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Notes: (i) Standard errors in brackets, (ii) * p<0.1, ** p<0.05, *** p<0.01

Table 38 Notes: (i) Standard errors in brackets, (ii) * p<0.1, ** p<0.05, *** p<0.01

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Table 2.1 Number of accounts with financial institutions (% of age 15+) 2014 Table 2.2 Access to financial services in Bangladesh According to Global Findex 2014, globally, 62% of adults reported having an account; this was 51% in 2011. This trend is driven by a 13-percentage- point rise in account ownership in developing countries as well as improved technology. In particular, mobile money accounts in developing countries are helping to rapidly expand and scale up access to financial services. Between 2011 and 2014, 700 million people became account holders at banks and other financial institutions or mobile money services providers. It has decreased the number of financially excluded individuals by 20% (from 2.5 to 2 billion adults). Table 2.1 presents global stands of financial
Table 2.1 Number of accounts with financial institutions (% of age 15+) 2014 Table 2.2 Access to financial services in Bangladesh According to Global Findex 2014, globally, 62% of adults reported having an account; this was 51% in 2011. This trend is driven by a 13-percentage- point rise in account ownership in developing countries as well as improved technology. In particular, mobile money accounts in developing countries are helping to rapidly expand and scale up access to financial services. Between 2011 and 2014, 700 million people became account holders at banks and other financial institutions or mobile money services providers. It has decreased the number of financially excluded individuals by 20% (from 2.5 to 2 billion adults). Table 2.1 presents global stands of financial
Fig. 2.2 Annual target and achievement of agricultural loan In recent years, the agriculture and rural finance program seems to have boosted up as the private commercial banks (PCBs) and foreign com- mercial banks (FCBs) along with state-owned commercial banks (SCBs) came forward to disburse agricultural credit in the country. The actual disbursement of BDT 209.99 billion in FY17 against the disbursement target of 175.50 billion was 19.0% higher than the actual disbursement of BDT 176.5 billion in FY16. Table 2.9 shows the comparative position of overall disbursement and recovery of agricultural loan and Fig. 2.2 shows the targets and actual disbursement of agricultural loan, respec- tively, in FY17.
Fig. 2.2 Annual target and achievement of agricultural loan In recent years, the agriculture and rural finance program seems to have boosted up as the private commercial banks (PCBs) and foreign com- mercial banks (FCBs) along with state-owned commercial banks (SCBs) came forward to disburse agricultural credit in the country. The actual disbursement of BDT 209.99 billion in FY17 against the disbursement target of 175.50 billion was 19.0% higher than the actual disbursement of BDT 176.5 billion in FY16. Table 2.9 shows the comparative position of overall disbursement and recovery of agricultural loan and Fig. 2.2 shows the targets and actual disbursement of agricultural loan, respec- tively, in FY17.
Table 2.3. MFIs’ branch network (as of June 2016)
Table 2.3. MFIs’ branch network (as of June 2016)
Table 2.4 Members in MFIs (as of June 2016) As of June 2016, the total number of members of microfinance sector was 38.62 million that includes active members of GB, BRAC, ASA, and also the rest of the Microfinance-NGOs (MF-NGOs). In the microfinance sec- tor, most of the members are women. Many Microfinance Institutions (MFIs) have both women and men as members. There are also MFIs where 100% members are women. Looked at in terms of gender, of the total members 34.23 million (91%) are females and 6.94 million (9%) are males.
Table 2.4 Members in MFIs (as of June 2016) As of June 2016, the total number of members of microfinance sector was 38.62 million that includes active members of GB, BRAC, ASA, and also the rest of the Microfinance-NGOs (MF-NGOs). In the microfinance sec- tor, most of the members are women. Many Microfinance Institutions (MFIs) have both women and men as members. There are also MFIs where 100% members are women. Looked at in terms of gender, of the total members 34.23 million (91%) are females and 6.94 million (9%) are males.
“Include wholesale lending of Palli Karmo Sahayak Foundation (PKSF) through 168 MFIs Include 6 state-owned commercial banks, 18 private commercial banks, and 3 specialized banks ‘Include Bangladesh Rural Development Board-Thana Central Cooperative Associations (BRDB-TCCAs) and Jubo Unnayan Adhidaptar Table 2.5 Microfinance in Bangladesh (as of June 2016)
“Include wholesale lending of Palli Karmo Sahayak Foundation (PKSF) through 168 MFIs Include 6 state-owned commercial banks, 18 private commercial banks, and 3 specialized banks ‘Include Bangladesh Rural Development Board-Thana Central Cooperative Associations (BRDB-TCCAs) and Jubo Unnayan Adhidaptar Table 2.5 Microfinance in Bangladesh (as of June 2016)
Table 2.6 Sector-wise microcredit disbursement (2015-2016) engaged in the agriculture sector as share-croppers/tenant farmers. All MFIs including GB, BRAC, and ASA have disbursed an amount of Tk. 955,772.18 million in ten identified sectors as shown in Table 2.6. The most important sectors where massive disbursement was made proposed by the borrowers include agriculture (35.13%), business (30.91%), livestock, dairy and poultry (11.26%), fisheries (3.58%), transport (3.72%), etc. In fact, broadly, the agriculture, livestock, and fisheries and poultry sectors account for 49.97%, that is 50%, of the total loan disbursed (Table 2.6). This indicates the massive contribution of these MFIs in the development of the rural economy in Bangladesh. More than 400 MFIs have disbursed loans to these four sectors. Other sectors, where MFIs have disbursed loans during the year to their member-borrowers include healthcare, education, housing, cottage industries and handicrafts, and others. About 15% of the total disbursement has gone to these aforesaid sectors. Significant numbers of MFIs have made disbursement to these small but important sectors.
Table 2.6 Sector-wise microcredit disbursement (2015-2016) engaged in the agriculture sector as share-croppers/tenant farmers. All MFIs including GB, BRAC, and ASA have disbursed an amount of Tk. 955,772.18 million in ten identified sectors as shown in Table 2.6. The most important sectors where massive disbursement was made proposed by the borrowers include agriculture (35.13%), business (30.91%), livestock, dairy and poultry (11.26%), fisheries (3.58%), transport (3.72%), etc. In fact, broadly, the agriculture, livestock, and fisheries and poultry sectors account for 49.97%, that is 50%, of the total loan disbursed (Table 2.6). This indicates the massive contribution of these MFIs in the development of the rural economy in Bangladesh. More than 400 MFIs have disbursed loans to these four sectors. Other sectors, where MFIs have disbursed loans during the year to their member-borrowers include healthcare, education, housing, cottage industries and handicrafts, and others. About 15% of the total disbursement has gone to these aforesaid sectors. Significant numbers of MFIs have made disbursement to these small but important sectors.
Table 2.8 Microfinance loan to micro-enterprises increase the loan size of the borrowers to expand their business activity. Now usually a loan above Tk. 50,000 is termed as enterprise loan. According to the capacity of the MFIs they raise the enterprise loan size. PKSF has allowed its Partner Organizations called POs to lend up to Tk. 1.0 million loans. But very few MFIs have reached this limit. This loan now can be up to Tk. 500,000 or even more depending on the size of the MFI. The borrowers are not required to put in any matching equity. That is, the debt-equity ratio is still not in practice in MFI enterprise financing.
Table 2.8 Microfinance loan to micro-enterprises increase the loan size of the borrowers to expand their business activity. Now usually a loan above Tk. 50,000 is termed as enterprise loan. According to the capacity of the MFIs they raise the enterprise loan size. PKSF has allowed its Partner Organizations called POs to lend up to Tk. 1.0 million loans. But very few MFIs have reached this limit. This loan now can be up to Tk. 500,000 or even more depending on the size of the MFI. The borrowers are not required to put in any matching equity. That is, the debt-equity ratio is still not in practice in MFI enterprise financing.
Table 2.10 Employment generation by MFIs (as of June 2016) even charge any fee at all. Such organizations have developed a welfare fund from their own earnings. The most common insurance practice is called loan insurance. Most MFIs have loan insurance. This insurance is applicable generally for the duration of the loan. In most cases of loan insurance, the spouses are covered. The number of claims and the amount involved is not significant. In fact, the claims are settled within a month. The MFIs have a policy in this regard. These claims may be in the process of settlement. The balance fund stands at Tk.13,710.61 million (Table 2.9).
Table 2.10 Employment generation by MFIs (as of June 2016) even charge any fee at all. Such organizations have developed a welfare fund from their own earnings. The most common insurance practice is called loan insurance. Most MFIs have loan insurance. This insurance is applicable generally for the duration of the loan. In most cases of loan insurance, the spouses are covered. The number of claims and the amount involved is not significant. In fact, the claims are settled within a month. The MFIs have a policy in this regard. These claims may be in the process of settlement. The balance fund stands at Tk.13,710.61 million (Table 2.9).
Table 2.11 Social services by microfinance institutions in Bangladesh
Table 2.11 Social services by microfinance institutions in Bangladesh
JICA FSPDSME- Japan International Cooperation Agency Financial Sector Project for the Development of Small and Medium Sized Enterprises Table 2.12 SME refinance schemes (up to June 2017)
JICA FSPDSME- Japan International Cooperation Agency Financial Sector Project for the Development of Small and Medium Sized Enterprises Table 2.12 SME refinance schemes (up to June 2017)
Table 2.14 CSR expenditure of banks and NBFIs (In FY2016-2017)
Table 2.14 CSR expenditure of banks and NBFIs (In FY2016-2017)
Table 2.15 Sector-wise CSR expenditure of IBBL 2.12.2 CSR activities of Islami Bank Bangladesh Limited lo BR head 0 head NYUANR WUE V Yeo a BOUVET SPT Oe Re Ee ee Ce hth Being the largest private bank in Bangladesh and the first Shariah-based bank in South and South-east Asia, the Islami Bank Bangladesh Limited (IBBL) has a huge contribution to the overall socio-economic development of Bangladesh. In order to popularize the concept of Islamic Banking, IBBL has started its charitable activities since inception through creation of “Sadaqah Tahbil”. Thereafter, it was done through the Islami Bank Foundation (IBF) converting the Sadaqah Tahbil into a full-fledged foun- dation since 20 May 1991. Further to its activities under IBF, IBBL estab- ished the Corporate Social Affairs Department (CSAD) in compliance with the instructions of the Central Bank. The CSR program of IBBL aims at the welfare and wellbeing of the people of the earth, the planet itself and all its stakeholders. Keeping that in view, this report focuses on the triple bottom line: People, Planet, and Profit. The purpose of this Sustainability Report is to provide in a single document an account of IBBL’s overall contribution to sustainable development in the country during the year 2016. Up to 2016 the total amount of CSR expenditure was Tk. 4998 million among 14.99 million beneficiaries. The amount of CSR expenditure in 2016 is Tk. 670.7 million among 1.19 million beneficiaries (Table 2.15).
Table 2.15 Sector-wise CSR expenditure of IBBL 2.12.2 CSR activities of Islami Bank Bangladesh Limited lo BR head 0 head NYUANR WUE V Yeo a BOUVET SPT Oe Re Ee ee Ce hth Being the largest private bank in Bangladesh and the first Shariah-based bank in South and South-east Asia, the Islami Bank Bangladesh Limited (IBBL) has a huge contribution to the overall socio-economic development of Bangladesh. In order to popularize the concept of Islamic Banking, IBBL has started its charitable activities since inception through creation of “Sadaqah Tahbil”. Thereafter, it was done through the Islami Bank Foundation (IBF) converting the Sadaqah Tahbil into a full-fledged foun- dation since 20 May 1991. Further to its activities under IBF, IBBL estab- ished the Corporate Social Affairs Department (CSAD) in compliance with the instructions of the Central Bank. The CSR program of IBBL aims at the welfare and wellbeing of the people of the earth, the planet itself and all its stakeholders. Keeping that in view, this report focuses on the triple bottom line: People, Planet, and Profit. The purpose of this Sustainability Report is to provide in a single document an account of IBBL’s overall contribution to sustainable development in the country during the year 2016. Up to 2016 the total amount of CSR expenditure was Tk. 4998 million among 14.99 million beneficiaries. The amount of CSR expenditure in 2016 is Tk. 670.7 million among 1.19 million beneficiaries (Table 2.15).
Table 2.16 No-frill accounts (NFAs) for farmers and the underprivileged group of the society
Table 2.16 No-frill accounts (NFAs) for farmers and the underprivileged group of the society
[able 2.17 Total numbers and amounts of deposits in Banks in Bangladesh
[able 2.17 Total numbers and amounts of deposits in Banks in Bangladesh
*The World Development Indicators (WDI). The World Bank ’The World Integrated Trade Solution (WITS). The World Bank ‘The Global Financial Development (GFD). The World Bank ‘International Monetary Fund, International Financial Statistics and data files using World Bank data on the GDP deflator ‘UNESCO Institute for Statistics ‘World Governance Indicators (WGI). The World Bank International Financial Statistics (IFS), International Monetary Fund (IMF)
*The World Development Indicators (WDI). The World Bank ’The World Integrated Trade Solution (WITS). The World Bank ‘The Global Financial Development (GFD). The World Bank ‘International Monetary Fund, International Financial Statistics and data files using World Bank data on the GDP deflator ‘UNESCO Institute for Statistics ‘World Governance Indicators (WGI). The World Bank International Financial Statistics (IFS), International Monetary Fund (IMF)
Table 3.2 Expected impact of variables
Table 3.2 Expected impact of variables
Table 3.3. Selected OIC countries
Table 3.3. Selected OIC countries
Table 3.4 Low- and high-income OIC countries plit our sample into two subcategories: low- and high-income countries. 3ased on the World Bank classifications, countries are classified into four ncome categories, namely: low-income, lower middle income, upper middle neome and high-income. For the purpose of this study we combined Jow nd lower middle income countries into low-income group. Similarly, we combined upper middle income and high-income countries into high-income roup. The detailed classification, according to the World Bank nethodology, is presented in Table 3.4. Out of 48 countries in our sample, 9 of them or 60.42% fall under the low-income countries group, while he remaining 19 or 39.58% countries fall under the high-income group. We go a step further and divide our sample into corrupted and less- orrupted countries (see Table 3.5). As a proxy measure of a level of cor- uption we use control of corruption (percentile) data provided by the Norld Governance Indicator, the World Bank. According to the database, hic “meacure cantires nercentions of the extent to which niblic nower jc
Table 3.4 Low- and high-income OIC countries plit our sample into two subcategories: low- and high-income countries. 3ased on the World Bank classifications, countries are classified into four ncome categories, namely: low-income, lower middle income, upper middle neome and high-income. For the purpose of this study we combined Jow nd lower middle income countries into low-income group. Similarly, we combined upper middle income and high-income countries into high-income roup. The detailed classification, according to the World Bank nethodology, is presented in Table 3.4. Out of 48 countries in our sample, 9 of them or 60.42% fall under the low-income countries group, while he remaining 19 or 39.58% countries fall under the high-income group. We go a step further and divide our sample into corrupted and less- orrupted countries (see Table 3.5). As a proxy measure of a level of cor- uption we use control of corruption (percentile) data provided by the Norld Governance Indicator, the World Bank. According to the database, hic “meacure cantires nercentions of the extent to which niblic nower jc
Table 3.5 Corrupted and less-corrupted OIC countries corruption, as well as ‘capture’ of the state by elites and private interests. Percentile rank indicates the country’s rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI.” We first use these data in their original form. After that, we use a median value of the sample to split data into two groups, corrupted and less-corrupted countries. The median value for the corruption measure is 27.18. Hence, a country is assigned value 0 (less-corrupted) if its average median for the study period is above 27.18 and value 1 (corrupted) for a country whose average median for the study period is below 27.18. In addition, if we look from a geographical point of view, OIC coun-
Table 3.5 Corrupted and less-corrupted OIC countries corruption, as well as ‘capture’ of the state by elites and private interests. Percentile rank indicates the country’s rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI.” We first use these data in their original form. After that, we use a median value of the sample to split data into two groups, corrupted and less-corrupted countries. The median value for the corruption measure is 27.18. Hence, a country is assigned value 0 (less-corrupted) if its average median for the study period is above 27.18 and value 1 (corrupted) for a country whose average median for the study period is below 27.18. In addition, if we look from a geographical point of view, OIC coun-
Table 3.6 Distribution of countries by geographical location 3.3.3.2 Descriptive Analysis: Overview Table 3.7 presents the summary statistics of our sample.'? The average GDP per capita (Gpc) growth rate is 2.26%, but there is wide cross- country variation in the sample with a low of around —12% to a high of around 18%. Surprisingly, the lowest (or rather the highest negative) GDP growth rate was recorded in 2009 by Kuwait, while the highest was recorded in 2013 by Sierra Leone.
Table 3.6 Distribution of countries by geographical location 3.3.3.2 Descriptive Analysis: Overview Table 3.7 presents the summary statistics of our sample.'? The average GDP per capita (Gpc) growth rate is 2.26%, but there is wide cross- country variation in the sample with a low of around —12% to a high of around 18%. Surprisingly, the lowest (or rather the highest negative) GDP growth rate was recorded in 2009 by Kuwait, while the highest was recorded in 2013 by Sierra Leone.
Table 3.7 Summary of descriptive statistics Notes: Gpc is the GDP per capita growth. CR5 is the 5-bank concentration ratio. CR3 is the 3-bank concentration ratio. HHI is the Herfindahl-Hirschman Index. LI is the Lerner index. BI is the Boone indicator. BNI is the bank noninterest income to total income ratio. BCI is the bank cost to income ratio. BNIM is the bank net interest margin. I is the inflation (GDP deflator). TO is the trade openness. ID is the institutional development. HC is the human capital accumulation. GCF is the gross capital formation. GS is the government size. FIN_p is the ratio of private credit to GDP FIN_| is the ratio of liquid liabilities to GDP
Table 3.7 Summary of descriptive statistics Notes: Gpc is the GDP per capita growth. CR5 is the 5-bank concentration ratio. CR3 is the 3-bank concentration ratio. HHI is the Herfindahl-Hirschman Index. LI is the Lerner index. BI is the Boone indicator. BNI is the bank noninterest income to total income ratio. BCI is the bank cost to income ratio. BNIM is the bank net interest margin. I is the inflation (GDP deflator). TO is the trade openness. ID is the institutional development. HC is the human capital accumulation. GCF is the gross capital formation. GS is the government size. FIN_p is the ratio of private credit to GDP FIN_| is the ratio of liquid liabilities to GDP
Notes: Gpc is the GDP per capita growth. CR5 is the 5-bank concentration ratio. CR3 is the 3-bank concentration ratio. HHI is the Herfindahl-Hirschman Index. LI is the Lerner index. BI is the Boone indicator. BNI is the bank noninterest income to total income ratio. BCI is the bank cost to income ratio. BNIM is the bank net interest margin. I is the inflation (GDP deflator). TO is the trade openness. ID is the institutional development. HC is the human capital accumulation. GCF is the gross capital formation. GS is the government size. FIN_p is the ratio of private credit to GDP FIN_1 is the ratio of liquid liabilities to GDP Table 3.8 Summary of descriptive statistics—low-income countries
Notes: Gpc is the GDP per capita growth. CR5 is the 5-bank concentration ratio. CR3 is the 3-bank concentration ratio. HHI is the Herfindahl-Hirschman Index. LI is the Lerner index. BI is the Boone indicator. BNI is the bank noninterest income to total income ratio. BCI is the bank cost to income ratio. BNIM is the bank net interest margin. I is the inflation (GDP deflator). TO is the trade openness. ID is the institutional development. HC is the human capital accumulation. GCF is the gross capital formation. GS is the government size. FIN_p is the ratio of private credit to GDP FIN_1 is the ratio of liquid liabilities to GDP Table 3.8 Summary of descriptive statistics—low-income countries
Table 3.9 Summary of descriptive statistics—corrupted countries Notes: Gpc is the GDP per capita growth. CR5 is the 5-bank concentration ratio. CR3 is the 3-bank concentration ratio. HHI is the Herfindahl-Hirschman Index. LI is the Lerner index. BI is the Boone indicator. BNI is the bank noninterest income to total income ratio. BCI is the bank cost to income ratio. BNIM is the bank net interest margin. I is the inflation (GDP deflator). TO is the trade openness. ID is the institutional development. HC is the human capital accumulation. GCF is the gross capital formation. GS is the government size. FIN_p is the ratio of private credit to GDP FIN_1 is the ratio of liquid liabilities to GDP
Table 3.9 Summary of descriptive statistics—corrupted countries Notes: Gpc is the GDP per capita growth. CR5 is the 5-bank concentration ratio. CR3 is the 3-bank concentration ratio. HHI is the Herfindahl-Hirschman Index. LI is the Lerner index. BI is the Boone indicator. BNI is the bank noninterest income to total income ratio. BCI is the bank cost to income ratio. BNIM is the bank net interest margin. I is the inflation (GDP deflator). TO is the trade openness. ID is the institutional development. HC is the human capital accumulation. GCF is the gross capital formation. GS is the government size. FIN_p is the ratio of private credit to GDP FIN_1 is the ratio of liquid liabilities to GDP
Notes: The table reports the correlation matrix of all regression variables. Gpc is the GDP per capita growth. CRS5 is the 5-bank concentration ratio. CR3 is the 3-bank concentration ratio. HHI is the Herfindahl-Hirschman Index. LI is the Lerner index. BI is the Boone indicator economic growth, measured by the annual growth rate of GDP per capita (Gpc). While doing so, we use sets of bank-specific and country-specific variables discussed earlier.!? In Table 3.11 models (1)-(6) are using CR5 as the main independent variable (Panel A), while models (7)—(12) are using LI as the main independent variable (Panel B). Le | ee: |, ee: |. aes
Notes: The table reports the correlation matrix of all regression variables. Gpc is the GDP per capita growth. CRS5 is the 5-bank concentration ratio. CR3 is the 3-bank concentration ratio. HHI is the Herfindahl-Hirschman Index. LI is the Lerner index. BI is the Boone indicator economic growth, measured by the annual growth rate of GDP per capita (Gpc). While doing so, we use sets of bank-specific and country-specific variables discussed earlier.!? In Table 3.11 models (1)-(6) are using CR5 as the main independent variable (Panel A), while models (7)—(12) are using LI as the main independent variable (Panel B). Le | ee: |, ee: |. aes
ble 3.10 Correlation between main dependent and independent variables
ble 3.10 Correlation between main dependent and independent variables
Table 3.11 Growth—concentration relationship—tinear model
Table 3.11 Growth—concentration relationship—tinear model
Notes: (i) Standard errors in brackets, (ii) * p<0.1, ** p<0.05, *** p<0.01
Notes: (i) Standard errors in brackets, (ii) * p<0.1, ** p<0.05, *** p<0.01
Fig. 3.3. Annual mean values of the market structure measure (HHI)—full sample
Fig. 3.3. Annual mean values of the market structure measure (HHI)—full sample
THE MARKET STRUCTURE MEAURES (CR5 & CR3)
THE MARKET STRUCTURE MEAURES (CR5 & CR3)
THE FINANCIAL DEVELOPMENT MEASURES (FIN_p & FIN_I)
THE FINANCIAL DEVELOPMENT MEASURES (FIN_p & FIN_I)
Table 3.12 Growth—concentration relationship—non-linear model—CR¢&
Table 3.12 Growth—concentration relationship—non-linear model—CR¢&
Notes: (i) Standard errors in brackets, (ii) * p<0.1, ** p<0.05, *** p<0.01
Notes: (i) Standard errors in brackets, (ii) * p<0.1, ** p<0.05, *** p<0.01
Notes: (i) Standard errors in brackets, (ii) * p<0.1, ** p<0.05, *** p<0.01
Notes: (i) Standard errors in brackets, (ii) * p<0.1, ** p<0.05, *** p<0.01
Table 3.14 Growth-concentration relationship—interaction term model
Table 3.14 Growth-concentration relationship—interaction term model
Notes: (i) Standard errors in brackets, (ii) * p<0.1, ** p<0.05, *** p<0.01
Notes: (i) Standard errors in brackets, (ii) * p<0.1, ** p<0.05, *** p<0.01
ple 3.15 Growth-concentration relationship—low income countries (dummy)
ple 3.15 Growth-concentration relationship—low income countries (dummy)
Table 3.16 Growth-concentration relationship—corrupted countries (dummy)
Table 3.16 Growth-concentration relationship—corrupted countries (dummy)
Notes: (i) Standard errors in brackets, (ii) * p<0.1, ** p<0.05, *** p<0.01
Notes: (i) Standard errors in brackets, (ii) * p<0.1, ** p<0.05, *** p<0.01
Table 4.2 Number of items retained Source: Researcher’s computation 2018
Table 4.2 Number of items retained Source: Researcher’s computation 2018
Table 4.3 Summary of regression models table
Table 4.3 Summary of regression models table
fable 5.1 A Structured approach to enhancing financial inclusion
fable 5.1 A Structured approach to enhancing financial inclusion
Table 5.4 Financial inclusion category scores ee E ——————_—_—_e_ee__ Secondly, the mean scores for the development program were relatively high with the highest and lowest being 1.00 and 00. Thirdly, the highest and lowest scores for the finance program is 1, and 00 with average remain- ing high. Fourthly, for the Islamic microfinance services dimension, the average rating is 0.34 with highest and lowest being 0.71 and 0.00. Fifth, the dimension of outreach has a common means at 0.32. Finally, in the
Table 5.4 Financial inclusion category scores ee E ——————_—_—_e_ee__ Secondly, the mean scores for the development program were relatively high with the highest and lowest being 1.00 and 00. Thirdly, the highest and lowest scores for the finance program is 1, and 00 with average remain- ing high. Fourthly, for the Islamic microfinance services dimension, the average rating is 0.34 with highest and lowest being 0.71 and 0.00. Fifth, the dimension of outreach has a common means at 0.32. Finally, in the
Table 5.5 Statistic descriptive
Table 5.5 Statistic descriptive
Note: * Indicate significance level at 10 percent Table 5.6 Pearson correlations
Note: * Indicate significance level at 10 percent Table 5.6 Pearson correlations
ble 5.7 Regression results of the financial inclusion disclosure 5.5.4.1 Hypothesis 1: ROE The analysis shows that ROE is not significantly associated with the disclo- sure level of financial inclusion. This implies that there is no relationship between ROE and BMT’s engagement and disclosure of financial inclu- sion activities. One possible reason for this insignificant relationship may be attributed to the high variation of ROE. Furthermore, this finding is consistent with findings from studies relating to a firm’s engagement and disclosure of social activities. Karim et al. argue that a firm’s exposure to social activities is unrelated to financial performance (ROE) because firms make decisions about disclosure of social activities in advance of reducing transaction costs. Intuitively, a larger level of total equity should have a positive impact on financial performance and disclosure, but it can also be argued that greater equity may have a negative effect on the financial per- formance of an MFI. While capital to assets ratio should have a positive effect on efficiency and financial performance, we do not have any a priori assumption about its impact on disclosure level.
ble 5.7 Regression results of the financial inclusion disclosure 5.5.4.1 Hypothesis 1: ROE The analysis shows that ROE is not significantly associated with the disclo- sure level of financial inclusion. This implies that there is no relationship between ROE and BMT’s engagement and disclosure of financial inclu- sion activities. One possible reason for this insignificant relationship may be attributed to the high variation of ROE. Furthermore, this finding is consistent with findings from studies relating to a firm’s engagement and disclosure of social activities. Karim et al. argue that a firm’s exposure to social activities is unrelated to financial performance (ROE) because firms make decisions about disclosure of social activities in advance of reducing transaction costs. Intuitively, a larger level of total equity should have a positive impact on financial performance and disclosure, but it can also be argued that greater equity may have a negative effect on the financial per- formance of an MFI. While capital to assets ratio should have a positive effect on efficiency and financial performance, we do not have any a priori assumption about its impact on disclosure level.
Source: Ascarya et al. (2015) and Bose et al. (2017)
Source: Ascarya et al. (2015) and Bose et al. (2017)
= + * 4 sO as to spell out their meanings appropriately. Reliability of the instrument was assessed using Cronbach’s Alpha. Reliability and KMO/Bartlett tests were conducted to examine the appro- priateness of items in measuring what they are expected to measure. In this study, Cronbach’s alpha was employed to test reliability, and mini- mum reliability levels of 0.50 and 0.60 have been suggested by scholars (Geraint et al. 2010; Creswell 2013; Krejcie and Morgan 1970). In this research, the reliability level was .883 Cronbach’s alpha. Table 6.1 shows the Cronbach alpha of this study.
= + * 4 sO as to spell out their meanings appropriately. Reliability of the instrument was assessed using Cronbach’s Alpha. Reliability and KMO/Bartlett tests were conducted to examine the appro- priateness of items in measuring what they are expected to measure. In this study, Cronbach’s alpha was employed to test reliability, and mini- mum reliability levels of 0.50 and 0.60 have been suggested by scholars (Geraint et al. 2010; Creswell 2013; Krejcie and Morgan 1970). In this research, the reliability level was .883 Cronbach’s alpha. Table 6.1 shows the Cronbach alpha of this study.
findings majority of the respondents (84%) stated that they know about the existence of the DIS in Nigeria, and 62.1% of the respondents are aware that the role of NDIC is to effectively guarantee to protect deposi- tors in the event of a bank’s failure. This is an indication that depositors are aware that the purpose of NDIC is protecting bank depositors in the event of the bank’s failure. However, many of the respondents (79.6%) are unaware that the corporation undertakes consumer protection initiatives of addressing depositors’ complaints against their banks. Similarly,
findings majority of the respondents (84%) stated that they know about the existence of the DIS in Nigeria, and 62.1% of the respondents are aware that the role of NDIC is to effectively guarantee to protect deposi- tors in the event of a bank’s failure. This is an indication that depositors are aware that the purpose of NDIC is protecting bank depositors in the event of the bank’s failure. However, many of the respondents (79.6%) are unaware that the corporation undertakes consumer protection initiatives of addressing depositors’ complaints against their banks. Similarly,
Table 6.4 Summary of regression analysis results on confidence in financial sys- tem as a result of DIS Source: Authors’ computation
Table 6.4 Summary of regression analysis results on confidence in financial sys- tem as a result of DIS Source: Authors’ computation
Table 6.5 Summary of regression analysis results on increased participation as a result of DIS
Table 6.5 Summary of regression analysis results on increased participation as a result of DIS
Source: Authors’ computation
Source: Authors’ computation
Source: calculations based on survey data (2018) Table 7.1 Distribution by gender
Source: calculations based on survey data (2018) Table 7.1 Distribution by gender
Table 7.2 Age of respondents
Table 7.2 Age of respondents
Source: calculations based on survey data (2018)
Source: calculations based on survey data (2018)
Fig. 7.3 Careers of the respondents. (Source: calculations based on survey data (2018)) Source: calculations based on survey data (2018)
Fig. 7.3 Careers of the respondents. (Source: calculations based on survey data (2018)) Source: calculations based on survey data (2018)
Table 7.3 Careers of the respondents
Table 7.3 Careers of the respondents
Table 7.5 Mobile telecommunication network expansion helped in increasing the services of farmers’ inclusion
Table 7.5 Mobile telecommunication network expansion helped in increasing the services of farmers’ inclusion
Table 7.7 The development of communications methods between farmers helped in reducing financing risks Source: calculations based on survey data (2018)
Table 7.7 The development of communications methods between farmers helped in reducing financing risks Source: calculations based on survey data (2018)
Table 7.8 The development of telecommunication services increased the out- reach of Islamic financial services among farmers
Table 7.8 The development of telecommunication services increased the out- reach of Islamic financial services among farmers
Source: calculations based on survey data (2018)
Source: calculations based on survey data (2018)
Table 7.10 Mobile phone subscribers (prepaid + postpaid): (Internet use postpaid) Source: Sudan National Telecommunication Authority annual report 2017
Table 7.10 Mobile phone subscribers (prepaid + postpaid): (Internet use postpaid) Source: Sudan National Telecommunication Authority annual report 2017
Table 7.11 Internet usage Source: Sudan National Telecommunication Authority annual report 2017
Table 7.11 Internet usage Source: Sudan National Telecommunication Authority annual report 2017
Table 7.12 Mobile SIMs registry statistics until December 31, 2017 Source: Sudan National Telecommunication authority annual report 2017
Table 7.12 Mobile SIMs registry statistics until December 31, 2017 Source: Sudan National Telecommunication authority annual report 2017
Source: Alfal Microfinance Company report 2018 Table 7.14 The growth of agricultural Islamic microfinance portfolios and farmer inclusion during the last three years
Source: Alfal Microfinance Company report 2018 Table 7.14 The growth of agricultural Islamic microfinance portfolios and farmer inclusion during the last three years
Fig. 9.1 Financial service providers
Fig. 9.1 Financial service providers
tig. 9.5 Opportunities for launching mobile microfinance
tig. 9.5 Opportunities for launching mobile microfinance
Fig. 9.6 Provision of mobile microfinance is beneficial
Fig. 9.6 Provision of mobile microfinance is beneficial
Fig. 9.12 Mobile microfinance contribute to financial inclusion
Fig. 9.12 Mobile microfinance contribute to financial inclusion
present state is weak and not capable of developing and launching mobile microfinance services (Fig. 9.13) (as opposed to the success stories of its regional peers) is a call to action for all stakeholders—executives, policy- makers, donors, and implementers—to work toward enhancing the oppor- tunities availed to microfinance in Sudan while simultaneously removing barriers that are currently preventing microfinance institutions (MFIs) from effectively engaging in poverty alleviation.
present state is weak and not capable of developing and launching mobile microfinance services (Fig. 9.13) (as opposed to the success stories of its regional peers) is a call to action for all stakeholders—executives, policy- makers, donors, and implementers—to work toward enhancing the oppor- tunities availed to microfinance in Sudan while simultaneously removing barriers that are currently preventing microfinance institutions (MFIs) from effectively engaging in poverty alleviation.
Source: SBP Islamic Banking Bulletin, December 2018
Source: SBP Islamic Banking Bulletin, December 2018
Table 10.2 Description of variables
Table 10.2 Description of variables
Table 10.3 Descriptive statistics In the first row of Table 10.3, the central tendency value of all variables is listed down. The mean value of GDP is 9,332,249 million rupees on average, broad money is 8,373,524, GFCF is 1,397,600, Islamic banks’ financing is 444,898.2 million rupees, labor force is 55.86889 million in numbers, and trade openness is 0.300565 million in ratio. Dispersion of data set is check out with standard deviation values. The normality of the data distribution is observed with the help of values of kurtosis and skewness. Skewness is a measure of the asymmetry of the probability distribution of a definite value arbitrary variable about its mean. Kurtosis is telling us about the degree of peakedness or smoothness of the unimoda frequency curve. Mesokurtic showed the normality of the data distribution. Further, Jarque-Bera test is also used to observe the normality of the data distribution. We observe that all values are there within normally defined standard benchmarks to proceed for the analysis further.
Table 10.3 Descriptive statistics In the first row of Table 10.3, the central tendency value of all variables is listed down. The mean value of GDP is 9,332,249 million rupees on average, broad money is 8,373,524, GFCF is 1,397,600, Islamic banks’ financing is 444,898.2 million rupees, labor force is 55.86889 million in numbers, and trade openness is 0.300565 million in ratio. Dispersion of data set is check out with standard deviation values. The normality of the data distribution is observed with the help of values of kurtosis and skewness. Skewness is a measure of the asymmetry of the probability distribution of a definite value arbitrary variable about its mean. Kurtosis is telling us about the degree of peakedness or smoothness of the unimoda frequency curve. Mesokurtic showed the normality of the data distribution. Further, Jarque-Bera test is also used to observe the normality of the data distribution. We observe that all values are there within normally defined standard benchmarks to proceed for the analysis further.
Table 10.4 ADF test results ane chataethel Ulli JL’ ow tii. We observe in the results of the ordinary least square (OLS) that there is a positive association between the Islamic banks’ financing and the eco- nomic growth in line with Furqani and Mulyany (2009), Majid and Kassim (2010), Abduh and Omar (2012), Manap et al. (2012), Kusuma and Mugorobin (2014), and Tabash and Dhankar (2014). Islamic financing increases the number of participants (financial inclusion) in financial mar- kets because Pakistan is with major Muslim population and they are inter- est sensitive. Hence, Islamic investment avenues provided them opportunity to park their abundant funds. This confidence has increased the overall economic growth of the country as empirical results supported our hypothesis of a positive significant association of the Islamic finance with the economic growth. Similarly, this result supports to promote
Table 10.4 ADF test results ane chataethel Ulli JL’ ow tii. We observe in the results of the ordinary least square (OLS) that there is a positive association between the Islamic banks’ financing and the eco- nomic growth in line with Furqani and Mulyany (2009), Majid and Kassim (2010), Abduh and Omar (2012), Manap et al. (2012), Kusuma and Mugorobin (2014), and Tabash and Dhankar (2014). Islamic financing increases the number of participants (financial inclusion) in financial mar- kets because Pakistan is with major Muslim population and they are inter- est sensitive. Hence, Islamic investment avenues provided them opportunity to park their abundant funds. This confidence has increased the overall economic growth of the country as empirical results supported our hypothesis of a positive significant association of the Islamic finance with the economic growth. Similarly, this result supports to promote
Islamic banking to achieve the economic growth as an objective of the UN agenda for SDGs. OSE ee ee ee Oe eee ee eee ee The GFCE (gross fixed capital formation) is positively significant ass ciated with economic growth of Pakistan. The growth is affected 0.2: due to the 1% change in GFCF. It means the GFCF plays a vital role determining the growth of the economy. It is notable that the GFCF used here as a proxy of capital. As per economics literature, investment h 1 positive impact on GDP growth. In fact, the developing countries fa severe problems in the formation of capital as people are unemployed offered employment but on compromised opportunities. Similarly, it al iffects savings adversely. As a result, there appeared a sharp decline mvestment and capital formation of the economy.
Islamic banking to achieve the economic growth as an objective of the UN agenda for SDGs. OSE ee ee ee Oe eee ee eee ee The GFCE (gross fixed capital formation) is positively significant ass ciated with economic growth of Pakistan. The growth is affected 0.2: due to the 1% change in GFCF. It means the GFCF plays a vital role determining the growth of the economy. It is notable that the GFCF used here as a proxy of capital. As per economics literature, investment h 1 positive impact on GDP growth. In fact, the developing countries fa severe problems in the formation of capital as people are unemployed offered employment but on compromised opportunities. Similarly, it al iffects savings adversely. As a result, there appeared a sharp decline mvestment and capital formation of the economy.
Table 10.6 Granger causality results Source: E-views’ results
Table 10.6 Granger causality results Source: E-views’ results
Source: World Development Indicators (WDI) and SBP APPENDIX: DATA OF VARIABLES
Source: World Development Indicators (WDI) and SBP APPENDIX: DATA OF VARIABLES
Fig. 11.1 Islamic banking industry branch network (2007-2012)
Fig. 11.1 Islamic banking industry branch network (2007-2012)
(Kaleem et al. 2016), Salam, and Istisna, offered by Islamic banks, as inde- pendent variables. The Islamic banking industry has shown tremendous growth in these areas (see Annexure). Hence, we used annual panel data ranging from 2004 to 2017, but it is an unbalanced data because different Islamic banks started their operations at different times. Data is collected from the bank’s financial statements (Table 11.2).
(Kaleem et al. 2016), Salam, and Istisna, offered by Islamic banks, as inde- pendent variables. The Islamic banking industry has shown tremendous growth in these areas (see Annexure). Hence, we used annual panel data ranging from 2004 to 2017, but it is an unbalanced data because different Islamic banks started their operations at different times. Data is collected from the bank’s financial statements (Table 11.2).
Method: panel EGLS (cross-section random effects) Islamic bank-specific variables like Salam, Murabaha, DM, Varah, and Istisna are significant and negatively related to poverty (unemployment). These results clearly explain that the Islamic banking industry is playing its positive role in reducing the unemployment level (Matarneh and Almanaseer 2015) in Pakistan. According to the findings, a 1% increase in DM financing will cause 6% reduction in the unemployment level. Diminishing Musharakah is a multidimensional mode through which machinery, equipment of factories, and productive material are financed. Moreover, Diminishing Musharakah is also a key element in boosting up the small and medium enterprises (SMEs). In this way, Islamic financial modes play a very important role in increasing employment and in digging up new entrepreneurial horizons because it is risk sharing-based mode, not risk transferring like conventional loans (Matarneh and Almanaseer 2015; Marzban and Asutay 2014; Bendjilali & Khan 1995). These results confirm the findings of Matarneh and Almanaseer (2015), Marzban and Asutay (2014), and Bendjilali & Khan (1995) that DM may increase the job opportunities. TL ek eh ef te BE Ol] Teton dd WMhoeseehehn nee nee btek
Method: panel EGLS (cross-section random effects) Islamic bank-specific variables like Salam, Murabaha, DM, Varah, and Istisna are significant and negatively related to poverty (unemployment). These results clearly explain that the Islamic banking industry is playing its positive role in reducing the unemployment level (Matarneh and Almanaseer 2015) in Pakistan. According to the findings, a 1% increase in DM financing will cause 6% reduction in the unemployment level. Diminishing Musharakah is a multidimensional mode through which machinery, equipment of factories, and productive material are financed. Moreover, Diminishing Musharakah is also a key element in boosting up the small and medium enterprises (SMEs). In this way, Islamic financial modes play a very important role in increasing employment and in digging up new entrepreneurial horizons because it is risk sharing-based mode, not risk transferring like conventional loans (Matarneh and Almanaseer 2015; Marzban and Asutay 2014; Bendjilali & Khan 1995). These results confirm the findings of Matarneh and Almanaseer (2015), Marzban and Asutay (2014), and Bendjilali & Khan (1995) that DM may increase the job opportunities. TL ek eh ef te BE Ol] Teton dd WMhoeseehehn nee nee btek
Method: panel EGLS (cross-section random effects) Islamic bank-specific variables like Salam, DM, and Jjarah are signifi- cant and positively related to economic growth of Pakistan at the level of 5%, while Istisna is significant at 10% level. These results confirm the find- ings of Kalim et al. (2016), Abduh and Omar (2012). Murabaha is nega- tively related to economic growth as found by Kalim et al. (2016). Furqani and Mulyany (2009) and Goaied and Seifallah (2010) also concluded that the development of the Islamic banking industry is playing an important role in economic growth. All these Islamic modes of financings are real asset-based modes and boosting GDP by real productive economic activ- ity in the economy. Out of the macroeconomic indicators, inflation remained highly insignificant, while FDI and EXPGR are found significant and positively related to economic growth (Abdullah et al. 2015; Muntah et al. 2015; Naseer 2013; Tiwari and Mutascu 2011; Chakraborty and
Method: panel EGLS (cross-section random effects) Islamic bank-specific variables like Salam, DM, and Jjarah are signifi- cant and positively related to economic growth of Pakistan at the level of 5%, while Istisna is significant at 10% level. These results confirm the find- ings of Kalim et al. (2016), Abduh and Omar (2012). Murabaha is nega- tively related to economic growth as found by Kalim et al. (2016). Furqani and Mulyany (2009) and Goaied and Seifallah (2010) also concluded that the development of the Islamic banking industry is playing an important role in economic growth. All these Islamic modes of financings are real asset-based modes and boosting GDP by real productive economic activ- ity in the economy. Out of the macroeconomic indicators, inflation remained highly insignificant, while FDI and EXPGR are found significant and positively related to economic growth (Abdullah et al. 2015; Muntah et al. 2015; Naseer 2013; Tiwari and Mutascu 2011; Chakraborty and
Table 12.1 Cooperatives in Indonesia Source: Ministry of Cooperative and Micro, Small, Medium Enterprises (2017) Islamic profit and loss sharing compared to conventional institutions such as risk sharing, wkhuwah (brotherhood) and ta’awun (helping each other). Hence, BMT has dual functions comprising social function and profit function in improving financial inclusion in Indonesia (Ismail 2014). According to the Ministry of Cooperative and Micro, Small and Medium Enterprise, there are 152,714 cooperatives in Indonesia. It can be seen in Table 12.1.
Table 12.1 Cooperatives in Indonesia Source: Ministry of Cooperative and Micro, Small, Medium Enterprises (2017) Islamic profit and loss sharing compared to conventional institutions such as risk sharing, wkhuwah (brotherhood) and ta’awun (helping each other). Hence, BMT has dual functions comprising social function and profit function in improving financial inclusion in Indonesia (Ismail 2014). According to the Ministry of Cooperative and Micro, Small and Medium Enterprise, there are 152,714 cooperatives in Indonesia. It can be seen in Table 12.1.
Fig. 12.1 Initial model of alleviating poverty through Islamic microfinance
Fig. 12.1 Initial model of alleviating poverty through Islamic microfinance
Table 12.2 Characteristics of respondents have positive impact with financial education 0.046, Islamic values 0.564 and financial policies 0.348.
Table 12.2 Characteristics of respondents have positive impact with financial education 0.046, Islamic values 0.564 and financial policies 0.348.
Table 12.4 Path coefficient and R-square Table 12.5 Construct reliability and validity
Table 12.4 Path coefficient and R-square Table 12.5 Construct reliability and validity
Composite reliability AVE Cronbach’s alpha Fit/unfit latent exogenous variables of the model are simultaneously significant affect latent exogenous variable.
Composite reliability AVE Cronbach’s alpha Fit/unfit latent exogenous variables of the model are simultaneously significant affect latent exogenous variable.
Table 13.1 Demographic statistics
Table 13.1 Demographic statistics
Fig. 13.2 Factors influencing the adoption of M-banking based on microfinance ustomer perspectives. (Source: Field Data 2015)
Fig. 13.2 Factors influencing the adoption of M-banking based on microfinance ustomer perspectives. (Source: Field Data 2015)
the members are staff and students of I1UM, they can hold meetings at any time at their conveniences. This also allows them to keep abreast with the activities of the project. Thirdly, the structure is cost saving to the operation of the project. The advisors do not receive any remuneration. Instead, they allocate part of their man-hours to the service of the com- munities. Only the four executives and the volunteers are paid modest monthly allowances for their services. Much of what they do is translated into community services.
the members are staff and students of I1UM, they can hold meetings at any time at their conveniences. This also allows them to keep abreast with the activities of the project. Thirdly, the structure is cost saving to the operation of the project. The advisors do not receive any remuneration. Instead, they allocate part of their man-hours to the service of the com- munities. Only the four executives and the volunteers are paid modest monthly allowances for their services. Much of what they do is translated into community services.
Fig. 14.5 IIUM and CIMB Islamic officials at the soft launch. From left is brother Zaim, Chairman of the 1-Taajir Executives; Encik Ken Mohd Faiz, CIMB Islamic Group Head of CSR; Prof. Dr. Aslam and Assoc. Prof. Dr. Mustafa, both i-Taajir advisors; Assoc. Prof. Dr. Mohd Nahar from Economics Department; and Encik Asrul Dahari, KENMS Deputy Dean of Student Affairs. In the photo on the right, in the middle is Prof. Dr. Norma Bt Md Saad, the third i-Taajir advisor, handing over a lucky draw gift to the winner. The lucky draw was conducted between each motivation session Apart from the official launch, the event included motivation session: to encourage the participants and instill in them the spirit of entrepreneur ship. They were also reminded about integrity in business and upholding moral principles and ethical values. Lucky draws were conducted fron time to time. At the end of the motivation sessions, the student volunteer: conducted interview with the participants for ice breaking, establishing rapport and to know more about their business skills, financial and socia status. This additional information would later be triangulated with th« information the participants provided earlier in the household survey (Figs. 14.6, 14.7, and 14.8).
Fig. 14.5 IIUM and CIMB Islamic officials at the soft launch. From left is brother Zaim, Chairman of the 1-Taajir Executives; Encik Ken Mohd Faiz, CIMB Islamic Group Head of CSR; Prof. Dr. Aslam and Assoc. Prof. Dr. Mustafa, both i-Taajir advisors; Assoc. Prof. Dr. Mohd Nahar from Economics Department; and Encik Asrul Dahari, KENMS Deputy Dean of Student Affairs. In the photo on the right, in the middle is Prof. Dr. Norma Bt Md Saad, the third i-Taajir advisor, handing over a lucky draw gift to the winner. The lucky draw was conducted between each motivation session Apart from the official launch, the event included motivation session: to encourage the participants and instill in them the spirit of entrepreneur ship. They were also reminded about integrity in business and upholding moral principles and ethical values. Lucky draws were conducted fron time to time. At the end of the motivation sessions, the student volunteer: conducted interview with the participants for ice breaking, establishing rapport and to know more about their business skills, financial and socia status. This additional information would later be triangulated with th« information the participants provided earlier in the household survey (Figs. 14.6, 14.7, and 14.8).
Fig. 14.6 Some of the participants at the soft launch
Fig. 14.6 Some of the participants at the soft launch
The training is done at weekends covering two half-day sessions. Typically the topics covered by the professional facilitators are introduction to business, financial and accounting management, morality [akhlaq] and business ethics and promotion and sales. Each topic has two parts, theory and practice/applied. Other forms of trainings are later provided by the student volunteers to the participants on a one-to-one basis (Fig. 14.9).
The training is done at weekends covering two half-day sessions. Typically the topics covered by the professional facilitators are introduction to business, financial and accounting management, morality [akhlaq] and business ethics and promotion and sales. Each topic has two parts, theory and practice/applied. Other forms of trainings are later provided by the student volunteers to the participants on a one-to-one basis (Fig. 14.9).
Table 15.1 Distribution of the respondents based on their gender, age, marita status, employment status and vocation ULI Al&o Uldl UILTL lo d dll LEUDICORINAUUTE OL VOUT SULIUero Id UIC CULO” tion of the customers of Tijarah Microfinance Bank Ltd (TMBL). Age is the length of time that somebody or something has existed, usu- ally expressed in years. For the purpose of this study, age describes the years a respondent lives from birth to the present time of the study. The result in Table 15.1 shows that majority (54.40%) of the respondents were within their active service age of between 20 and 40 years. While 27.70% were below 20 years, 13.40% and 4.50% of the respondents were 41-50 and 51 years and above, respectively. The result further indicates that there
Table 15.1 Distribution of the respondents based on their gender, age, marita status, employment status and vocation ULI Al&o Uldl UILTL lo d dll LEUDICORINAUUTE OL VOUT SULIUero Id UIC CULO” tion of the customers of Tijarah Microfinance Bank Ltd (TMBL). Age is the length of time that somebody or something has existed, usu- ally expressed in years. For the purpose of this study, age describes the years a respondent lives from birth to the present time of the study. The result in Table 15.1 shows that majority (54.40%) of the respondents were within their active service age of between 20 and 40 years. While 27.70% were below 20 years, 13.40% and 4.50% of the respondents were 41-50 and 51 years and above, respectively. The result further indicates that there
Table 15.2 Pearson correlation
Table 15.2 Pearson correlation
Table 15.3. Model summary
Table 15.3. Model summary
Table 15.4 Regression coefficients “Dependent variable: financial inclusion
Table 15.4 Regression coefficients “Dependent variable: financial inclusion
Related topics:
BusinessIslamic LawDevelopment EconomicsIslamic EconomicsSustainable Production and ConsumptionSocial EntrepreneurshipIslamic Contemporary StudiesIslamic FinanceFinancial InclusionShariah issues in takaful
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