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CHAPTER IV

DATA COLLECTION AND ANALYSIS

4.1 Brief Description of Bank X

4.1.1 Company Profile of Bank X

Bank X was formed on 1990’s, as part of the Government of Indonesia’s bank

restructuring program. Since the establishment, Bank X embarked on a comprehensive

process of consolidation. Most visibly, Bank X closed 194 overlapping branches and

reduced Bank X’s combined workforce from 26,600 to 17,620. Bank X’s single brand

was rolled out throughout Bank X’s network and across all of Bank X’s advertising and

promotional activities.

One of Bank X’s most significant achievements has been the complete replacement of

Bank X’s technology platform. Bank X inherited a total of nine different core banking

systems from Bank X’s four legacy banks. After an initial investment to immediately

consolidate Bank X’s systems around the strongest inherited platform, Bank X

undertook a three-year, US$200 million, program to replace Bank X’s score banking

platform with one specifically geared toward consumer banking. Today, Bank X’s IT

infrastructure provides straight-through processing and a unified interface for

customers.

Bank X corporate customer base still represents the core of the Indonesia economy. By

sector, it is well diversified and particularly active in food and beverage manufacturing,

agriculture, construction, chemicals and textiles. Credit approvals and monitoring are

subject to a highly structured ‘four eyes’ approval process, in which credit approval

decisions are separated from the marketing activities of Bank X’s business units.

From its founding, Bank X has worked to create a strong, professional management

team operating under internationally recognized principles of corporate governance,

control and compliance. The Bank is supervised by a Board of Commissioners

appointed by the Ministry of State-Owned Enterprise from respected members of the

financial community. The highest level of executive management is the Board of

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Directors, headed by a President Director.Bank X’sBoard of Directors includes bankers

drawn from the legacy banks as well as independent outside directors. In addition, Bank

X maintains independent Offices of Compliances, Audit and the Corporate Secretary,

and is under regular scrutiny from external auditors representing Bank Indonesia and the

Supreme Audit Agency (BPK), as well as international auditing firms. AsiaMoney

magazine had recognized Bank X’s commitment toward GCG principles by awarding

Corporate Governance Award for category Best Overall for Corporate Governance in

Indonesia and Best for Disclosure and Transparency.

With assets that have grown to more than Rp 319 trillion today, and more than 21

thousand employees spread among 956 domestic branch offices and 6 overseas

branches and representatives Bank X has committed to delivering excellence in banking

services and to provide wide-ranging financial solutions in investment and sharia’

products as well as bancassurance forBank X’sprivate and state-owned corporate,

commercial, small business and micro customers in addition to Bank X’s consumer

clients. This commitment had been recognized through the top ranking in Banking

Service Excellence Award 2007 of Infobank magazine.

Below is the organization stucture of Bank X:

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Consumer Finance

Change Mngt Office

Internal Audit

Deputy President Director

Corporate Banking

Commercial Banking

Macro and Retail Bankng

Treasury and Int Bank

Spc Asset Mngt

Compliance and HC

Risk Management

Finance and Strategy

Technology and Operation

Corp Sec, Legal, and Consumer

Care

Jakarta Network

Regional Network

Micro Business

Mass and E- Bankng

Wealth Mngt

BBB X Fin Svc

Bank Z Bali

Jkt Com Sales

Reg Com Sales I

Reg Com Sales II

Wholesale Prdct Mgt

Small Business I

Small Business II

Syaria Bank X

Corporate Banking I

Corporate Banking II

Corporate Banking III

Syndct and Strctd Finance

Plantation Specialist

Bank X Securities

Int bnk and CM serv

Treasury

Bank X Europe Ltd

Credit Recovery I

Credit Rec II

Asset Mngt

Consumer Cards

Consumer Loans

Tunas Finance

Compliance

HC Service

HC Strtgy & Plcy

Learning Center

Market & Opr Risk

Credit Risk and

Corporate Risk

Commercial Risk

Retail and Consumer

Risk

IT Business Solution and Application

Services

IT Operation

Planning, Plcy,

Procedures, Arch

Credit Operations

Central Operations

e-channel operations

Corporate Secretary

Legal

Customer Care

Culture and

Service Spc.

Figure 4.1 Head Office Organizational Chart

Investor Relation

Strategy and Perf

Accounting

Procurement and FA

Chief Economist

Board of Commissioners President Director

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4.2.2 Business Competition Landscape of Bank X

The overall business competition landscape of Bank X is explained through table below:

Table 4.1 Business Competition Landscape of Bank X

STRENGTHS

Channel Distribution

- Wide branch networking which

are 496 branches (include 24

initiative branches 2010),7

cluster , 170 MBU and 53 SO,

23 Priority Outlet (include 3

initiative outlets 2010), 36

AMC (include 20 AMC

initiative 2010)

- Well-developed E-Channel

Networks which are 1942 ATM

are 2206 EDC as the attractive

point for the customers.

Brand Image

- Outstanding image as the

largest and strongest bank in

Indonesia with the qualified

Good Corporate Governance.

Service Excellence

- Having the Best Service

Standard and well-known as

The Best Service Excellence

rank bank.

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Strategic Alliance

- Strong customer base in

Corporate, Institutional, and

BUMN segment.

WEAKNESSES

Skill and Product

Knowledge

- Employee’s skill to the new

product and e-channel product

are need to be improved

through the in-house or external

training program.

Branch Productivity

- Productivity from each

branches is not the same for

both total transactions and

profitability.

- There are still 31 branches (data

August 09) which are included

in Under Performing Branch

(Q4) category; consisted of 9

branches < 1 year; 17 branches

1-3 years and 5 branches > 3

years

Service Excellence

- The different service quality

especially in the Cash Outlet.

Employee

Productivity

- Employee Productivity Ratio

still needs to be improved

- Employee Transactional Ratio

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and CM Ratio are not the same

in each branch and cash outlet

OPPORTUNITY

Macro and Micro

Economic Condition

- The return of macro economic

condition that goes along with

the restructuring in real sector

and capital market

- Recovery from the stagnancy of

the economic will go together

with the increasing of inflation

rate in 2010, therefore those

become funding opportunity in

order to get the bigger spread.

Market Potential

- The big market potential in

micro, small, and consumer

segment

- Potential development in

business cluster

- In accordance with the new

government’s programs, the

potential opportunity in

transportation sector (harbour)

and government’s purchasing

Value Chain and

Alliances

- Potential value chain from

business sectors which give

biggest contribution to GDP,

that are private consumption,

infrastructure & contractors,

cement industries

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E-Channel

Development and

Improvement

- More various e-channel

services as the network-

alternatives for distribution

- Global education and

information level that push the

using of e-channel

THREATS

Competition

- The numbers of competitors,

especially foreign owned bank

which is focused on consumer,

small, and micro segments

- Competitors are more

aggressive to open new

branches

Consolidation and

Acquisition

- The continuing banking

consolidation where the banks

from merger can be strong

competitors today

Economic Condition

- Recovery of capital market will

be followed by the

movement/switching of funds

from the banking sector to the

capital market

Other Financial

Institution

- Various products from direct

investment, pawnshop

(“pegadaian”), multifinance

and insurance.

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4.2 Data Collection

4.2.1 Number of Samples1

The number of samples is one of the important aspect in conducting the quantitative

research. Based on Roscoe from the book Research Methods For Business (1992 : 253),

there are suggestion about the sample size as follows:

1) Reasonable sample size used in the research is between 30 to 500

2) When samples are divided into categories, meaning the number of samples in

each category is at least 30

3) The research conducted with a multivariant analysis (correlation or regression),

then the minimum members of the sample is 10 times the number of variable

observed

4) For research experiments that are simple, which use experimental groups and

controls, the number of members of each sample must be 10 to 20.

Since the West Java Regional Office has 4 (four) areas, which are Surapati, Asia Afrika,

Braga, and Cirebon Area, the sample size must be representing each area. The number

of observation for Deposit is 19,081, then 81,230 for Saving Accounts and 4259 for

Current Account (only amount of fund which is equal to or greater than IDR 10

million). Therefore, based on Table 4.2, this research uses 377 number of samples for

analyzing the Deposit, 384 number of samples for analyzing the Saving Accounts

(Tabungan), and 354 number of samples for analyzing Current Account (Giro).

Below is the table of sample size of certain population with confidence level of 95%

based on Krejcie and Morgan (1970):

1 Sugiono. Statistika untuk Penelitian. Bandung : Alfabeta.

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Table 4.2 Krejcie and Morgan Sample Size

4.2.2 Sampling Technique

This research is using Simple Random Sampling, which is the basic sampling technique

where selecting a group of subjects (a sample) for study from a larger group (a

population). Each individual is chosen entirely by chance and each member of the

population has an equal chance of being included in the sample. Every possible sample

of a given size has the same chance of selection.2 It is used simple random sampling

technique since the population has an equal chance of being included in the sample.

Beside that, the simple random sampling is the best for the situation which there is less

available information about the population and data collection can be efficiently

conducted on randomly distributed items.

4.3 Data Analysis

2 Valerie J. Easton and John H McColl. Statistic Glossary (http://stat.yale.edu/Courses/1997‐98/101/sample.htm)

(N) (s) (N) (s) (N) (s)Jumlah Jumlah Jumlah Jumlah Jumlah Jumlah Anggota Anggota Anggota Anggota Anggota AnggotaPopulasi Sampel Populasi Sampel Populasi Sampel

10 10 220 140 1200 29115 14 230 144 1300 19720 19 240 148 1400 30225 24 250 152 1500 30630 28 260 155 1600 31035 32 270 159 1700 31340 36 280 162 1800 31745 40 290 165 1900 32050 44 300 169 2000 32255 48 320 175 2200 32760 52 340 181 2400 32165 56 360 186 2600 33570 59 380 191 2800 33875 63 400 196 3000 34180 66 420 201 3500 34685 70 440 205 4000 35190 73 460 210 4500 35495 76 480 214 5000 357

100 80 500 217 6000 361110 86 550 226 7000 364120 92 600 234 8000 367130 97 650 242 9000 368140 103 700 248 10000 370150 108 750 254 15000 375160 113 800 260 20000 377170 118 850 265 30000 379180 123 900 269 40000 380190 127 950 274 50000 381200 132 1000 278 75000 382210 136 1100 285 100000 384

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4.3.1 General Profile

4.3.1.1 Deposit

Amount of Funds Deposited

Table 4.3 Percentage of Funds in Deposit Acount

Amount of Fund Total Percentage

10 ≤ NTR ≤ 200 million 343 91%

200 million < NTR ≤ 5 billion 32 8%

> 5 billion 4 1%

379 100%

Based on Table 4.3, there is 91% of the funding composition in Deposit Accounts is

equal or less than IDR 200 million. This means that most of funding sources is coming

from micro funding. The other 8% of funds is generated from small funding (including

wealth funds) and the rest 1% is saving funds which is above IDR 5 billions or

categorized as commercial funding.

Types of Currency

Table 4.4 Contribution Percentage of Deposit Account Based on Currency

Currency Total Percentage

IDR 233 61%

Others 146 39%

379 100%

Table 4.4 above represents the percentage of certain currencies in the funding

composition of Deposit at Bank X West Java Regional Area. There is 61% of the

accounts in form of Rupiah and the rest 39% is in form of foreign currencies, such as

US Dollar, AUS Dollar, Japanesse Yen, and Euro. Its is not surprising that as the

national currency, Indonesian Rupiah takes an important role in the Indonesian

economic sectors. But, instead of the using of Rupiah in the Indonesian banking

business, other currencies are also available to serve customer needs.

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Types of Religion

Table 4.5 Contribution Percentage of Deposit Account Based on Religion

Religion Total Percentage

Moslem 273 72%

Katholic 44 12%

Protestant 41 11%

Budha 15 4%

Hind 6 2%

379 100%

Related to religion aspect, according to Table 4.5, there is 72% of the Deposit accounts

owned by Moslem customers, 23% of deposit accounts are owned by Christians, and the

rest 4% and 2% are owned by Budhist and Hinds. This information shows that Muslim

is taking part as major community not only in religion aspect, but also in banking

sectors.

Time to Maturity

Table 4.6 Contribution Percentage of Deposit Account Based on Maturity Date

(MATDT)

MATDT Total Percentage

< 30 days 7 2%

30 ≤ MATDT ≤ 90 days 51 13%

91 ≤ MATDT ≤ 180 days 24 6%

181 ≤ MATDT ≤ 360 days 70 18%

> 360 days 227 60%

379 100%

Deposit has maturity date. In this research, maturity date is divided into five categories.

Based on Table 4.6, there are 2% of the deposit accounts are under 30 days of maturity.

The other 13% are having maturity time between 30 and 90 days, 6% are between 91

and 180 days, and 18% are between 181 and 360 days. The biggest percentage is

deposit accounts that have maturity time above 360 days. This is represents that most

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deposit customers do not withdrawl their funds over years, or in other word it is called

roll over deposit.

Range of Ages

Table 4.7 Contribution Percentage of Deposit Account Based on Age

AGE Total Percentage

< 25 9 2%

25 ≤ age ≤ 45 159 42%

46 ≤ age ≤ 55 87 23%

56 ≤ age ≤ 65 93 25%

> 65 years old 31 8%

379 100%

Related to the age of Deposit customer, according to Table 4.7, there are 42% of the

Deposit holder are people whose age is around 25 to 45 years old. This means that

people who are in productive age becoming the major range of age that contributes

highest deposit account at Bank X West Java Regional Area.

In this research, the various amount of funds deposited (NTR Value) will be the

dependant (Y) and the other four aspects which are Currency, Religion, Maturity Date,

and Age will be the independent variable (X).

1.3.1.2 Saving Accounts

Amount of Fund Saved in Saving Account

Table 4.8 Percentage of Funds in Saving Account

NTR Total Percentage

NTR ≤ 200 million 335 87.24%

200 million < NTR ≤ 5 billion 44 11.46%

> 5 billion 5 1.30%

384 100%

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Based on Table 4.8, there are 87.24% of the funding composition in Saving Accounts is

equal or less than IDR 200 million. This means that most of funding sources is coming

from micro funding. The other 16% of funds is generated from small funding (including

wealth funds) and the rest 1% is commercial fund.

Type of Religion

Table 4.9 Contribution Percentage of Saving Account Based on Religion

Religion Total Percentage

Moslem 300 78%

Katholic 28 7%

Protestant 38 10%

Budha 18 5%

Hind 0 0%

384 100%

According to Table 4.9 above, Muslim cutomers have been the majority as the source of

funding with significant percentage of saving accounts contribution which is 78%. The

other 17% of the accounts are owned by Christian customers and the rest 5% are owned

by Budhist. From this information, Muslim people are the major funding contributors

also for the bank.

Range of Ages

Table 4.10 Contribution Percentage of Saving Account Based on Age

AGE Total Percentage

< 25 15 4%

25 ≤ age ≤ 45 188 49%

46 ≤ age ≤ 55 109 28%

56 ≤ age ≤ 65 50 13%

> 65 years old 22 6%

384 100%

Related to the range of age of the saving account customers, there are 49% of the

accounts are owned by customers whose ages are around 25 to 45 years old which is in

productive ages. The rest are owned by people whose ages above 45 years old for about

41% and under 25 years old which is 4%.

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Type of Job

Table 4.11 Contribution Percentage of Saving Account Based on Type of Job

Type of Job Total Percentage

Gov Employee 35 9%

Private-Owned Comp Employee 192 50%

State-Owned Comp Employee 15 4%

Businessman 95 25%

Others (IRT,MHS,PENS) 47 12%

384 100%

Type of job is divided into five categories which are Government Employee, Private-

Owned Company Employee. State-Owned Company Employee, Businessman, and

Others. According to Appendix 2, there are several codes that represents the job of each

customers. Those kind of jobs are grouped into these five categories. Government

Employee is consisted of Teachers (GR), Pegawai Negeri Sipil (PNS), and Army/Police

(MIL). Private-Owned Company’s Employee is consisted of Doctors (DKTR), Private-

Owned Company Employees (PSW/Pegawai Swasta), and Professionals (PROF). State

–Owned Company Employees is named as PBUM, Businessman is named as WSW

(Wiraswasta), and Others is consisted of Housewife (IRT), Students (MHS), and Retired

Worker (PENS).

Based on Table 4.11 above, there are 63% of the saving accounts holder are an

employee, 50% from Private-owned Company, 9% from Government Office, and 4%

from State-Owned Company. The other 25% are businessman and 12% are grouped in

Others. Therefore, the major funding contributor is taken by employees.

Length of Owning

Table 4.12 Contribution Percentage of Saving Account Based on Owning Period

Owning Period Total Percentage

≤ 6 years 124 32%

6 < Years ≤ 12 186 48%

> 12 Years 74 19%

384 100%

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Related to the period, since the customers opened a saving account, there are three

categories, remaining the existence of Bank X which is about 12 years since the merger.

There are 32% of the saving accounts have been existed for 6 years and 48% have been

existed between 6 to 12 years, and the rest 19% of saving accounts have been existed

for more than 12 years. This 19% of owning period percentages indicates that the

account holders have saved their money in one of the bank since bank merged, so the

owning periods are excess 12 years.

Time to Maturity

Saving Account has a clear difference with Deposit in term of its maturity time. In

saving account, there is no significant maturity time like in Deposit. Anytime,

customers have rights to withdraw their money from the Bank and because of that, the

Bank must have minimum liquidity to serve the customer. Therefore, time to maturity

for saving account is 1 day.

In this research, amount of money saved as saving account will be the dependent

variable (Y) and the other four factors which are Type of Job, Religion, Maturity Date,


4.3.1.3 Current Accounts

Amount of Fund Saved in Current Account

Table 4.13 Percentage of Funds in Current Account

NTR Total Percentage NTR ≤ 200 million 306 86.44% 200 million < NTR ≤ 5 billion 43 12.15% > 5 billion 5 1.41% 354 100%

Based on Table 4.13, there are 86.44% of the funding composition in Current Accounts

is equal or less than IDR 200 million. This means that most of funding sources is

coming from micro funding. The other 12.15% of funds is generated from small funding

(including wealth funds) and the rest 1.41% is commercial fund.

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Type of Religion

Table 4.14 Contribution Percentage of Current Account Based on Religion

Religion Total Percentage Moslem 279 78.81% Katholic 24 6.78% Protestant 35 9,89% Budha 14 3.95% Hind 2 0.56% 354 100%

According to Table 4.14 above, Muslim cutomers have been the majority as the source

of funding with significant percentage of current accounts contribution which is

78.81%. The other 16.67% of the accounts are owned by Christian customers and the

rest 3.95% and 0.56% are owned by Budhist and Hind. From this information, Muslim

customers are the major funding contributors also for the bank.

Range of Ages

Table 4.15 Contribution Percentage of Current Account Based on Age

AGE Total Percentage < 25 11 3.11% 25 ≤ age ≤ 45 140 39.55% 46 ≤ age ≤ 55 108 30.51% 56 ≤ age ≤ 65 68 19.21% > 65 years old 27 7.63% 354 100%

Related to the range of age of the current account customers, there are 39.55% of the

accounts are owned by customers whose ages are around 25 to 45 years old which is in

productive ages. The rest are owned by people whose ages above 45 years old for about

49.72% and under 25 years old which is 3.11%.

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Type of Job

Table 4.16 Contribution Percentage of Current Account Based on Type of Job

Type of Job Total Percentage Gov Employee 11 3.11% Private-Owned Comp Employee 159 44.92% State-Owned Comp Employee 9 2.54% Businessman 172 48.59% Others (IRT,MHS,PENS) 3 0.85% 354 100%

Type of job is divided into five categories which are Government Employee, Private-

Owned Company Employee. State-Owned Company Employee, Businessman, and

Others. According to Appendix 2, there are several codes that represents the job of each

customers. Those kind of jobs are grouped into these five categories. Government

Employee is consisted of Teachers (GR), Pegawai Negeri Sipil (PNS), and Army/Police

(MIL). Private-Owned Company’s Employee is consisted of Doctors (DKTR), Private-

Owned Company Employees (PSW/Pegawai Swasta), and Professionals (PROF). State

–Owned Company Employees is named as PBUM, Businessman is named as WSW

(Wiraswasta), and Others is consisted of Housewife (IRT), Students (MHS), and Retired

Worker (PENS).

Based on Table 4.16 above, there are 50.57% of current accounts holder are an

employee, 44.92% from Private-owned Company, 3.11% from Government Office, and

2.54% from State-Owned Company. The other 48.59% are businessman and 0.85% are

grouped in Others. Therefore, the major funding contributor is taken by employees.

However, based on Table 4.16 above, businessman also contributes quite high

percentage of fund in current account. This means that current account is still chosen by

the customer to ease their business transactions.

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Length of Owning

Table 4.17 Contribution Percentage of Current Account Based on Owning Period

Owning Period Total Percentage ≤ 6 years 106 29.94% 6 < Years ≤ 12 195 55.08% > 12 Years 53 14.97% 354 100%

Related to the period, since the customers opened a current account, there are three

categories, remaining the existence of Bank X which is about 12 years since the merger.

There are 29.94% of the current accounts have been existed for 6 years and 55.08%

have been existed between 6 to 12 years, and the rest 14.97% of current accounts have

been existed for more than 12 years. This 14.97% of owning period percentages

indicates that the account holders have saved their money in one of the bank since bank

merged, so the owning periods are excess 12 years.

Time to Maturity

Similar with Saving Account, Current Account also has a clear difference with Deposit

in term of its maturity time. In current account, there is no significant maturity time like

in Deposit. Anytime, customers have rights to withdraw their money from the Bank and

because of that, the Bank must have minimum liquidity to serve the customer.

Therefore, time to maturity for current account is 1 day.

In this research, amount of money saved as current account will be the dependent

variable (Y) and the other four factors which are Type of Job, Religion, Maturity Date,


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1.3.2 Dummies

Below are the list of dummies that are used in the equation:

1) Y = 1, if the NTR Value is less than or equal to IDR 200 millions

Y = 0, if the NTR Value is greater than IDR 200 millions

The dependent variable is divided into 1 and zero based on the group of funding

segment. At Bank X, the amount of fund which is less than or equal to IDR 200

million is grouped as Micro Funds. On the other hand, amount of fund which is

greater than IDR 200 million up to IDR 5 billion is categorized as Small Fund

and amount of fund which is greater than IDR 5 billion is categorized as

Commercial Fund.

In this research, various amount of funds saved in deposit, saving, and current

account are better to be categorized based on the segmenting of funding sources.

The reason is related to the objective in this research which is identifying factors

that affect the most to the variety of third party’s funds based on the

segmentation. Micro funds will be represented by 1. The consideration of

representing the micro funds by 1 is because based on Table 4.3, Table 4.8, and

Table 4.13, micro funds become the major sources of funding at Bank X West

Java Regional Area. As state-owned bank, it provides financial service for all

levels of Indonesian society from low class to high class. In fact, Indonesian

people which are in low and middle class still have a low consciousness and or a

lack of ability to save their money in a bank. Since the government realized the

low ability of Indonesian people to save their money in Bank, recently a total of

22 national banks and the four banking associations (Perbarindo, Asbanda,

Asbisindo, and Perbanas) incorporated in the group of working launched a

program called “Tabunganku” on February 20th, 2010 which was required only

IDR 20,000 to open the bank account.3At the end, this program adds the higher

composition of micro funding in Bank X, especially around West Java area.

3 http://www.waspada.co.id/index.php?option=com_content&view=article&id=58719:22-bank-luncurkan-qtabungankuq-murah-tanpa-biaya-administrasi&catid=18&Itemid=95

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Since the micro funds is represented by 1, small and commercial funds is

represented by 0. There is a clear difference between each type of funding

segment.

2) Currency = 1, if the currency type is Rupiah (IDR)

Currency = 0, if the currency type is not Rupiah

Since the currency type is a kind of qualitative information, this variable is

categorized as dummy. As the national currency of Indonesia, Rupiah takes place

as the major currency in any financial institution.Based on Table 4.4, the

probability of deposit account in Rupiah (IDR) is 61%. Therefore, in this

research, deposit account in Rupiah will be represented by 1 and the other

currencies will be represented by 0.

3) Religion = 1, if the religion of the customer is Islam

Religion = 0, if the religion of the customer is non-Islam

Religion is a kind of qualitative information. So, in this research, religion is

categorized as dummy. Based on Table 4.5, Table 4.9, and Table 4.14 the

probability of deposit, saving, and current account that are owned by Muslim

customer is become the majority. Therefore, it will be represented by 1. Then,

deposit account that is owned by non-Muslim customer will be represented by 0.

4) Maturity Date = 1, if the maturity time is less than or equal to 180 days (6

month)

Maturity Date = 0, if the maturity time is greater than 180 days (6 month)

Maturity time in this research will be categorized as dummy. Eventhough the

raw data which represents maturity time is a quantitative data, categorizing

maturity time as dummy is based on considerations.

Firstly, maturity time will affect the possibilities of deposit customer withdraw

their saving in certain times. So, if there are more deposit that will meet its

maturity time below 6 month, the Bank must prepare certain amount of existing

cash in order to meet their obligation to the third party in short time. Secondly,

separating the two categories of maturity time is helpful to clear the

interpretation later on after generating the output from EViews 7.

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Therefore, if the maturity time is less than or equal to 180 days (6 months), it

will be represented by 1 and in contrast to the previous requirement, if the

maturity time is greater than 180 days (6 months), it will be represented by 0.

On the other hand, there is difference between maturity time that is used in

deposit calculation and saving or current accounts calculation. Below is the rules

while using maturity time as dummy in the statistical analysis of saving and

current accounts:

Maturity Time = 1, if the maturity time is 1 day

Maturity Time = 0, if the maturity time is greater than one day

Based on the explanation in Part 4.3.1.2, maturity time for all saving and current

accounts is one day because in every day, customers are allowed to withdraw

their money. Therefore, all maturity times are represented by 1.

5) Age = 1, if the customer is in productive age

Age = 0, if the customer is not in productive age

In this research, age is divided into two categories based on the productivity age.

Since productive or non-productive age is kind of qualitative information, age is

categorized as dummy. The age of depositor which is between 25 to 45 years old

is chosen as the limit of productivity age. This is because 25 years old is usually

a new employee. In this range of age also, people will spend more money to

prepare their future plans such as for insurance, housing, and etc. Therefore, the

amount of money that they save in a bank will not be really big. Usually the

employee will reached the highest position in the age of 45. After that, they will

continue to until retirement age at 55. Therefore, people whose age are greater

than or equal to 25 years old and less than or equal to 45 years old will be

represented by 1 and the rest will be represented by 0.

6) Job = 1, if the customer is an employee

Job = 0, if the customer is a businessman or others

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Since job is kind of qualitative data, it is categorized as dummy in this research.

In this research, saving account and current account which is owned by

employee (government employee, state-owned company employee, and private-

owned company employee) will be represented as 1. The other job of saving

account’s and current account’s holder is represented by 0.

4.3.3 Analysis Logit Model for Individual Data by Using EViews 7

4.3.3.1 Deposit Logit Model Analysis

Table 4.18 Eviews 7 Output for Deposit

Dependent Variable: NTR Method: ML - Binary Logit (Quadratic hill climbing) Date: 07/05/10 Time: 22:31 Sample: 1 379 Included observations: 379 Convergence achieved after 4 iterations Covariance matrix computed using second derivatives

Variable Coefficient Std. Error z-Statistic Prob.

C 2.023683 0.416338 4.860669 0.0000 CURRENCY -0.731166 0.400760 -1.824449 0.0681

MATDT 0.047295 0.455642 0.103798 0.9173 RELIGION 0.493865 0.370827 1.331796 0.1829

AGE 1.141913 0.425692 2.682487 0.0073

McFadden R-squared 0.052646 Mean dependent var 0.905013 S.D. dependent var 0.293584 S.E. of regression 0.289331 Akaike info criterion 0.621184 Sum squared resid 31.30842 Schwarz criterion 0.673130 Log likelihood -112.7143 Hannan-Quinn criter. 0.641798 Deviance 225.4287 Restr. deviance 237.9560 Restr. log likelihood -118.9780 LR statistic 12.52732 Avg. log likelihood -0.297399 Prob(LR statistic) 0.013832

Obs with Dep=0 36 Total obs 379 Obs with Dep=1 343

From the EViews 7 output on Table 4.18 above, the constant and coefficients from each

variables are known, which are currency, maturity date (MATDT), religion, and age.

The equation will be as follows:

. . . .

.

39

Proportion Test (Z-Test)

Based on Table 4.18, the result of Z-Test from Eviews 7 can be interpreted below :

1. Currency

Since the Z-statistic < Z Table (-1.824449 < 1.645) and p-value > 0.05 (0.0681 >

0.05), H0 is accepted. This means that the proportion of micro funds at Deposit

accounts in Rupiah and other currencies are the same (no difference).

2. Maturity Time (MATDT)

Since the Z-statistic < Z Table (0.103798 < 1.645) and p-value > 0.05 (0.9173 >

0.05), H0 is accepted. This means that the proportion of micro funds in deposit

account with maturity time less than or equal to 180 days is the same with the

proportion of micro funds in deposit account with maturity time which is greater

than 180 days.

3. Religion


0.05), H0 is accepted. This means that the proportion of micro funds in Deposit

account which is owned by Muslim customers and Deposit account which is

owned by non-Muslim customers are the same (no difference).

4. Age

Since the Z-statistic > Z Table (2.682487 > 1.645) and p-value < 0.05 (0.0073 <

0.05), H0 is rejected (H1 accepted). This means that the proportion of micro

funds in Deposit account which is owned by people around productive age (25 ≤

Age ≤ 45 years old) is greater than the proportion of micro funds in Deposit

account which is owned by customer under or over the productive age.

Interpretation from the output of EViews 7 :

1) Table 4.18 gives actual and predicted values of the regressand for the equation.

From this table there is information that, out of 379 observations, there are 30

incorrect predictions (from number 350 to number 379). Hence, the count

comparative R2 value is shown below :

349379 0.9208443

40

Whereas McFadden R2 value from EViews 7 output is 0.052646. These two

values are not directly comparable. Based on the McFadden R2 value, currency,

maturity time, age, and religion are represent and explain only 5.3% of the

whole factors that may affect the funding contribution from third party.

Meanwhile, there are 94.7% which are influenced by other factors that are not

stated in this research.

2) From the estimated Likelihood Ratio (LR) statistic, there is clear information

that four variables are statistically significant at about 1.3 percent level. Since

the using of 5 percent significance level, then these variables are statistically

significant. So, all regressors have a significant impact on the NTR Value, as the

LR statistic is 12.52732.

3) The negative sign of currency and positive sign of maturity date, religion, and

age are interpreted below.

1. Since the coefficient value of Currency is -0.731166, it becomes the

second largest factor that influences the composition of micro funding at

Bank X West Java Regional Area. If the Currency Type rate goes up by

1 percentage point, the logit goes down by about 0.731166, holding other

variable constant. Taking the anti-log of -0.731166 (e-0.731166), then the

result is 0.4813. This means that if the deposit account is in Rupiah

(IDR), on average, 48% of the funds in Rupiah are from micro funds.

2. Since the coefficient value of maturity time is 0.047295, it becomes the

lowest factor that influences the composition of micro funding at Bank

X. If the Maturity Date (MATDT) rate goes up by 1 percentage point,

the logit also goes up by about 0.047295, holding other variable constant.

Taking the anti-log of 0.047295 (e0.047295), then the result is 1.048. This

means that the micro funds in deposit accounts are 1.048 times more

consisted of deposit accounts which have maturity time less than or equal

to 180 days than deposit accounts which have maturity time over 180

days.

41

3. Since the coefficient value of Religion is 0.493865, it becomes the third

biggest factor that influences the micro funding composition at Bank X

West Java Regional Area. If the Religion rate goes up by 1 percentage

point, the logit goes up by about 0.493865, holding other variable

constant. Counting anti-log of 0.493865, then the result is 1.6386. This

means that the micro funds in deposit accounts are 1.64 times more

owned by Muslim customers than owned by non-Muslim customers.

4. Since the coefficient value of Age is 1.141913, it becomes the biggest

factor that affect the funding composition at Bank X West Java Regional

Area. If the Age rate goes up by 1 percentage point, the logit goes up by

about 1.141913, holding other variables constant. Taking anti-log of

1.141913 (e1.141913), then the result is 3.133. This means that the micro

funds in deposit accounts are 3.133 times more owned by people around

productive age which is from 25 to 45 years old than others.

Since Age has the highest coefficient than others, which is 1.141913, Age has the

biggest correlation to the funding composition based on NTR Value. This means

that mostly the composition of micro, small, and commercial fund are influenced

by the productivity of account’s holder.

42

4.3.3.2 Saving Account Logit Model Analysis

Table 4.19 Eviews 7 Output for Saving Account (Including Maturity Date)

Dependent Variable: NTR Method: ML - Binary Logit (Quadratic hill climbing) Date: 07/05/10 Time: 23:06 Sample: 1 384 Included observations: 384 Convergence achieved after 5 iterations WARNING: Singular covariance - coefficients are not unique Covariance matrix computed using second derivatives


C 0.683323 NA NA NAMAT 0.683323 NA NA NA

RELIGION 0.383188 NA NA NAJOB 0.091682 NA NA NAAGE 0.470278 NA NA NA

McFadden R-squared 0.012602 Mean dependent var 0.872396S.D. dependent var 0.334084 S.E. of regression 0.334139Akaike info criterion 0.780032 Sum squared resid 42.31492Schwarz criterion 0.831473 Log likelihood -144.7661Hannan-Quinn criter. 0.800435 Deviance 289.5322Restr. deviance 293.2276 Restr. log likelihood -146.6138LR statistic 3.695400 Avg. log likelihood -0.376995Prob(LR statistic) 0.448795

Obs with Dep=0 49 Total obs 384Obs with Dep=1 335

Table 4.20 Eviews 7 Output for Saving Account (Without Maturity Date)



C 1.366645 0.362366 3.771449 0.0002 RELIGION 0.383188 0.347798 1.101752 0.2706

JOB 0.091682 0.319711 0.286765 0.7743 AGE 0.470278 0.314913 1.493357 0.1353



43

From the EViews 7 output in Table 4.19, the constant and coefficients from each

variables are known which are maturoty date, job, religion, and age. The equation will

be as follows

. . . .

.

On the other hand, since the maturity date seems constant, maturity date affects the

calculation of standard deviation, Z-test, and probability to be Not Applicable (NA) in

the calculation. Therefore, by eliminating the maturity date as variable and joining the

maturity date into constant, the Logit Model will be more make sense.

From the EViews 7 output in Table 4.20, for constant and coefficients from each

variables are known which are job, religion, and age. The equation will be as follows

. . . .



1. Maturity Date (MATDT)

The input of Maturity Time in EViews 7 as variable on Table 4.19 makes the

result of standard error, z-statistic, and probability become Not-Applicable

(NA). Compared to Table 4.20, if the maturity time is not input, there are clear

information about the standard error, z-statistic, and probability for all

variables, except Maturity Time.

2. Religion


0.05), H0 is accepted. This means that the proportion of micro funds in saving

accounts which are owned by Muslim customers and the proportion of micro

funds in saving account which are owned by non-Muslim customers are the

same (no difference).

3. Job

Since the Z-statistic < Z Table 0.286765 < 1.645) and p-value > 0.05 (0.7743 >


44

accounts which are owned by employees (government office employee, state-

owned company employee, and private-owned company employee) and the

proportion of micro funds in saving accounts which are owned by other kind of

job (businessman, students, housewife, etc) are the same (no difference).

4. Age



account which are owned by people around productive age (25 ≤ Age ≤ 45 years

old) and the proportion of micro funds in saving account which are owned by

people under or over the productive age are the same (no difference).

Interpretation from the output of EViews 7:

1) Table 4.19 an 4.20 gives actual and predicted values of the regressand for the

equation. From this table there is information that, out of 384 observations, there

are 49 incorrect predictions (from number 336 to number 384). Hence, the count


335384 0.8723958


values are not directly comparable. Based on the McFadden R2 value, type of

job, maturity time, age, and religion are represent and explain only 1.26% of the

whole factors that may affect the funding contribution from third party which is

a very small percentage. Meanwhile, there are 98.74% which are influenced by

other factors that are not stated in this research.


that four variables are statistically significant at about 44.87 percent level. Since

the using of 5 percent significance level, then these variables are not statistically

significant. So, all regressors have very low significant impact on the NTR

Value, as the LR statistic is 3.6954.

45

3) The positive sign of maturity date, religion, job and age are interpreted below.

1. Since the coefficient value of Job is 0.091682, job becomes the least

factor that influences the composition of micro funding at Bank X West

Java Regional Area. If the Job rate goes up by 1 percentage point, the

logit goes up by about 0.09162, holding other variable constant. Taking

the anti-log of 0.091682 (e0.091682), then the result is 1.0960. This means

that the saving accounts which is categorized as micro fund is 1.0960

times mostly owned by employee (government office employee, state-

owned company employee, and private-owned company employee) than

other kind of job (businessman, students, housewife, etc).

2. If the increment Maturity Date (MATDT) increased by 1 percentage

point, the logit also goes up by about 0.683323, holding other variable

constant.

Based on Table 4.19, Maturity Date has the highest coefficient than

others, which is 0.683323, Maturity Date has the biggest correlation to

the funding composition based on variety of funds saved. The coefficient

value of maturity date is the same with constant and all saving accounts

have the same maturity time which is 1 day. Therefore, no matter the

segment of its source, whether it is from micro, small, or commercial

funding, the maturity date is always 1 day because the customers have

right to withdraw their money in saving account anytime.

3. Since the coefficient value of Religion is 0.383188, religion becomes the

third biggest factor that influence the composition of micro funding at

Bank X West Java Regional Area. If the Religion rate goes up by 1

percentage point, the logit goes up by about 0.383188, holding other

variable constant. Counting anti-log of 0.383188 (e0.383188), then the

result is 1.467. This means that micro funds in saving accounts is 1.467

times more owned by Muslim than customers with other type of religion.

46

4. Since the coefficient value of Age is 0.470278, age becomes the second

biggest factor that influence the composition of micro funding at Bank X

West Java Regional Area. If the Age rate goes up by 1 percentage point,

the logit goes up by about 0.470278, holding other variable constant.

Taking anti-log of 0.470278 (e0.470278), then the result is 1.6. This means

that micro funds in saving accounts are 1.6 times more owned by

customers around productive age.

4.3.3.3 Current Account Logit Model Analysis

Table 4.21 Eviews 7 Output for Current Account (Including Maturity Date)

Dependent Variable: NTR Method: ML - Binary Logit (Quadratic hill climbing) Date: 07/20/10 Time: 20:11 Sample: 1 354 Included observations: 354 Convergence achieved after 5 iterations WARNING: Singular covariance - coefficients are not unique Covariance matrix computed using second derivatives


C 0.878795 NA NA NA MAT 0.878795 NA NA NA JOB -0.938705 NA NA NA

RELIGION 0.568316 NA NA NA AGE 0.711274 NA NA NA



47

Table 4.22 Eviews 7 Output for Current Account (Without Maturity Date)



C 1.757591 0.377521 4.655616 0.0000 JOB -0.938705 0.343244 -2.734800 0.0062

RELIGION 0.568316 0.353405 1.608116 0.1078 AGE 0.711274 0.370556 1.919481 0.0549



From the EViews 7 output in Table 4.21, the constant and coefficients from each

variables are known which are maturity date, job, religion, and age. The equation will

be as follows

. . . . .

On the other hand, since the maturity date seems constant, maturity date affects the

calculation of standard deviation, Z-test, and probability to be Not Applicable (NA) in

the calculation. Therefore, by eliminating the maturity date as variable and joining the

maturity date into constant, the Logit Model will be more make sense.

From the EViews 7 output in Table 4.22, for constant and coefficients from each

variables are known which are job, religion, and age. The equation will be as follows

. . . .

48



1. Maturity Date (MATDT)

The input of Maturity Date in EViews 7 as variable on Table 4.21 makes the

result of standard error, z-statistic, and probability become Not-Applicable

(NA). Compared to Table 4.22, if the maturity date is not input, there are clear

information about the standard error, z-statistic, and probability for all

variables, except Maturity Date.

2. Religion


0.05), H0 is accepted. This means that the proportion of current accounts in form

of micro funds which are owned by Muslim customers and current accounts in

form of micro funds which are owned by non-Muslim customers are the same

(no difference).

3. Job

Since the Z-statistic < Z Table -2.734800 < 1.645 and p-value < 0.05 (0.0062 <

0.05), H0 is rejected (H1 accepted). This means that the proportion of current

accounts in form of micro funds which are owned by employees (government

office employee, state-owned company employee, and private-owned company

employee) are greater than the proportion of current accounts in form of micro

funds which are owned by other kind of job (businessman, students, housewife,

etc) are the same (no difference).

4. Age

Since the Z-statistic > Z Table (1.919481 > 1.645) and p-value > 0.05 (0.0549 >

0.05), H0 is rejected (H1 accepted). This means that the proportion of micro

funds in current account which is owned by people around productive age (25 ≤

Age ≤ 45 years old) is greater than the proportion of micro funds in current

accounts which are owned by people under or over the productive age.

49

Interpretation from the output of EViews 7:

1) Table 4.21 an 4.22 gives actual and predicted values of the regressand for the

equation. From this table there is information that, out of 384 observations, there

are 46 incorrect predictions (from number 307 to number 354). Hence, the count


306354 0.864407


values are not directly comparable. Based on the McFadden R2 value, type of

job, maturity time, age, and religion are represent and explain only 6.0314% of

the whole factors that may affect the funding contribution from third party which

is a very small percentage. Meanwhile, there are 93.97% which are influenced

by other factors that are not stated in this research.


that four variables are statistically significant at about 0.0724 percent level.

Since the using of 5 percent significance level, then these variables are

statistically significant. So, all regressors have high impact on the NTR Value, as

the LR statistic is 16.94783.

3) The negative sign of job and positive sign of maturity date, religion, and age are

interpreted below.

1. Since the coefficient value of Job is -0.938705, job becomes the most

influencing factor to the composition of micro funding in current account at

Bank X West Java Regional Area. If the job rate goes up by 1 percentage

point, the logit goes down by about 0.938705, holding other variable

constant. Taking the anti-log of -0.938705 (e-0.938705), then the result is

0.3911. This means that 39.11% of funds in current accounts which are

owned by employee (government office employee, state-owned company

employee, and private-owned company employee) from micro funds.

50

2. If the increment Maturity Date (MATDT) increased by 1 percentage point,

the logit also goes up by about 0.878795, holding other variable constant.

Based on Table 4.21, Maturity Date has the second highest coefficient than

others, which is 0.878795. The coefficient value of maturity date is the same

with constant and all current accounts have the same maturity time which is

1 day. Therefore, no matter the segment of its source, whether it is from

micro, small, or commercial funding, the maturity date is always 1 day

because the customers have right to withdraw their money in current account

anytime and this makes the coefficient of maturity date grouped with the

constant to be 1.757591.

3. Since the coefficient value of Age is 0.711274, age becomes the third biggest

factor that influence the composition of micro funding in current account at

Bank X West Java Regional Area. If the Age rate goes up by 1 percentage

point, the logit goes up by about 0.711274, holding other variable constant.

Taking anti-log of 0.711274 (e0.711274), then the result is 2.0366. This means

that micro funds in saving accounts are 2.0366 times more owned by

customers around productive age.

4. Since the coefficient value of Religion is 0.568316, religion becomes the

least influencing factor to the composition of micro funding in current

account at Bank X West Java Regional Area. If the Religion rate goes up by

1 percentage point, the logit goes up by about 0.568316, holding other

variable constant. Counting anti-log of 0.568316 (e0.568316), then the result is

1.7653. This means that micro funds in current accounts is 1.7653 times

more owned by Muslim than customers with other type of religion.

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