CHAPTER IV DATA COLLECTION AND ANALYSIS

Transcription

1 CHAPTER IV DATA COLLECTION AND ANALYSIS 4.1 Brief Description of Bank X 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 17

2 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: 18

3 Board of Commissioners President Director 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 Corporate Banking I Corporate Banking II Corporate Banking III Syndct and Strctd Finance Plantation Specialist Bank X Securities Jkt Com Sales Reg Com Sales I Reg Com Sales II Wholesale Prdct Mgt Small Business I Small Business II Jakarta Network Regional Network Micro Business Mass and E- Bankng Wealth Mngt BBB X Fin Svc Int bnk and CM serv Treasury Bank X Europe Ltd Consumer Finance Consumer Cards Credit Recovery I Credit Rec II Asset Mngt Complianc e HC Service HC Strtgy & Plcy Learning Center Market & Opr Risk Credit Risk and Corporate Risk Commerci al Risk Retail and Consumer Risk Investor Relation Strategy and Perf Accountin g Procureme nt and FA Chief Economist IT Business Solution and Application Services IT Operation Planning, Plcy, Procedures, Arch Credit Operations Corporate Secretary Legal Customer Care Culture and Service Spc. Syaria Bank X Bank Z Bali Consumer Loans Central Operations Figure 4.1 Head Office Organizational Chart Tunas Finance Change Mngt Office Internal Audit e-channel operations 19

4 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. 20

5 Strategic Alliance - Strong customer base in Corporate, Institutional, and BUMN segment. 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. WEAKNESSES 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 21

6 and CM Ratio are not the same in each branch and cash outlet 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. OPPORTUNITY 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 22

7 E-Channel Development and Improvement - More various e-channel services as the networkalternatives for distribution - Global education and information level that push the using of e-channel 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 THREATS 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. 23

8 4.2 Data Collection Number of Samples 1 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. 24

9 Table 4.2 Krejcie and Morgan Sample Size (N) (s) (N) (s) (N) (s) Jumlah Jumlah Jumlah Jumlah Jumlah Jumlah Anggota Anggota Anggota Anggota Anggota Anggota Populasi Sampel Populasi Sampel Populasi Sampel 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 ( 98/101/sample.htm) 25

10 4.3.1 General Profile Deposit Amount of Funds Deposited Table 4.3 Percentage of Funds in Deposit Acount Amount of Fund Total Percentage 10 NTR 200 million % 200 million < NTR 5 billion 32 8% > 5 billion 4 1% % 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 % Others % % 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. 26

11 Types of Religion Table 4.5 Contribution Percentage of Deposit Account Based on Religion Religion Total Percentage Moslem % Katholic 44 12% Protestant 41 11% Budha 15 4% Hind 6 2% % 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 % % 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 27

12 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 age % 46 age % 56 age % > 65 years old 31 8% % 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) Saving Accounts Amount of Fund Saved in Saving Account Table 4.8 Percentage of Funds in Saving Account NTR Total Percentage NTR 200 million % 200 million < NTR 5 billion % > 5 billion % % 28

13 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 % Katholic 28 7% Protestant 38 10% Budha 18 5% Hind 0 0% % 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 age % 46 age % 56 age % > 65 years old 22 6% % 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%. 29

14 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 % State-Owned Comp Employee 15 4% Businessman 95 25% Others (IRT,MHS,PENS) 47 12% % 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 % 6 < Years % > 12 Years 74 19% % 30

15 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, and Age will be the independent variable (X) Current Accounts Amount of Fund Saved in Current Account Table 4.13 Percentage of Funds in Current Account NTR Total Percentage NTR 200 million % 200 million < NTR 5 billion % > 5 billion % % 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. 31

16 Type of Religion Table 4.14 Contribution Percentage of Current Account Based on Religion Religion Total Percentage Moslem % Katholic % Protestant 35 9,89% Budha % Hind % % 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 age % 46 age % 56 age % > 65 years old % % 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%. 32

17 Type of Job Table 4.16 Contribution Percentage of Current Account Based on Type of Job Type of Job Total Percentage Gov Employee % Private-Owned Comp Employee % State-Owned Comp Employee % Businessman % Others (IRT,MHS,PENS) % % 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. 33

18 Length of Owning Table 4.17 Contribution Percentage of Current Account Based on Owning Period Owning Period Total Percentage 6 years % 6 < Years % > 12 Years % % 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, and Age will be the independent variable (X). 34

19 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 20 th, 2010 which was required only IDR 20,000 to open the bank account. 3 At the end, this program adds the higher composition of micro funding in Bank X, especially around West Java area

20 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. 36

21 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 , 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 37

22 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 privateowned company employee) will be represented as 1. The other job of saving account s and current account s holder is represented by Analysis Logit Model for Individual Data by Using EViews 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: Included observations: 379 Convergence achieved after 4 iterations Covariance matrix computed using second derivatives Variable Coefficient Std. Error z-statistic Prob. C CURRENCY MATDT RELIGION AGE McFadden R-squared Mean dependent var S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter Deviance Restr. deviance Restr. log likelihood LR statistic Avg. log likelihood Prob(LR statistic) 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:

23 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 ( < 1.645) and p-value > 0.05 ( > 0.05), H 0 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 ( < 1.645) and p-value > 0.05 ( > 0.05), H 0 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 Since the Z-statistic < Z Table ( < 1.645) and p-value > 0.05 ( > 0.05), H 0 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 ( > 1.645) and p-value < 0.05 ( < 0.05), H 0 is rejected (H 1 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 R 2 value is shown below :

24 Whereas McFadden R 2 value from EViews 7 output is These two values are not directly comparable. Based on the McFadden R 2 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 ) 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 , 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 , holding other variable constant. Taking the anti-log of (e ), then the result is 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 , 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 , holding other variable constant. Taking the anti-log of (e ), then the result is This means that the micro funds in deposit accounts are 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. 40

25 3. Since the coefficient value of Religion is , 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 , holding other variable constant. Counting anti-log of , then the result is 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 , 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 , holding other variables constant. Taking anti-log of (e ), then the result is This means that the micro funds in deposit accounts are 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 , 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. 41

26 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: Included observations: 384 Convergence achieved after 5 iterations WARNING: Singular covariance - coefficients are not unique Covariance matrix computed using second derivatives Variable Coefficient Std. Error z-statistic Prob. C NA NA NA MAT NA NA NA RELIGION NA NA NA JOB NA NA NA AGE NA NA NA McFadden R-squared Mean dependent var S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter Deviance Restr. deviance Restr. log likelihood LR statistic Avg. log likelihood Prob(LR statistic) Obs with Dep=0 49 Total obs 384 Obs with Dep=1 335 Table 4.20 Eviews 7 Output for Saving Account (Without Maturity Date) Dependent Variable: NTR Method: ML - Binary Logit (Quadratic hill climbing) Date: 07/07/10 Time: 11:08 Sample: Included observations: 384 Convergence achieved after 4 iterations Covariance matrix computed using second derivatives Variable Coefficient Std. Error z-statistic Prob. C RELIGION JOB AGE McFadden R-squared Mean dependent var S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter Deviance Restr. deviance Restr. log likelihood LR statistic Avg. log likelihood Prob(LR statistic) Obs with Dep=0 49 Total obs 384 Obs with Dep=

27 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.... Proportion Test (Z-Test) Based on Table 4.19, the result of Z-Test from Eviews 7 can be interpreted below : 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 Since the Z-statistic < Z Table ( < 1.645) and p-value > 0.05 ( > 0.05), H 0 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 < 1.645) and p-value > 0.05 ( > 0.05), H 0 is accepted. This means that the proportion of micro funds in saving 43

28 accounts which are owned by employees (government office employee, stateowned 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 Since the Z-statistic < Z Table ( < 1.645) and p-value > 0.05 ( > 0.05), H 0 is accepted. This means that the proportion of micro funds in saving 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 comparative R 2 value is shown below : Whereas McFadden R 2 value from EViews 7 output is These two values are not directly comparable. Based on the McFadden R 2 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. 2) From the estimated Likelihood Ratio (LR) statistic, there is clear information that four variables are statistically significant at about 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

29 3) The positive sign of maturity date, religion, job and age are interpreted below. 1. Since the coefficient value of Job is , 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 , holding other variable constant. Taking the anti-log of (e ), then the result is This means that the saving accounts which is categorized as micro fund is times mostly owned by employee (government office employee, stateowned 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 , holding other variable constant. Based on Table 4.19, Maturity Date has the highest coefficient than others, which is , 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 , 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 , holding other variable constant. Counting anti-log of (e ), then the result is This means that micro funds in saving accounts is times more owned by Muslim than customers with other type of religion. 45

30 4. Since the coefficient value of Age is , 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 , holding other variable constant. Taking anti-log of (e ), 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 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: Included observations: 354 Convergence achieved after 5 iterations WARNING: Singular covariance - coefficients are not unique Covariance matrix computed using second derivatives Variable Coefficient Std. Error z-statistic Prob. C NA NA NA MAT NA NA NA JOB NA NA NA RELIGION NA NA NA AGE NA NA NA McFadden R-squared Mean dependent var S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter Deviance Restr. deviance Restr. log likelihood LR statistic Avg. log likelihood Prob(LR statistic) Obs with Dep=0 48 Total obs 354 Obs with Dep=

31 Table 4.22 Eviews 7 Output for Current Account (Without Maturity Date) Dependent Variable: NTR Method: ML - Binary Logit (Quadratic hill climbing) Date: 07/20/10 Time: 20:13 Sample: Included observations: 354 Convergence achieved after 4 iterations Covariance matrix computed using second derivatives Variable Coefficient Std. Error z-statistic Prob. C JOB RELIGION AGE McFadden R-squared Mean dependent var S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter Deviance Restr. deviance Restr. log likelihood LR statistic Avg. log likelihood Prob(LR statistic) Obs with Dep=0 48 Total obs 354 Obs with Dep=1 306 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

32 Proportion Test (Z-Test) Based on Table 4.21, the result of Z-Test from Eviews 7 can be interpreted below : 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 Since the Z-statistic < Z Table ( < 1.645) and p-value > 0.05 ( > 0.05), H 0 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 < and p-value < 0.05 ( < 0.05), H 0 is rejected (H 1 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 ( > 1.645) and p-value > 0.05 ( > 0.05), H 0 is rejected (H 1 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. 48

33 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 comparative R 2 value is shown below : Whereas McFadden R 2 value from EViews 7 output is These two values are not directly comparable. Based on the McFadden R 2 value, type of job, maturity time, age, and religion are represent and explain only % 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. 2) From the estimated Likelihood Ratio (LR) statistic, there is clear information that four variables are statistically significant at about 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 ) 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 , 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 , holding other variable constant. Taking the anti-log of (e ), then the result is 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. 49

34 2. If the increment Maturity Date (MATDT) increased by 1 percentage point, the logit also goes up by about , holding other variable constant. Based on Table 4.21, Maturity Date has the second highest coefficient than others, which is 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 Since the coefficient value of Age is , 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 , holding other variable constant. Taking anti-log of (e ), then the result is This means that micro funds in saving accounts are times more owned by customers around productive age. 4. Since the coefficient value of Religion is , 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 , holding other variable constant. Counting anti-log of (e ), then the result is This means that micro funds in current accounts is times more owned by Muslim than customers with other type of religion. 50