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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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

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

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

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

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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