 populations on in-come inequality. By using a sample of developedand undeveloped countries, they found that greateraccess to bank branchess strongly reduces incomeinequality accross countries. Brune et al. (2011)found that increased financial access through com-mitment saving account in rural Malawi improvesthe well-being of poor households as it providesaccess to their savings for agricultural input use.In an earlier version of his paper, Honohan (2007)tested the significance of his financial access indic-tor in reducing income equality. His results showthat higher financial access significantly reducesincome inequality as measured by the Gini coeffi-cient. However, the link between the two variablesdepends on which specification is used, i.e., whenthe access variable is included on its own and/orincludes financial depth measure, the results aresignificant, but the same does not hold when percapita income and dummy variables are included.

3. Method

3.1. Calculating Financial Inclusion In-dicator

Before testing the impact of financial inclusion toinequality, we first construct Financial Inclusion In-dicator (FII). There are two reasons of constructingour own FII. Firstly, to our knowledge, there hasbeen no study computing FII for all provinces in In-

donesia. Secondly, we need to include all provincesin our sample to avoid biases estimates and to de-velop a consistent measure of financial inclusionfor a large sample of provinces, which will be usedto standardize the measure for Indonesia. We alsolimit the scope of the calculation of the FII using in-dicators in the banking industry. Based on financialsystem statistics published by Central Bank of In-donesia, the banking industry still dominates 77.3%of the Indonesian financial system. Moreover, theavailability of data for the non-bank financial indus-try is currently limited.

In the earlier studies, several indicators have beenused individually to measure the extent of financialinclusion. The most commonly used indicator is thenumber of bank credit accounts (per 1,000 adultpersons), number of bank branches (per 1,000,000people), amount of bank credit and amount of bankdeposit. However, depends only on individual indica-tor might cause fallacy. It provides only partial infor-mation of the inclusiveness of the financial systemin an economy. Table 1 presents some indicatorsfor a selected group of provinces.

As shown in Table 1, the number of bank credit ac-counts per 1,000 adults is highest in East Kaliman-tan. However, West Papua rank first for the numberof bank branches per 1,000,000 adults. Anotherdimension is the inclusiveness of banking system,which can be estimated through the usage of thebanking system in terms of volume of credit. EastKalimantan seems to have a low credit to GDP ratioin spite a high density of bank accounts and bankbranches. On the other hand, in Bali the usage ofbanking system is high despite a moderate densityof bank branches. Based on the example of Bali,East Kalimantan, West Papua, DI Yogyakarta, andNorth Sumatera, one single indicator is inadequateto capture the whole complexity of financial inclu-sion. Therefore, a more comprehensive measureof financial inclusion is required. Preferably in onesingle number which able to incorporate information



on several aspects (dimensions) of financial inclu-sion. Such measure can be used to compare thelevels of financial inclusion across provinces withincountries at a specific time range.

In constructing FII for Indonesia, we closely followthe methodology of Sarma (2008) that is multidi-mensional. Specifically, three measures namelythe number of bank accounts (per 1,000 adult per-sons), number of bank branches (per million peo-ple), amount of bank credit to GDP ratio are in-cluded. The first measure pertains the dimensionof banking penetration, the second refers to theavailability of banking service and the third oneattributes to the dimension of usage of banking sys-tem. From this point forward, we call it dimension1, dimension 2, dimension 3 respectively. We col-lect all data from Bank Monthly Report (LaporanBulanan Bank Umum) in Bank Indonesia, regulardata publication by Indonesia Bureau of Statisticsand Financial Services Authority. We use data fromyear 2015 to 2017 to capture the dynamics overtime. Data for all provinces are downloaded, exceptNorth Kalimantan due to data availability. One bigadvantage of this method is that we can producelarge amount of observations, timely indicators andlimited costs in data collection.

After collecting three financial inclusion indicatorsmentioned above for 33 provinces, we then calcu-late the dimension index replicating the UNDP com-putation for Human Development Index (HDI) andspecification of Sarma (2008). Specifically, eachdimension index is derived as:

di =Ai −mi

Mi −mi(1)

where Ai : actual value of dimension i; mi : mini-mum value of dimension i, given by the observedminimum for dimension i; Mi : maximum value ofdimension i, given by the empirical 94th quartile fordimension i and 0 ≤ di < 1.

The index of financial inclusion for province i is thenmeasured by the normalized inverse of Euclideandistance of point di computed in Equation (1) fromthe ideal point I which is equal to 1. Specifically, theformula is given by:

IFIx = 1−a(1− d1)2 + (1− d2)2 + · · ·+ (1− dn)2

?n

(2)where the second term of the numerator in Equation(2) is the Euclidean distance from an ideal point,normalizing it by the square root of the numberof observations and subtracting it by 1, giving theinverse normalized distance. We normalized the in-dicator to make the computed values lie between 0and 1, where 1 corresponds to the highest financialinclusion index and 0 is the lowest, following Sarma(2008).

To investigate the impacts of financial inclusion onincome inequality, we incorporate other related vari-ables in the model. These variables are incomeinequality measured by Gini ratio, GRDP, years ofschooling, and trade openness. The variables aresimilar to the one used by Park & Mercado (2015).However, this paper adds trade openness variabledue to its importance in Indonesian economic struc-ture. International trade is believed to have a signifi-cant impact to income inequality in the nation.

Besides using full sample, we will also divide sam-ple into three categories based on their source ofeconomy, which are agricultural based economy,manufacture based economy, and mining basedeconomy. The reason is to analyze whether eco-nomic structure matters to income inequality. Thus,there will be three estimations of fixed effect paneldata.

3.2. Methodology

Due to large number of cross section and shorttime period, we use Fixed Effect Model Panel Data.



Table 1: Indicators of Financial Inclusion for Selected Provinces (2017)

Province No. of bank credit accounts No. of bank branches Domestic credit(per 1,000 adults) (per 1,000,000 adults) (as % of GRDP)

North Sumatera 213.87 220.20 0.29DI Yogyakarta 208.32 208.88 0.28Bali 231.30 274.59 0.33East Kalimantan 287.50 424.08 0.10West Papua 197.47 475.40 0.15

The OLS Panel data is transformed to fixed effectmodel through decomposing the disturbance terminto individual specific effect and the remainder dis-turbance left unexplained. Therefore, the equationis:

yit = α+ βxit + µi + vi (3)

The variable µi encapsulates all variables that effectyit cross sectionally that do not vary over time. Thismodel could be estimated using dummy variables,which would be termed the least squares dummyvariable approach:

yit = βxit + µiD1i + µ2D2i + µnDni + vi (4)

where D1i is a dummy variable that takes the value1 for all observations on the first entity in the sam-ple and zero otherwise, D2i is a dummy variablethat takes the value 1 for all observations on thesecond entity and zero otherwise, and so on. Theintercept α is removed to avoid “dummy trap”. In ad-dition, to avoid the necessity to estimate too manydummy variable parameters, a transformation ismade to the data to simplify matters. The transfor-mation is known as the within transformation. Thereexists a statistical method to choose between themost suitable panel data between common effectmodel, fixed effect model, and random effect model.However, observing the nature of the data and thepreliminary hypothesis, we believe the fittest modelis fixed effect (Brooks, 2014).

4. Results

4.1. Some Stylized Facts

Financial inclusion in Indonesia showed an improve-ment every year. Based on Global Financial Indexby World Bank, Indonesia’s financial inclusion in-creased from 36 percent in 2014 to 50 percent in2017. The number explains that 50 percent of adultpopulation in Indonesia already had a bank account.In the last 5–7 years, financial inclusion in Indonesia(or broader in the world) have been helped by thepenetration of digitalization. More specifically, thedevelopment of cell phone. The producers of mobilephones are now competing to create the most ad-vanced technology. The sales of mobile phone arenow appeared in small stores in a remote area ofKalimantan. It helps people to engage with internet,including financial transaction. Nowadays, mobilephone usage is not limited to calling and textingonly but also watching Youtube, interacting in Face-book, as well as shopping. Roughly, people can findanything in their cell phone. In Indonesia, number ofsmart phone users will grow from 55 million peoplein 2015 to 100 million in 2018. To catch up withthe technology, bank introduces mobile banking. Bydays, the facilities get better too thus very conve-nient for its user. The technology has broadenedfinancial sector in most part of urban area.

Although, in rural area of Indonesia people startsgetting to know internet, sometimes the networkis not well built. Therefore, the financial inclusionis heavily helped by the expansion of rural branch



of Bank. Nevertheless, the operational cost of ru-ral branch bank is not cheap such there are notmany banks willing to open it. There are few familiarnames that is seen in remote area such as Develop-ment Bank of Each Region (BPD) and Bank RakyatIndonesia (BRI). Based on Indonesia Bureau ofStatistics, there are at least 16 million poor peoplelive in the rural area compare to 7 million in urbanarea. By expanding to rural area, the banks haveopened financial access to poorest as well as farm-ers. The bank has built a connection in such therural population has a new way of financing theirdaily needs.

Unlike the ones who live in the city, people in therural area are not exposed to many information.Before bank was brought in to the rural, loan sharkis the only option to get financing for education northeir business. For a little money they applied a highinterest rate such it hurts the business instead ofdeveloping. In that way, the presence of Bank willease the circulation of money in the rural. It helpsthe poor to finance the education for their kid, tostart a business. For farmers, the borrowing from aformal institution will broaden their ability to buy abetter quality of seeds and a more advanced toolto boost productivity. The more they get financingfor their business, the more prosperous the life ofthe poor. Hence, there will be less people living ina poverty line. In that way, the inequality gap willnarrow.

Income inequality is a developing nation problem.In Indonesia, the level of income inequality (repre-sent by Gini coefficient) has varied across the rangeof 0.37–0.42 for the last 10 years. Though, thereis a tendency to decrease. Based on IMF report,other developing nations such as China and Indiaboth scores 0.53 and 0.51. The disparity becamelarge in developing nation because the engine ofgrowth centered in the city. Many companies andfactories were built in the greater area of big city.By nature, good schools, public health, and public

services will follow. Then it created massive urban-ization, leaving the rural area in worse conditionthan before.

In recent years, Indonesia has tried to encounterthe inequality problem by starting a program called“Developing Indonesia from the Rural”. One of theprogram is village fund which transfers to more than70,000 village in Indonesia using national budget.Indonesian government also focuses on buildinginfrastructures to connect area within Indonesiathrough the development of highways, bridges, na-tional sea highways, airport and port upgrading.The infrastructure projects aim to ease distributionof goods and services in every part of Indonesia.Thus, goods and services are available with afford-able budget. In the end, the policy is meant to re-duce income inequality gap. On the other hand,income inequality in Malaysia has made the coun-try slumped in “middle income trap”.

Based on Malaysia Household Income Survey2014, Gini coefficient for Malaysia reached 0.43,the same as Indonesia. However, in the same pe-riod, Malaysia’s GDP per capita is already 2,3 timeshigher than Indonesia. Yet, Malaysia still faced in-equality problem. Malaysia is caused by the inabilityof the poor population to have a high education.Nevertheless, Malaysia has moved toward a hightechnology industry, where many companies need aminimum education of bachelor’s degree or diploma.Malaysia economic transformation is faster than In-donesia. Few decades ago, Malaysia relied on oilas their source of economy. However, it shifted tomanufacture, gradually. The nation then becamethe center of factories for mainly electronics pro-ducer, as well as their call centers. Although, theeconomy started to move towards manufactures, itwas still a labor intensive – low education manufac-ture. In recent years, due to raising minimum wageand competition from China and other cheap laborcountries had made it expensive for the manufac-turers to open factories in Malaysia. Although the



transformation is beneficial for some part of popula-tion but for the poor it became harder to catch up. Inaddition, taxation system in Malaysia is still in favorof the rich. Tax for the highest income bracket is 25percent, compare to 35 percent in developed coun-tries. In order to reduce the inequality, Malaysiangovernment plan to build roads, extend electric-ity coverage, mobile clinics, and build houses forhousehold with income lower than RM2,500 in poorregion such as Sabah and Sarawak.

Table 2 presents our computed financial inclusionindicator. Several observations are noted. Unsur-prisingly, DKI Jakarta has the highest financial inclu-sion. Given its status as the capital city of Indonesiaas well as the center of financial industries, DKIJakarta has by far the most improved financial sys-tem. Interestingly, however, provinces that have sig-nificant contribution to Indonesian economies suchas West Java, Central Java, and East Java are notincluded in top one-third of the ranking table. Oneexplanation is that more than half of Indonesianpopulation currently live in Java. It made a signifi-cant impact on FII calculation because the numberof adults population and density in Java provinces isvery high. In addition, Java’s landscape is differentfrom provinces in eastern part of Indonesia where aprovince consists of several islands. Although theremight be only some small number of people live inone island, the regional development bank or otherstate-owned banks might try to open a bank branchto provide financial services in the island. Moreover,mobility rate is higher in Java. Supported by moredeveloped infrastructures, is easier for people wholive in Central Java to mobile to reach a bank thansome groups living on an island in Maluku province.This leads to lower bank services to population ratioin Java.

After calculating the FII for all provinces, we testwhich factors significantly increase or decrease fi-nancial inclusion in Indonesia. Through the plotfrom figure 2 to 5 we examine the relation between

few macro economy indicators and financial inclu-sion. Figure 5 illustrate the relation between finan-cial inclusion and inequality indeed positive, imply-ing region with higher access to financial servicehas a bigger inequality problem. This simple findingis contradictory to our preliminary hypothesis, whichthe relation is supposed to be negative. We alsoplot other indicators which may influence financialinclusion such as GRDP (Figure 4), poverty (Fig-ure 2), and years of schooling (Figure 3). GRDPand years of schooling shows a positive tendencytowards financial inclusion. It indicates that regionwith the bigger the economy and the longer aver-age students went to school has a higher financialpenetration. On the other hand, region with lowerpoverty rate tends to have better access to financialsystem.

4.2. Empirical Results

In order to answer the first research question in thispaper, we ran the regression model to test whetherfinancial inclusion helps to reduce income inequal-ity in Indonesia. Various specifications are used totest the robustness of the results and address mul-ticollinearity among the regressors. Specifications(1) solely test the relationship of financial inclusionand income inequality. While specifications (2) in-clude economic growth variable, specifications (3)add the role of education. Finally, specifications (4)include all regressors.

Table 3 shows the result using full sample of 33provinces in Indonesia. FII has a positive significantimpact to income inequality, in which the oppositefrom our expectation. A better financial access issupposed to help narrowing inequality. However, asmore variables are added the sign change into neg-ative signing yet not significant It indicates that con-sidering other indicators, FII able to lower inequalitythough it remains insignificant with a relatively low



Table 2: Financial Inclusion Index of All Provinces

Province FII RankDKI Jakarta 0.99 1North Sulawesi 0.84 2Bali 0.81 3South Sulawesi 0.69 4North Sumatera 0.65 5DI Yogyakarta 0.61 6Maluku 0.61 7Central Kalimantan 0.61 8West Sumatera 0.57 9Banten 0.57 10Bengkulu 0.57 11North Maluku 0.56 12Central Sulawesi 0.54 13West Java 0.52 14West Papua 0.51 15South Kalimantan 0.51 16Gorontalo 0.48 17East Java 0.47 18East Kalimantan 0.47 19Jambi 0.46 20Central Java 0.46 21Aceh 0.46 22Papua 0.43 23South East Sulawesi 0.42 24West Nusa Tenggara 0.39 25West Sulawesi 0.3 26Riau Islands 0.29 27South Sumatera 0.28 28Riau 0.26 29West Kalimantan 0.26 30Bangka Belitung Islands 0.25 31East Nusa Tenggara 0.24 32Lampung 0.15 33

Figure 2: Financial Inclusion Index and Poverty



Figure 3: Financial Inclusion and Years of Schooling

Figure 4: Financial Inclusion Index and GRDP

coefficient value. This implies that the success offinancial inclusion depends on financial educationreceived by communities. Moreover, financial in-clusion cannot be done in one year or two. It is acountry’s long-term investment and Indonesia juststarted to realize the importance of financial inclu-sion in recent years. Other indicators such as GRDP,years of schooling and trade openness are addedto the estimation to provide a more robust model. It

shows that across specification, a higher GRDP willlower inequality. In the case of Indonesia, a higherGRDP apparently able to lift people’s quality of lifethrough a more balance wealth distribution, thus itable to narrow inequality. Another indicator is yearsof schooling. The longer a person attend school, themore chance of higher income later. However, theestimation result finds that a longer year of school-ing only increase inequality. At this moment, through



Figure 5: Financial Inclusion and Inequlity

an expansion of technology some groups of peopleable to reach education up to doctorate level morethan it used to. However, some remains struggle totouch university level. In 2015 Statistics Indonesia(BPS) stated that there are 121 public universitiescompare to 3,104 private universities in Indonesia.Nevertheless, private universities do not receivegovernment funding like the public universities doso the tuition fee is higher. Also, good universitiesconcentrated in urban area. By the distribution ofpublic and private universities and the location, itdemonstrates an inequality within Indonesian edu-cation system. Later, it creates income inequality.Last variable to be added into the model is tradeopenness. The export-led growth hypothesis em-phasizes that export is main engine of growth bothin developing or industrialized countries. However,Cobb-Douglass Function explains that labor is oneof production variables. Therefore, trade opennessis supposed to have a significant impact to output(production) and labor (Medina-Smith 2001). Laterthere will be more people who can afford to livebetter and tightening inequality gap. The estimationoutput shows the reverse. A higher exposure to ex-

port will widening inequality. Those labor-intensiveindustries heavily employ low skill workers so whilethey are expanding the needs of high skill workerstay the same. Rather than helping to reduce, thesituation has enlarged the inequality in Indonesia.

With regards to the second research question, wedivide Indonesian provinces into three categories,which are agriculture, manufacture, and miningbased economy. Out of 34 provinces currently, thispaper excludes North Kalimantan. In addition, DKIJakarta and Bali‘s largest sector of the economydo not fall in those 3 categories; thus these twoprovinces are excluded in sectoral estimation aswell. List of provinces based on their dominant sec-tors are written in the Table 4.

The estimation result for agricultural-based econ-omy is shown in Table 5. This sub-sample showsthat a greater financial inclusion will cause the in-equality to widen though in specification 4 the im-pact turns to negative, yet insignificant. Majority ofIndonesian farmers (to the extent of workers in palmoil, rubber, etc.) live in the village or rural area. Thepressure to have knowledge of financial systemis much less than in the city because the finan-



Table 3: Regression Results on Income Inequality, Full Sampel Indonesia (33 Provinces)

Variable (1) (2) (3) (4)c -1.005891* 0.817626* 0.589856* 0.926245*log(fii) 0.01497* -0.006894 -0.007301 -0.004451log(grdp) -0.151308* -0.153385* -0.171574*log(edu) 0.124734* 0.044391log(to) 0.022237*R2 0.978407 0.983479 0.987656 0.981659N 99 99 99 99

Table 4: List of Province Based on the Dominant Sector of the Economy

Agriculture-Based Provinces Manufacture-Based Provinces Mining-Based ProvincesDI Aceh West Java RiauNorth Sumatera Banten South SumateraWest Sumatera Central Java South KalimantanJambi East Java East KalimantanBengkulu DI Yoyakarta PapuaLampung West PapuaWest Nusa Tenggara Riau IslandsEast Nusa Tenggara Bangka Belitung IslandsWest KalimantanCentral KalimantanWest SulawesiSouth SulawesiSouth East SulawesiCentral SulawesiGorontaloNorth SulawesiMalukuNorth Maluku

cial service is not provided as developed as in theurban area. Up to this point, the inclusion of Indone-sian financial service in the agricultural dominatedeconomy only benefit the high income (in this casecorporation) because it does not well receive by theworkers/labors. On the other hand, GRDP shows asignificant impact to inequality. An increase in thesize of the economy will tighten inequality througha progressive taxation. As for the years of school-ing, it shows a positive and significant impact toenlarge income inequality. The fact that there stillexists a paradigm about no need for farmers to at-tain a good and longer school years. The studentswhose parents are farmers are not encouraged toexperience high education because they will con-tinue the legacy of being farmer, which does notrequire a high education. In fact, because there aretoo many of the students has a vision to become“normal” farmers, the one who achieve a higher ed-

ucation will well distinct from other. The one withhigh education then able to get into big plantationcompanies in which pay better. Later, it will createa bigger inequality. As for trade openness, it has aninsignificant impact to inequality. Variabel

Table 6 shows the estimation result for manufacturedominated provinces. At first it shows a positivesignificant impact of financial inclusion to inequality.However, after adding more regressor the resultdemonstrates the opposite. The more regressors inthe model, the higher impact of financial inclusionable to reduce inequality. The factories or officeswhich manufacture’s workers work usually locatedin the sub-urban area. In that way, everyone has thesame access to financial service and actually ableto experience the service itself. Manufacture sectoris also considered to be better developed than agri-culture sector. Also, it uses more advanced technol-ogy, so the workers are more familiar to computers



Table 5: Regression Results on Income Inequality, Agriculture based Provinces

Variabel (1) (2) (3) (4)c -1.008024* -0.228459 -0.316175 0.276674log(fii) 0.042682* 0.019543** 0.015041 -0.005993log(grdp) -0.068542* -0.229443* -0.279336*log(edu) 0.941887* 0.91603*log(to) 0.014213R2 0.987047 0.986889 0.986942 0.967434N 54 54 54 54

and machine. Mostly, the workers’ earning is re-ceived through bank. As a result, workers are usedto technology and by living close to the cities they re-ceive more information about financial services. So,a further development of financial service will helpthe low-middle income workers to live better off byhaving access to financing their education, houses,etc. Meanwhile the middle-high income workers willhave better funding for their second home or cars.Although both low-middle and middle-high incomereceive benefit through financial inclusion, but theformer is by far more affected. Similar to finding offull sample Indonesia, the bigger size of GRDP isalso helping to distribute wealth more equal sincethe result shows a negative sign. Although, in thespecification 4 the sign turns into positive, but it isnot significant. Adding years of schooling into themodels, it has been found that the longer years ofschooling has a negative impact to inequality. Inother words, the longer a person stay in school themore he will have power to increase his income andcreate a more equal society. Manufacture companytends to be big (at least the one who employ lotsof labors). Since the size of their operation is large,they are monitored by the government closely. InIndonesia, association for labors (manufacture) haspower to deliver their wills. Companies are care-ful enough to put workers based on their level ofeducation and experience. There will be a specificdescription to job entitle. For instance, a personwith vocational degree will not be places as worker,rather he would be a supervisor. Education in man-ufacture sector then determine the level of earning.

The more workers with good education backgroundthe society will be less unequal. Like the earlier es-timation, we also add trade openness to measurethe impact of export to inequality. In manufacture-oriented economy, a bigger exposure to export willcause an economy to more unequal. Exporter com-panies are usually the biggest of all. The smallerones are probably struggling to enter the exportmarket due to its economies of scale.

Table 7 demonstrates the estimation result of min-ing dominated provinces. In the mining economies,financial inclusion will able to reduce inequality. Abig coverage of financial sector will help the low-income bracket to access financing. Also, incomein mining sector is comparably higher than in agri-culture and manufacture. Although workers mightbe considered low income in mining but could bemedium income in another sector. Mostly, most ofmining site located in remote area thus the highwage is considered a compensation. Due to thenature of mining sector, it does not employ workersas others though the contribution to regional econ-omy is large. Regardless their location, it is easierto spread financial service to the ones working inmining sector because there are less of them. In ad-dition, working in the remote area made them needsa mechanism in which able to send money to theirfamilies back home. Thus, there is a need of finan-cial services especially banking. Another variablewe add to the model is GRDP. In the case of mining-oriented economy, the result is different than earlierestimation. A higher GRDP leads to a higher in-come inequality. A bigger production in which cause



Table 6: Regression Results on Income Inequality Manufacture based Provinces

Variabel (1) (2) (3) (4)c -0.92043* 3.858468* 3.865548* 3.137375*log(fii) 0.092265* -0.072088* -0.07345* -0.104865*log(grdp) -0.382991* -0.267543** 0.005839log(edu) -0.71685 -2.157726**log(to) 0.06926**R2 0.977593 0.997763 0.996061 0.988684N 24 24 24 24

mining sector to increase, highly depends on theirmachine and technology. It does not reduce inequal-ity because to some extent, a production boom willcause to adding more machines and not humancapitals. Also, there exist a production bonus in min-ing companies. As the companies receiving morerevenue through sales, bonus will be given but theschemes are most likely to be progressive thuscreating inequality. Education in this model is repre-sented by the years of schooling. Technology usedin mining sector is also advanced and complicatedtherefore they need skill. By attending school longer,they workers will be more skilled and enlarge theirchance to get higher earnings. By observing the co-efficient of the regression, we conclude that yearsof school in mining provinces has bigger impactto reduce inequality than in manufacture-orientedeconomies. Trade openness also has a bigger im-pact to widen inequality than in manufacture noragriculture.

In order to answer the third question, We also run 2regressions using the same model. However, thistime we divided the sample based on their quantileincome level (GRDP per capita). There are 4 cate-gories which are high income, upper middle income,lower middle income, and low income. The list is asfollow on Table 8.

Firstly, we ran a regression with FII as a single inde-pendent variable. We found that financial inclusiongives a significant impact to income inequality. Inmost areas, a higher access to financial systemlead to a higher inequality. It gives an early indi-cation that easier financing is more beneficial to

the well being than the poorer. It might be the casethat credit is distributed more to a medium-big localfirms than to small medium enterprises, local farm-ers, and others low-wage workers. Nevertheless,in low income areas the impact is different. Higherfinancial inclusion is able to give the poorer oneto get financing. Thus, they can use the loan as aworking capital and lift their welfare.

Secondly, we also add other regressors into equa-tion which are GRDP, years of schooling, and tradeopenness. The estimation result shows that theimpact of financial inclusion is positive yet not sig-nificant in high income provinces. In this area, asignificant factor to reduce income inequality is agreater economy. A larger economy is able to cre-ate a bigger job opportunity, thus able to give theunemployed jobs.

In upper middle income provinces, a wider finan-cial inclusion is significantly caused a higher in-come disparity whereas in lower middle incomeprovinces, the effect remains insignificant. In lowincome provinces, a wider financial access for thecommunities along with bigger economy will resulta lower income inequality.

Regarding the effect of financial inclusion to re-duce income inequality we need to acknowledgethat banks are Indonesia’s financial system biggestplayer. However, bank is a highly regulated financialcorporation. Therefore, they are selective in termsof approving loan. All measurement such as thefinancial history of their lenders, income, and collat-eral are all taken into account. Most of the time a



Table 7: Regression Results on Income Inequality Mining based Provinces

Variabel (1) (2) (3) (4)c -1.061513* 0.145433 -0.853554 -0.135256log(fii) -0.019588* -0.009277 -0.000208 -0.031434**log(pdrb) -0.094285 0.821925* 0.766175*log(edu) -5.147866* -5.388363*log(to) 0.139294*R2 0.885684 0.893158 0.940198 0.97532N 15 15 15 15

Table 8: Indonesia Province Rank Based on GRDP per Capita

High Income Upper Middle Income Lower Middle Income Low IncomeDKI Jakarta Bali Aceh BengkuluJambi Banten Jawa Barat DI YogyakartaJawa Timur Kalimantan Tengah Jawa Tengah GorontaloKalimantan Timur Sulawesi Selatan Kalimantan Barat MalukuKepulauan Bangka Belitung Sulawesi Tenggara Kalimantan Selatan Maluku UtaraKepulauan Riau Sulawesi Utara Lampung Nusa Tenggara BaratPapua Sumatera Selatan Sulawesi Tengah Nusa Tenggara TimurPapua Barat Sumatera Utara Sumatera Barat Sulawesi Barat

Table 9: Regression Result on Income Inequality and Financial Inclusion Index

Variabel High Income Upper Middle Income Lower Middle Income Low Incomec -0.985206* -0.96755* -0.975789 -1.062671*log(fii) 0.04099* 0.052286* 0.082913* -0.073195*R2 0.991041 0.964717 0.99632 0.98778N 27 24 24 24

wealthier one has a better income as well as morecollateral. The problem arises in high income areais that bank has options to choose between givingloans to the wealthy or poor. Considering the riskfor the poor has a higher credit risk than the wealth-ier one, logically more loans are provided for thewealthy one. However for the low income are, thepool of lenders is dominated by the less wealthy.Meaning, most of them might have a high credit risk,giving banks less option. It supports the argumentthat in low income area, higher financial inclusionleads to income inequality reduction.

5. Conclusion

It is widely believed that financial inclusion aids in-clusive growth and reducing inequality. More specif-ically, it expands poor people’s access to financialservices, increasing their economic opportunities

and improving their lives. Recognizing the positiveimpact of financial inclusion on inclusive growth aswell as poverty reduction, Indonesian governmentin 2012 released the National Financial InclusionStrategy (NFIS).

This paper contributes in constructing Financial In-clusion Index for each province in Indonesia fortime period of 2015–2017. We find that provinceswhich shows a high financial inclusion is the onewith urban area such as DKI Jakarta, North Su-lawesi (Manado), Bali (Denpasar), and South Su-lawesi (Makassar). Some big economies namelyWest Java and East Java does not appear at highrank due to massive number of adult population. Inaddition, geographical landscape play an importantrole in terms of spreading financial service.

Though we find robust evidence that provinces withhigh financial inclusion have lower inequality, an-swer to the question whether financial inclusion re-



Table 10: Regression Result on Income Inequality, Based on Income Level

Variabel High Income Upper Middle Income Lower Middle Income Low Incomec 6.227211* -0.97964 1.450.356 1.842811*log(fii) 1.05E-05 0.04744* 0.01068 -0.142511*log(pdrb) -0.589045* 0.047406 0.112056 -0.298052*log(edu) -0.02957 -0.32478 -186.751 0.083351log(to) 0.036375* 0.051693* -0.00051 0.030736*R2 0.988934 0.979925 0.97996 0.986824N 27 24 24 24

ally helps to reduce income inequality depends onother supporting factors. Our study suggests thatfinancial inclusion alone is hardly having an impacton reducing income inequality. Rather, the spreadof financial inclusion in Indonesia will have a chanceto lower inequality if other supporting developmentsuch as education, infrastructure, and governmentproject are in place.

Furthermore, the validity of the results seems to de-pend on the main economic sector of each province.The estimation using full samples of 33 provincesprovide information that the power of financial inclu-sion in Indonesia has not show a strong impact toreduce inequality. Out of 4 independent variables,GRDP is acknowledged to be the variable whichcould decrease inequality. Trade openness seemedto have opposite effect, in which a bigger exportleads to higher inequality. Surprisingly, years ofschooling does not have significant effect to reduceIndonesian inequality.

The result is slightly different in agriculture basedeconomies sub-sample. Though financial inclusionremains insignificant in the last specification butin specification 1 to 3 the effect is positive. MostIndonesian farmers live in rural area which becamea constrain for financial services. On the other hand,longer years of schooling tend to increase inequality,which the opposite from our preliminary hypothesis.

As for manufacture-based economies, financial in-clusion has a strong impact to reduce inequality.Manufacture sector usually concentrated in sub ur-ban area which make it easier for the expansion of

financial inclusion. Also, most of the players in thissector is big corporation that apply a more modernsystem of wage payment. The number of labors ismassive in such make it impossible to pay themmanually, thus banking system is applied. GRDPno longer significant. On the other hand, the impactof years of schooling to inequality is different fromthe two earlier estimations. In manufacture-basedprovinces, a longer year of schooling has powerto reduce income inequality because each job de-mands a specific educational background unlike inthe agriculture sector. Another variable, trade open-ness has a positive impact to increase inequality.

Other economies, which is mining basedeconomies has a negative impact of finan-cial inclusion to income inequality. The number ofworkers in this sector is relatively small, thus it iseasier to spread financial service. Differently, ahigher GRDP in these provinces cause inequalityto widen because the industry itself is capitalintensive. As for years of schooling and tradeopenness the effect is similar to estimation result ofmanufacture based provinces.

The results suggest that financial inclusion onlyhelps to lower income inequality when overall eco-nomic conditions empower people to use access tofinance for productive purposes such as expandinga business or investing in education. Such a rela-tionship is much more reliable in both manufactureand mining-based provinces which have relativelyhigher income where better regulatory conditionsprovide an enabling environment for a range of de-velopment outcomes.



More focused programs implemented by NCFI inlow-income regions could make financial inclusionto be more effective to help reducing income in-equality in Indonesia. Firstly, NCFI needs to con-tinuously educate and promote women as wellas young generation to engage with financial sys-tem, especially the one in lower income area. Sec-ondly, to expand financial inclusion in agricultureeconomies, NCFI along with local government andlocal banks have to build attractive products or lend-ing schemes that support trading transaction offarmers.

Annex

This paper has made some adjustment to the FII. Itis not 100% replicating computation done by earlierSarma (2008) in terms of the indicators. As for thebanking penetration (dimension 1), this paper usesthe same indicators which are a number of bankaccounts. More precisely, a number of credit bankaccount/1,000 adults. The dimension 2, availabilityof banking services is rather a bit different becausethe only measure being used is a number of bankbranches/1,000 population. A number of ATM/1,000population is not used because, in the case of In-donesia, bank branches have more influence inthe rural area. In some parts of Indonesia, thereis some area which electricity is not available for24 hours. In this circumstances, ATM is not conve-nient, so bank branch is preferable. Another thing isthat some people who live in the rural area are notused to the banking system. The year 2017 couldbe the first time they get accessed to the financialsector. Therefore, the help from customer serviceis needed and by doing a face to face interactionthe customer’s trust grow. ATM does not have thisammenities because it is a machine. The third di-mension is usage which is proxied by credit/GRDP.

References

[1] Amidžic, G, Massara, A, & Mialou, A 2014. As-sessing countries’ financial inclusion standing—A new composite index. IMF Working Pa-per WP/14/36. International Monetary Fund.https://www.imf.org/external/pubs/ft/wp/2014/wp1436.pdf.

[2] Bae, K, Han, D, & Sohn, H 2012. Importance of accessto finance in reducing income inequality and poverty level.International Review of Public Administration, 17(1), pp.55-77. doi: https://doi.org/10.1080/12264431.2012.10805217.

[3] Brooks, C 2014. Introductory econometrics for finance.Cambridge university press.

[4] Brune, L, Giné, X, Goldberg, J, & Yang, D 2011.Commitments to Save: A Field Experiment in RuralMalawi. Policy Research Working Paper No. WPS5748; Impact Evaluation Series No. IE 50. Financeand Private Sector Development Team, DevelopmentResearch Group, World Bank. Washington, DC. doi:https://doi.org/10.1596/1813-9450-5748. http://documents.worldbank.org/curated/en/881911468271793505/Commitments-to-save-a-field-experiment-in-rural-Malawi.

[5] Chongvilaivan, A & Kim, J 2016. Individual income inequal-ity and its drivers in Indonesia: A Theil decomposition re-assessment. Social Indicators Research, 126(1), pp.79-98.doi: https://doi.org/10.1007/s11205-015-0890-0.

[6] Contreras, D, Larrañaga, O, Puentes, E, & Rau, T 2009.Evidence for inequality of Opportunities: A cohort analysisfor Chile. Serie Documentos de Trabajo SDT 298. Depar-tamento de Economía Facultad Economía Y NegociosUniversidad de Chile. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.607.3022&rep=rep1&type=pdf.

[7] De Silva, I & Sumarto, S 2013. Poverty-growth-inequalitytriangle: The case of Indonesia. TNP2K Working Pa-per 04 - 2013. Tim Nasional Percepatan Penanggulan-gan Kemiskinan. http://www.tnp2k.go.id/images/uploads/downloads/WP4-PovertyTriangle(1).pdf.

[8] dos Santos, VF & da Cruz Vieira, W 2013. Effects ofgrowth and reduction of income inequality on povertyin Northeastern Brazil, 2003-2008. Economia Aplicada,17(4), pp.647-666. doi: http://dx.doi.org/10.1590/S1413-80502013000400006.

[9] Honohan, P 2007. Cross-country variation in householdaccess to financial services. Prepared for the WorldBank Conference “Access to Finance”, Washington DC,March 15-16, 2007. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.170.9339&rep=rep1&type=pdf.

[10] Honohan, P 2008. Cross-country variation inhousehold access to financial services. Journalof Banking & Finance, 32(11), pp.2493-2500. doi:https://doi.org/10.1016/j.jbankfin.2008.05.004.

[11] Kuznets, S 1955. Economic growth and income inequality.


http://documents.worldbank.org/curated/en/881911468271793505/Commitments-to-save-a-field-experiment-in-rural-Malawi



http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.607.3022&rep=rep1&type=pdf


http://www.tnp2k.go.id/images/uploads/downloads/WP4-PovertyTriangle(1).pdf

http://www.tnp2k.go.id/images/uploads/downloads/WP4-PovertyTriangle(1).pdf




The American Economic Review, 45(1), pp.1-28.[12] Medina-Smith, EJ 2001. Is the export-led growth hy-

pothesis valid for developing countries? A case studyof Costa Rica. Policy Issues in International Trade andCommodities Study Series No. 7 (UNCTAD/ITCD/TAB/8).United Nations Conference on Trade and Development.https://unctad.org/en/docs/itcdtab8_en.pdf.

[13] Mookerjee, R & Kalipioni, P 2010. Availability of financialservices and income inequality: The evidence from manycountries. Emerging Markets Review, 11(4), pp.404-408.

[14] Morduch, J & Sicular, T 2002. Rethinking inequal-ity decomposition, with evidence from rural China.The Economic Journal, 112(476), pp.93-106. doi:https://doi.org/10.1111/1468-0297.0j674.

[15] Park, C & Mercado, RV 2015. Financial Inclusion, Poverty,and Income Inequality in Developing Asia. ADB Eco-nomics Working Paper Series No. 426. Asian DevelopmentBank. https://www.adb.org/publications/financial-inclusion-poverty-and-income-inequality-developing-asia

[16] Sapelli, C 2011. A cohort analysis of the income distributionin Chile. Estudios de economía, 38(1), pp.223-242. doi:http://dx.doi.org/10.4067/S0718-52862011000100009.

[17] Sarma, M 2008. Index of Financial Inclusion.ICRIER Working Papers No. 215. Indian Councilfor Research on International Economic Relations.http://www.icrier.org/pdf/Working_Paper_215.pdf.

[18] Wan, G & Zhou, Z 2004. Income Inequality in RuralChina: Regression-based Decomposition Using House-hold Data. Research Paper 2004/051. United Nations Uni-versity World Institute for Development Economics Re-search. Helsinki: UNU-WIDER. https://www.wider.unu.edu/publication/income-inequality-rural-china.


https://www.wider.unu.edu/publication/income-inequality-rural-china

https://www.wider.unu.edu/publication/income-inequality-rural-china

financial development and income inequality in indonesia

Documents