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Descriptive Statistics of Number of Loans of Families with Student Loans


Table 4.3: Gender statistics of borrowers



Sex

Quantity

Percentage

Male

51

39.2

Female

79

60.8

Total

130

100

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Descriptive Statistics of Number of Loans of Families with Student Loans

iii) Ethnicity

According to the survey results, the majority of people who take out student loans are Kinh ethnic group, the remaining ethnic groups are Stieng, Khmer, and Monong. In Binh Phuoc province, ethnic minorities live in remote areas and often have to encourage their children to go to school. The number of families with children who go to school after high school is rare or they are studying but drop out because they do not have the conditions to continue studying at intermediate, college, or university.

According to the survey, Kinh people account for 80% of the 130 borrowers surveyed. Meanwhile, ethnic minorities account for 20%, equivalent to 26 people.

Table 4.4: Descriptive statistics of ethnicity



Nation

Quantity

Percentage

Terrible

104

80

Other ethnic groups

26

20

Total

130

100

iv) Education level

The survey results show that the educational level of the borrowers of student loans is not high, of which 20 people have not attended school, accounting for 15.4%, 47.7% of those who have studied up to primary school, equivalent to 62 people, 34 people who have studied up to secondary school, equivalent to 26.2%, and 14 people who have studied up to high school, equivalent to 10.8%. No one has studied at a higher level.


Table 4.5 Descriptive statistics of educational level

Education level

Quantity

Percentage

Not been to school

20

15.4

To level 1

62

47.7

To level 2

34

26.2

To level 3

14

10.8

Total

130

100.0

v) Occupation

The majority of people who took out student loans in this survey participated in small and medium-sized agricultural production, the rest worked in some jobs such as rubber tapping workers, hired workers, small traders, teachers, etc. The survey results showed that the number of people working in agricultural production was 47 people, equivalent to 36.20%, and the number of people working in other jobs was 63.80%.

Table 4.6: Occupational descriptive statistics

Job

Quantity

Percentage

Agricultural production

47

36.2

Other occupations

83

63.8

Total

130

100

vi) Number of employees

The number of workers in a family with student loans in the survey ranged from 1 to 6 people. The average number of workers in a family with student loans was 2.61 people, with a standard deviation of 1.103. The majority of families had 2 workers. A few had more than 6 workers.


Table 4.7 Descriptive statistics of number of employees


Number of observations

Average value

Standard deviation

Small value

best

Great value

best

130

2.61

1.103

1

6

vii) Number of dependents

Overall, the average number of dependents in a family with a student loan is 3, the highest number of dependents is 7, the lowest is 1, the standard deviation is 1.389. The majority of dependents in the sample is 2.


Table 4.8: Descriptive statistics of number of dependents


Number of observations

Average value

Standard deviation

Small value

best

Great value

best

130

3.01

1,389

1

7

ix) Number of sources of income

According to the survey, the average total income of families with student loans is 2.44, the highest number of income sources is 7, the lowest is 1, the standard deviation is 0.965. The number of income sources is 2, accounting for the majority.

Table 4.9 Descriptive statistics of income sources

Number of observations

Average value

Standard deviation

Small value

best

Great value

best

130

2.44

0.965

1

5

ix) Number of loans

According to the survey results, the average number of loans is 1.40, the family with the highest number of loans is 3, the lowest is 1 (which is a student loan). A family with more than 1 loan may have more than 1 student or the loan is for another purpose.


Table 4.10 Descriptive statistics of the number of loans for families with student loans


Number of observations

Average value

Standard deviation

Small value

best

Great value

best

130

1.4

0.689

1

3


x) Saving purpose

According to the survey results, the number of families with student loans who deposit savings to pay off student loans at the Binh Phuoc Provincial Social Policy Bank is 79 observations, accounting for 60% of the total 130 observations. The remaining number either do not deposit savings or deposit savings for other purposes, accounting for 39%.

Table 4.11: Descriptive statistics of savings purposes


Saving purpose

Quantity

Percentage

Save to pay off debt

51

39.2

Save for other purposes

79

60.8

Total

130

100

xi) Dependent variable debt repayment ability

According to the survey, the number of observations with the ability to repay debt accounts for a larger proportion than the number of observations with the ability to repay debt.

Saving purpose

Quantity

Percentage

Inability to pay debt

69

53.1

able to pay debt

61

46.9

Total

130

100

Table 4.12: Debt repayment ability survey results


Thus, through a survey of 130 households borrowing money for students to go to school, the data is summarized in the table below:


Table 4.13. : Summary of some characteristics of the survey sample



Target

Number of households

Percent

Total households

130

100%

Age of household head



In working age

114

88%

Out of working age

16

12%

Gender of household head



Male

51

39%

Female

79

61%

Nation

Kinh ethnic group


104


80%

Stieng and other ethnic groups

26

20%

Education level of household head

Not allowed to go to school


20


15%

Study to primary school

62

48%

Study to secondary school

34

26%

Study to high school

14

11%

Job



Agricultural production

47

36%

Other professions

83

64%

Number of employees



1-2 people

74

57%

3-5 people

44

34%

Over 5 people

12

9%

Number of dependents



1-2 people

61

47%

3-5 people

62

48%

Over 5 people

7

5%

Number of income sources

From 1-2


84


65%

From 3-5

46

35%

Number of loans



1

93

72%

2

22

18%

3

15

11%

Saving purpose

Debt repayment purpose


51


53%

Other purposes

79

47%


CHAPTER 4 SUMMARY


Chapter 4 presents the analytical framework and research model of the topic. The analytical framework provides 10 factors that may affect the ability to repay student loans at the Binh Phuoc Provincial Policy Bank Branch, including: age, gender, ethnicity, education level, occupation, number of employees, number of dependents, number of income sources, number of loans, and savings purposes.

With the random sampling method, the sample size was 130 observations, primary data was taken from direct interviews with borrowers, secondary data was taken from the database of the social policy bank of Binh Phuoc province. The results of data collection and descriptive statistics of the variables are presented in this chapter.


CHAPTER 5: RESEARCH RESULTS AND POLICY SUGGESTIONS


5.1. Necessary tests of regression models

Before presenting the regression results, the author performed some necessary tests to ensure that the regression results from the research model have the highest accuracy. The results from the following tests are extracted from the results obtained from SPSS 23.0 software with the collected data.

5.1.1. Pearson correlation test

Pearson correlation test is used to examine the linear relationship between explanatory variables and dependent variables. If the correlation coefficient is large between explanatory variables, then multicollinearity phenomenon should be considered.

Hypothesis H 0 : The correlation coefficient is 0. Therefore, if Sig is less than 5%, it can be concluded that the two variables are correlated with each other. The larger the correlation coefficient, the stronger the correlation. Conversely, if Sig is greater than 5%, the two variables are not correlated with each other.

From the test results presented in Table 5.1, the independent variables number of income sources and number of employees are closely correlated with each other, the correlation coefficient is 0.78, so in the regression process, it is necessary to pay attention to the phenomenon of multicollinearity between these two independent variables. In addition, there are independent variables education level correlated with occupation and number of income sources, savings purpose and occupation are correlated with each other, however, the correlation of these variables is at a low level.

The autocorrelation test also shows that at the 5% significance level, independent variables such as education level, occupation, number of employees, number of dependents, number of income sources, number of loans and savings purpose have a correlation with the dependent variable of debt repayment ability. However, the correlation coefficient is not large. The test results are shown in Table 5.1.

Table 5.1 Results of autocorrelation test between independent variables and dependent variables


Correlations




age


news


people


virgin

_study_and_eat

listen

p


so_la

_VND


so_sleep_my_wife_

medicine


number_of_people

app


so_m on_v

yes


detail

_check


test_person

no

age

Pearson Correlation

1

-.051

.040

-.129

.059

.064

-.076

.009

-.026

-.022

.032


Sig. (2-tailed)


.565

.651

.144

.503

.468

.389

.923

.771

.806

.721


N

130

130

130

130

130

130

130

130

130

130

130

sex

Pearson Correlation


-.051


1


.110


.046

-

.178

*


-.086


.087


.060


-.032


.129


-.061


Sig. (2-tailed)

.565


.212

.602

.042

.331

.327

.500

.717

.143

.491


N

130

130

130

130

130

130

130

130

130

130

130

ethnicity

Pearson Correlation


.040


.110


1


-.121

-

.016


.108


.011


.052


.017


.008


-.085


Sig. (2-tailed)

.651

.212


.172

.857

.219

.900

.557

.849

.929

.338


N

130

130

130

130

130

130

130

130

130

130

130

literacy

Pearson Correlation


-.129


.046


-.121


1

-

.208

*


.240 **


-.151


.247 **


-.115


.045


.417 **


Sig. (2-tailed)

.144

.602

.172


.018

.006

.087

.005

.194

.609

.000


N

130

130

130

130

130

130

130

130

130

130

130

career

Pearson Correlation

.059

-.178 *

-.016

-.208 *

1

.006

-.004

-.043

.051

.178 *

-.226 **


Sig. (2-tailed)

.503

.042

.857

.018


.942

.962

.623

.562

.042

.010


N

130

130

130

130

130

130

130

130

130

130

130

labor_number

Pearson Correlation

.064

-.086

.108

.240 **

.006

1

.189 *

.782 **

-.118

.029

.378 **


Sig. (2-tailed)

.468

.331

.219

.006

.942


.031

.000

.180

.747

.000


N

130

130

130

130

130

130

130

130

130

130

130

number_of_women

_medicine

Pearson Correlation


-.076


.087


.011


-.151

-

.004


.189 *


1


.090


.070


-.098


-.261 **


Sig. (2-tailed)

.389

.327

.900

.087

.962

.031


.308

.431

.267

.003


N

130

130

130

130

130

130

130

130

130

130

130

number_of_receipts

_enter

Pearson Correlation


.009


.060


.052


.247 **

-

.043


.782 **


.090


1


-.056


.022


.421 **


Sig. (2-tailed)

.923

.500

.557

.005

.623

.000

.308


.527

.801

.000


N

130

130

130

130

130

130

130

130

130

130

130

number_of_clothes

Pearson Correlation

-.026

-.032

.017

-.115

.051

-.118

.070

-.056

1

.147

-.368 **


Sig. (2-tailed)

.771

.717

.849

.194

.562

.180

.431

.527


.095

.000


N

130

130

130

130

130

130

130

130

130

130

130

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