A Study of Individual Financial Behavior and Financial Literacy under the Development of Digital Financial Technology

The trend of digital transformation of the world economy, digital finance process accelerated, all-round digital socio-economic form gradually formed, and derived from the corresponding digital financial services ecosystem, but also inseparable from the ecological center of the population groups [1–3]. The results of the digital economy have gradually appeared, producers, financiers, investors, consumers in the digital economy, boosted by the industrial transformation, enterprise relief, confidence boost, etc. injected into the “cardiotonic agent”, digital finance and the application of the scene accelerated into the economic life [4–6].

In the accelerated process of digital financial ecology, the symbiosis and integration of digital literacy and financial literacy can better adapt to the practical application under the complex association of economic system, financial system, and science and technology system [7–8]. The acceleration of the digital financial process has brought about new social problems, in which the low level of technology use by institutions and individual participants and the lack of solutions for digital financial scenarios have triggered potential development dilemmas, leading to inefficient financial services, causing financial integrity failures, resulting in limited financial sharing, and causing financial ethical disorders, which in turn have impeded the sustainability of digital financial development [9–12]. Therefore, the symbiosis and integration of digital literacy and financial literacy are needed behind the digital economy, and it is necessary to cultivate and improve the digital financial literacy of institutions and individuals in order to realize the standardized, sustained, and healthy development of digital integration [13–14]. Digital financial literacy is a critical financial application literacy rooted in information technology, the new needs of the development of the digital economy, including digital infrastructure, digital financial risk awareness, digital financial risk management capabilities, as well as including digital financial consumer rights and responsibilities [15–17].

At the same time, modern financial technology in the rapid development of the rapid penetration and integration into daily life, so that the individual human being itself and the survival of human beings and the way of life changes [18–19]. Relying on this, the quality of life of contemporary human beings has been greatly improved, material needs and spiritual needs have been more satisfied, the interaction between human beings and financial technology has become more and more close, and the impact of financial technology on human society and natural development has become more and more profound. The current era of high technology has gradually evolved into an era full of desires and but lack of ideological guidance, because modern financial technology has profoundly changed the traditional values, moral concepts, legal concepts, authority and sense of responsibility, and more and more subjective factors have sprung up to satisfy the interests of the individual, which threaten people’s sustained and healthy survival and development [20–22]. We all know that the combination of personal financial behavior and modern financial technology is imperative, but there are certain destructive effects that come with it. We should accept the resulting risks and potential pitfalls and face up to their test of human values. It is especially important to study the contradictions between the two and put forward countermeasures and suggestions to promote good interaction between the two in order to achieve sustainable and healthy development [23–25].

The article firstly elaborates on the factor analysis model and studies the basic ideological characteristics and arithmetic steps of the model. Then, taking the population of province A as an example, a questionnaire survey was conducted to measure the level of personal financial literacy of its citizens, and factor analysis was applied to measure the level of personal financial literacy. Then the probit model is constructed to carry out regression analysis of personal financial literacy and financial behaviors under the support of digital financial technology development, and the four financial behaviors of borrowing, wealth management, insurance and financial consulting are used as the entry point to explore the influence of personal financial literacy level on financial behaviors, and the robustness test and the analysis of heterogeneity of financial behaviors are also carried out.

2

Factor analysis model

2.1

Basic Ideas and Characteristics of Factor Analysis

Factor analysis is a promotion of principal component analysis, which is also a multivariate statistical analysis method from the study of the dependency relationship within the absorption PR matrix, to reduce some variables with intricate relationships to a few integrated variables. Factor analysis is a statistical method for simplification and dimensionality reduction of data. When faced with many intrinsically correlated variables, factor analysis intends to use a few random variables to describe the underlying structure embodied by many variables.

Factor analysis explores the underlying structure in the observed data by examining the internal dependencies among the many variables and representing their underlying data structure with a few dummy variables. These dummy variables can reflect the main information of the original many variables. The original variables are observable explicit variables, while the dummy variables are unobservable latent variables called factors.

The basic idea of factor analysis is to group the original variables according to the magnitude of the correlation, so that the correlation between variables within the same group is low. Each group of variables represents a basic structure and is represented by an unobservable composite variable, which becomes the common factor [26–27].

Characterization of factor analysis:

First, the number of factor variables is much smaller than the number of original indicator variables, and the analysis of factor variables can reduce the computational workload: second, factor variables are not only the trade-offs of the original variables, but also the re-combination of the original variables based on the information of the original variables, which can reflect most of the information of the original variables. Thirdly, factor variables do not have linear correlation, which is more convenient for analyzing variables. Fourth, factor variables have named interpretability, i.e., the variable is a comprehensive reflection of the information of some original variables.

2.2

R-type factor analysis model

Let X = (X₁, X₂, …, X_p) be an observed random vector and F = (F₁, F₂, … F_m) be an unobservable vector. Then there are: 1 $X_{i} = μ + α_{i 1} F_{1} + \dots + α_{i m} F_{m} + ε_{i}$ in matrix shorthand: 2 $X - μ = Λ F + ε$ where F = (F₁, F₂, …F_m) are called common factors and are unobservable variables and their coefficients are called factor loadings.

ε = (ε₁, …, ε_p)′ is a special factor, a part that cannot be included by the first m common factors [28].

and the assumptions are satisfied: 1)

m ≤ p.

2)

cov(F, ε) = 0.

3)

var(F) = 1_m, $var (ε) = d i a g (σ_{1}^{2}, \dots, σ_{p}^{2})$ .

Call F_i the i th common factor and α_ij the factor loading. F_i are independent of each other and have variance 1.

2.3

Properties of factor analytic models

Decomposition of the covariance of the original variable –X. According to the R -type factor analysis model, the covariance array of the original observed variables can be obtained as: 3 $\begin{array}{l} V a r (X) & = V a r (X - μ) = E (A F + ε) (A F + ε)' \\ = A E (F F') A' + E (ε ε') \\ = A V a r (F) A' + V a r (ε) = A A' + D \end{array}$

Factor analysis models have the following properties: 1)

Factor analysis models are not affected by the unit of measure.

2)

Factor loadings are not unique.

2.4

Significance of parameters in factor analysis models

1)

Statistical significance of factor loadings

Known factor analysis model 4 $X_{i} = α_{i 1} F_{1} + \dots + α_{i n} F_{m} + ε_{i}, i = 1, \dots, p$

Then multiplying F_j, at both ends of the above equation at the same time gives: 5 $E (X_{i} F_{j}) = \sum_{K = 1}^{m} α_{i k} E (F_{k} F_{j}) = \sum_{K = 1}^{m} α_{i k} r (F_{k} F_{j}) = α_{i j}$

Since F_k, F_j are irrelevant and (FF_jF₁ = 1) is α_ij = r_{X_i},_{F_j}.

indicates that the factor loading α_ij is the correlation coefficient of the i th variable with the j th common factor, i.e., X_i depends on the weight of F_j. The greater the absolute value of the factor loadings, the closer the correlation. However, for historical reasons, psychologists call this a loading, which indicates the relative importance of the i th variable on the j th common factor.

2)

Statistical significance of commonality of variables

The common degree of the variable X_i is the sum of the squares of the elements of row i in the factor loading matrix A, i.e: 6 $h_{i}^{2} = \sum_{j = 1}^{m} α_{i j}^{2} (i = 1, \dots, p)$

Finding the variance on both sides of the above equation gives: 7 $\begin{array}{l} var (X_{i}) & = \sum_{j = 1}^{m} var (α_{i j} F_{j}) + var (ε_{i}) \\ = \sum α_{i j}^{2} var (F_{j}) + σ_{i}^{2} = \sum_{j = 1}^{m} α_{i j}^{2} + σ_{i}^{2} = h_{i}^{2} + σ_{i}^{2} \end{array}$

Since X_i is standardized, we have: $1 = h_{i}^{2} + σ_{i}^{2}$ . This shows that the variance of the variable X_i consists of two parts: The first component is the commonality $h_{i}^{2}$ , which characterizes the contribution of all common factors to the total variance of the variable X_i. The first component AA is the commonality, which portrays the contribution of all common factors to the total variance of the variable X_i. The second component is $σ_{i}^{2}$ , who is the special factor variance and is only associated with changes in the variable X_i. Obviously the smaller the value of the special factor variance, the more it indicates that the factor analysis model has a strong and effective explanatory power, and the nature of the transformation from the space of the original variables to the space of the common factors is good.

3)

Statistical significance of the variance contribution of the common factor F_j

The sum of squares of the columns in the factor loading matrix is denoted: 8 $S_{j} = \sum_{i = 1}^{p} α_{i j}^{2}, j = 1, \dots, p$

Call S_j the contribution of the common factor F_j to X_i, which represents the sum of the variance contributions provided by the same common factor F_j to each variable and is a measure of the relative importance of the common factor.

2.5

Basic steps in factor analysis

1)

Selection of variables for analysis

Selecting variables by qualitative and quantitative analysis, the prerequisite for factor analysis is a strong correlation between the observed variables, because if there is no correlation between the variables or the correlation is small, they will not have a shared factor, so there should be a strong correlation between the original variables.

2)

Correlation analysis

It is important to examine whether there is a strong correlation between the original variables and whether they are suitable for factor analysis. Because one of the main tasks of factor analysis is to extract the part of the original variables with overlapping information as factors, and finally achieve the goal of reducing the number of variables. Therefore, it is necessary for there to be a strong correlation between the original variables. Otherwise, if the original variables are independent of each other and there is no overlap of information, there is no need for comprehensive factor analysis.

3)

Extracting common factors

This step of extracting common factors is to determine the method of factorization and the number of factors. It needs to be determined according to the research design program or related factors and empirical knowledge. The number of factors to determine can be based on the size of the factor variance. Only those factors with variance greater than 1 (or eigenvalues greater than 1) are taken, as the contribution of factors with variance less than 1 may be small. The factors are chosen based on their cumulative variance contribution, which is commonly thought to be 80% to meet the requirements.

4)

Factorization

This step makes each original variable closely related to each other in as few factors as possible by coordinate transformation, so that the actual meaning of the factor solution is easier to interpret, and assigns a meaningful name to each potential factor.

5)

Calculate factor scores

To find out the factor score of each sample, with the factor score value, you can use these factors in many analyses, such as the factor score to do the cluster analysis of the variables, to do regression analysis of the regressors.

The basic process of factor analysis is shown in Figure 1.

3

Financial Literacy Measurement and Analysis of Influential Factors

3.1

Sample Selection and Questionnaire Design

3.1.1

Sample Selection

The data in this paper originates from the research conducted in Province A on the impact of personal financial literacy on their participation in family finance, the sample selection process takes into account the consistency of personal training goals and patterns due to the development of Internet information technology, the individual differences between provinces and municipalities are relatively small, in order to investigate the convenience of the survey, this paper selects Province A as a sample province to randomly distribute questionnaires in the form of the network (including emails, WeChat and QQ links as well as web page links). The intelligence of the electronic questionnaire effectively avoids the problems of clerical errors when filling out the questionnaire manually and repetitive filling out of the questionnaire during the collection process, which is conducive to saving the time for data collection, saving money and improving the efficiency of the survey. The target of the survey is citizens of Province A (under 60 years old), and the research involves information on the basic characteristics of individuals, the financial literacy status of individuals (including three dimensions of financial awareness, financial knowledge, and financial capability), and information on the behavior and willingness to participate in personal household financial management, totaling three parts. A total of 800 questionnaires were distributed in the research, and 800 valid questionnaires were recovered, with an effective rate of 100%. All data processing work in this paper utilizes SPSS22.0 and STATA14.0.

3.1.2

Questionnaire design

The questionnaire on personal financial literacy and its impact on personal financial behavior has three parts. The first part is basic personal information, the second part is the level of personal financial literacy, and the third part is personal financial behavior and willingness.

The level of personal financial literacy may have an important impact on their participation in personal financial behavior, and the level of financial literacy varies with different individual characteristics, so the impact of individual characteristics should be fully taken into account when measuring the level of personal financial literacy and examining the impact of their financial literacy on their personal financial behavior. This part of the questionnaire is designed to include the individual’s age, gender, nature of household registration, whether he/she has experience in financial management, the highest education level of his/her parents, job, monthly income, whether his/her parents have experience in financial management, the individual’s personal attention to financial news, whether he/she personally prefers financial related news and information, whether he/she has relatives or friends working in securities companies, whether he/she has relatives or friends working in banks, whether he/she has relatives or friends working in insurance companies.

The second part of the questionnaire is the questions related to the measurement of personal financial literacy level, which contains three dimensions, financial awareness, financial knowledge and financial ability, from awareness to theoretical knowledge to application skills, combined with the current financial management methods relying on the Internet economy to design practical questions for individuals, in order to comprehensively and accurately measure the level of personal financial literacy. The aspect of financial awareness includes individuals’ attitudes towards consumption and savings, attitudes towards starting a business with financing, attitudes towards credit payments, awareness of protecting the safety of one’s own property, and attitudes towards financial investment and the purchase of financial products. Financial knowledge includes basic interest calculations, judgment of the risks of different investment products, knowledge of mortgages, and knowledge related to understanding bad credit. Financial capability includes whether one is able to pay bills on time, whether one can skillfully use various payment apps such as banks, whether one knows how to open online credit services, purchase financial products and services, and whether one can borrow relief funds.

Personal financial behavior includes both personal financial behavior and participation in financial behavior other than personal finance. Personal financial behaviors include purchasing financial products and managing cash. Therefore, this part of the questionnaire asks, from the perspective of personal financial behavior, whether or not one has purchased financial products, whether or not one is willing to put idle funds into a financial platform in order to obtain interest income, whether or not one has formulated a financial plan, purchased financial products, and other related financial information.

3.2

Analysis of results of financial literacy measures

In this section, the factor analysis method is used to measure the level of personal financial literacy. The three dimensions of financial literacy: financial awareness, financial knowledge, and financial capability are extracted from the common factor dimensionality reduction, and the financial literacy index system is constructed, the factor score levels of the three dimensions are calculated, and then totaled to get the comprehensive score of financial literacy.

Firstly, KMO and Bartlett’s sphericity are used for model validation and testing to determine the applicability of variable factor analysis. The analysis results show that the KMO values of the three dimensions are 0.628, 0893 and 0.774, respectively, and the P-value of the Bartlett sphericity test is 0.000, which passes the significance test, indicating that the questionnaire variable is suitable for factor analysis. The data was standardized using dimensionless techniques, resulting in a standardized vector of financial literacy measures for the 800 respondents. Z=(z1, z2, z3……z749)T. Factor analysis was then performed to extract the common factors after deleting the variables with factor loadings less than 0.5, and finally, the dimensional factor scores and financial literacy composite score.

The questionnaire option settings indicate that the higher the sample financial literacy composite score (larger the index), the higher the level of personal financial literacy. Using factor analysis to reduce the dimensionality, each dimension is summed up to obtain the financial literacy composite score, the mean of this standardized score is 0, and the standard deviation is 1. In order to facilitate the interpretation of the results and subsequent empirical analyses, this paper will be the standardized financial literacy composite score for the equivalent transformation, in the form of a percentage of the financial literacy scores, the descriptive statistics of the financial literacy index as shown in Table 1. Analyzing the relevant statistics about individual financial literacy scores in the table, it can be seen that the mean and median of the index are 65.14 and 69.64, respectively, and the number of individuals with scores around 70 is the largest, indicating that the level of individual financial literacy in Province A is generally high. The standard deviation is 16.95, and the distribution of scores is relatively dispersed, suggesting that the level of personal financial literacy is uneven and individual differences are significant. The histogram of financial literacy level is shown in Figure 2. As can be seen from the figure, the distribution characteristics of individual financial literacy level tend to obey the normal distribution, which is consistent with the statistical characteristics of general social phenomena, and is in line with the general reality, that is, the number of individuals with lower and higher levels of financial literacy is relatively small.

Table 1.

Descriptive statistics of financial literacy index

Sample size	Mean value	Standard deviation	Median	Mode number	Minimum value	Maximum value
758	65.14	17.26	69.64	4.98	0	100

The results of the factor analysis and the reliability and validity tests of the financial literacy measure are shown in Table 2. The results of the factor analysis show that the factor loadings of all measurement question items are greater than 0.5, indicating that the measure has good convergent validity.

Table 2.

Analysis of factors of financial literacy measures and reliability testing

Target layer	Quasi test layer	Index layer	Metric item	Factor load	α coefficient
Financial literacy	Financial consciousness	Financial awareness	Have you learned about the money management products in alipay and wechat	0.754	0.529
			Whether mobile phones download wealth management software	0.769
			The degree of familiarity of college students’ network loans	0.689
			If the money is sufficient, the payment will be paid in installments	0.494
		Consumption awareness	Today will be rich today, tomorrow is tomorrow	0.788
		Consumption awareness	Money is spent on flowers, and there is no need to finance	0.731
		Financial awareness	Do you know the monthly payment form of online shopping	0.82
		Financial awareness	Have you learned about the money management products in alipay and wechat	0.755
	Financial knowledge	Financial accounting	Inflation understanding is correct	0.714	0.826
			Whether the deposit interest is calculated correctly	0.716
			Different types of stock risks are compared to the correct answer	0.584
			The interest rates for different methods are correct	0.636
			Whether the stock investor is right is correct	0.626
			The calculation of the interest rate of the installment is correct	0.552
		Knowledge of credit risk	Credit understanding is correct	0.742
			Whether insurance purchases are correct and risk understanding is correct	0.79
			Is the understanding of bad credit right	0.849
			Understand the rights and obligations after the financial product contract	0.553
	Financial capacity	Financial skills	Using the mobile phone bank app	0.561	0.722
			Do you know how to buy gold	0.792
			Can identify legal and illegal investment channels and product services	0.773
			Will you buy wealth management products on your mobile phone	0.674
		Financial ability	Can you pay the bills on time	0.675
			Will you use alipay and wechat	0.84
			If you need money, you can borrow money from your friends	0.789

4

Empirical analysis of personal financial literacy and financial behavior

4.1

Construction of the model

This section continues the regression analysis by exploring the significance and magnitude of the impact of personal financial literacy on financial behaviors-namely, borrowing behaviors, money management behaviors, insurance behaviors, and financial counseling behaviors. The dependent variable is personal financial literacy, which is a numerical variable based on the assessment indicators presented above and the assigned scores obtained by rating individuals. The explanation variables are borrowing behavior, financial management behavior, insurance behavior, and financial consulting behavior. If the respondents who fill out the questionnaire or are interviewed have corresponding financial behavior, then fill in 1, otherwise fill in 0. The explanation variables are dummy variables. The control variables were age, gender, education, and monthly income.

It can be seen that since the explanatory variables are dichotomously selected discrete models of 0 and 1, the application characteristics of the probit model are more in line with the probit model, so the probit model is chosen as a way to study the correlation between financial literacy and financial behavior.

The regression model is set as follows: A_ij = α_i + β_jFL_j + γ_iX_j + ε_ij

i = 1 (lending behavior), 2 (financial management behavior), 3 (insurance behavior), and 4 (financial advice behavior).

j = 1,2……n, denotes each sample surveyed.

A_i denotes the dependent variable, a dummy variable of type 0-1.

FL_j denotes the independent variable, i.e., the financial literacy of the individual.

X_j denotes the control variables, i.e., the demographic characteristics of the financial literacy subjects - given the existing research results, the four items of age, gender, education level and monthly income are selected in this paper.

α_i denotes the intercept term.

ε_ij is the random error term.

β_i represents the regression coefficient, which indicates the magnitude of the effect of the independent variable on on the dependent variable, due to the specificity of the dependent variable, its practical significance is difficult to illustrate concretely, so this paper selects the marginal effect of financial literacy on financial behavior as an explanation.

The variable definitions, types and assignments are shown in Table 3. The path framework of the model is shown in Figure 3.

Table 3.

Variable definition, type, and assignment

Variable Type	Variable Name	Variable Property	Variable Definition
Independent Variable	Financial Literacy Evaluation Index	Numerical Type	Index Weight Score
Dependent Variable	Lending Behavior	Virtual Variable	It Happens To Be 1, Whether It’s Equal To 0
	Insurance Behavior	Virtual Variable	It Happens To Be 1, Whether It’s Equal To 0
	Financial Behavior	Virtual Variable	It Happens To Be 1, Whether It’s Equal To 0
	Financial Counseling	Virtual Variable	It Happens To Be 1, Whether It’s Equal To 0
Control Variable	Gender	Virtual Variable	Female=0,Man=1
	Age	Numerical Type	Specific Values, Units: Years Old
	Highest Degree	Sequence Variable	Illiterate/Unknown. Primary School = 2: Primary School = 30. College And Above
	Household Monthly Income	Sequence Variable	RMB 3, 000 Yuan = 1, 300 Yuan = 2, 000 Yuan ~ 1, 000 Yuan = 2, 000 Yuan ~ 1, 000 Yuan

4.2

Definition and statistical description of variables

4.2.1

Dependent Variable - Financial Literacy Rating Score

The personal financial literacy score adopted in this paper is obtained by conducting field research, distributing questionnaires, and calculating the scores of each questionnaire. After obtaining the score data of each questionnaire, the data were screened by eliminating the invalid data and selecting the real and valid data, and then the data were processed by the percentage system, so that the data could reflect the financial literacy of individuals more intuitively. The descriptive statistics of variables are shown in Table 4. The data results show that the average score of individual financial literacy is 56.21 with a standard deviation of 28.64, the highest score is 71.65 and the lowest score is 22.84. Therefore, according to the results of the survey, the overall level of financial literacy in Province A needs to be improved.

Table 4.

Variable descriptive statistics

Variable name	Mean	Standard deviation	Maximum value	Minimum value
Financial accomplishment score	56.21	28.64	71.65	22.84
Lending behavior	0.55	0.55	1	0
Insurance behavior	0.59	0.16	1	0
Financial behavior	0.64	0.43	1	0
Financial counseling	0.71	0.48	1	0
Age	39.64	18.34	59	18
Gender	0.51	0.5	1	0
Educational background	2.81	1.02	5	1
Educational background	2.28	0.88	5	1

4.2.2

Dependent variables - 4 financial behaviors: borrowing, lending, money management, insurance, and financial advice

1)

Borrowing behavior

In the questionnaire set the question “through what channels you borrow” to reflect the occurrence of borrowing behavior, according to the data show that 62% of the respondents applied for loans, mainly bank loans, accounting for 28% of the data, the rest of the borrowing channels for friends and relatives borrowing, accounting for 20%, the network borrowing methods accounted for 18% of the data.

2)

Financial Management Behavior

In the questionnaire set the question “through what channels you buy financial products” to reflect the occurrence of financial behavior, in which 52% of the respondents have purchased financial products. Among those who purchased financial products, 41% purchased them through bank counters, 25% purchased them through bank APPs, 16% purchased them through financial APPs such as Alipay, and 18% purchased financial products through other channels.

3)

Insurance behavior

In the questionnaire, the question “What kinds of insurance do you purchase?” was set to reflect the occurrence of insurance behavior. Only 28% of the respondents have not purchased insurance, and 72% of the respondents have purchased insurance. In terms of the purchase status of insurance products, motor vehicle insurance accounted for the highest proportion of 35%, followed by life and medical insurance, accidental injury insurance, and property insurance, which accounted for 29%, 25%, and 7% respectively.

4)

Financial Consulting Behavior

In the questionnaire, the question “What channels do you use for financial counseling” was set to reflect the occurrence of financial counseling behavior. Among the respondents, 68% of the respondents asked professionals. Twenty-eight percent of the respondents consulted their friends and relatives, and eight percent of the respondents studied on the Internet.

4.2.3

Control variables

In this study, the four variables of age, gender, monthly household income, and education were selected as control variables to study the occurrence of financial behavior, and the study variables of all data were analyzed with descriptive statistics. The minimum value of age is 18 and the maximum value is 59, the mean is 39.64 and the standard deviation is 18.34, which indicates that there is a large variation in the selected age sample. In addition the other two control variables also had large differences, the mean value of education was 2.81 with a standard deviation of 1.02 and the mean value of monthly household income was 2.28 with a standard deviation of 0.88.

4.3

Description of Probit regression results

4.3.1

Probit model

In this section, the Probit model is used to analyze the impact of personal financial literacy on borrowing, money management, insurance, and financial counseling behaviors. In order to test the setting problem of the model, this paper adopts both ordinary standard errors and robust standard errors for Probit estimation, and the results of the Probit model measures are shown in Table 5 (***, **, and * indicate that they are significant at the 1%, 5%, and 10% levels, respectively, and standard deviations are shown in parentheses). As can be seen from the table, the robust standard errors are very close to the ordinary standard errors, so one can not worry about the setting of the model. The Probit measurement results show that financial literacy has a significant positive effect on borrowing, money management, insurance, and financial counseling behaviors, i.e., the higher the financial literacy, the more likely it is to occur in the borrowing, money management, insurance, and financial counseling behaviors, and it is significant at the 1% level. This suggests that in the current context of insufficient financial supply in Province A, improving financial literacy among individuals will help them access financial services and thus improve their well-being.

Table 5.

Probit model measurement results

Variable	Lending behavior		Financial behavior		Insurance behavior		Financial counseling
Variable	Ordinary	Soundness	Ordinary	Soundness	Ordinary	Soundness	Ordinary	Soundness
Financial literacy	0.6623*** [0.0776]	0.6623*** [0.0778]	0.3686*** [0.0663]	0.3686*** [0.0662]	0.3527*** [0.0604]	0.3527*** [0.0606]	0.8046*** [0.0708]	0.8046*** [0.0709]
Age	0.0057 [0.0283]	0.0057 [0.0285]	-0.0101 [0.0032]	-0.0101 [0.0033]	-0.0038 [0.0062]	-0.0038 [0.0064]	-0.0054 [0.0022]	-0.0054 [0.0024]
Gender	0.3821*** [0.064]	0.3821*** [0.065]	0.0772*** [0.0612]	0.0772*** [0.0613]	0.1193* [0.0616]	0.1193* [0.0617]	0.0604 [0.0614]	0.0604 [0.0616]
Educational background	0.1131*** [0.0328]	0.1131*** [0.0329]	0.1583 [0.0323]	0.1583 [0.0325]	0.0426** [0.0338]	0.0426** [0.0339]	0.1492 [0.0361]	0.1492 [0.0362]
Family income	0.1904*** [0.0325]	0.1904*** [0.0327]	0.1181*** [0.0215]	0.1181*** [0.0217]	0.1461*** [0.0339]	0.1461*** [0.0338]	0.0707** [0.0251]	0.0707** [0.0255]
Family income	-2.5986*** [0.2508]	-2.5986*** [0.2509]	-1.6851*** [0.2516]	-1.6851*** [0.2518]	-0.8005*** [0.2429]	-0.8005*** [0.2428]	-2.5129*** [0.2485]	-2.5129*** [0.2487]

Meanwhile, the statistical characteristics of the population also have a significant impact on financial behavior. Males with higher education and higher income are more likely to engage in financial behavior, and they are largely significant at the 1% confidence level. However, the age factor is an accident, no matter processed by age or the square of age, the significance of age is not high, the coefficient is relatively small, and the sign of the coefficient is also positive or negative, a possible explanation is: according to the research results of the existing literature, the financial behavior with the change of age shows an inverted “U” characteristics, for example, after the age of 60, cannot take out loans, cannot buy insurance, and other restrictions. One possible explanation is that, according to the research results in the literature, financial behavior changes with age in an inverted “U” shape, for example, after 60 years old, one cannot take out a loan, cannot buy insurance, and other restrictions, which prevent financial behavior from changing with age.

4.3.2

Probit model with endogenous variables

Explaining the impact of financial literacy on financial behavior must take into account the fact that on the one hand, residents may improve their financial literacy by engaging in financial behavior, and on the other hand, there may be omitted variables and measurement errors. As a result of these two aspects, the model may have endogeneity problems. In order to solve the endogeneity problem, this paper conducts regression analysis with the help of instrumental variable approach.

In the first step, the way of acquiring financial knowledge is first regressed on financial literacy, and the results show that both instrumental variables and control variables are correlated with financial literacy at 1% confidence level. Among them, the richness of financial access contributes to financial literacy, males have higher financial literacy than females, and education and annual household income are positively correlated with financial literacy, which are all consistent with the previous analysis. The F-value estimated in the first step is 286.14 and the t-value of the instrumental variable is 10.25. The F-value is greater than the critical value of 17.26 at the 10% level of bias. Indicating that the paper uses the question, “Which of the following are the main ways in which you learn about financial literacy?” as an instrumental variable for financial literacy is appropriate and there is no weak instrumental variable problem.

In the second step, the endogeneity of financial literacy affecting financial behaviors is then tested, and the results show that the p-values of the Wald endogeneity test of financial literacy for the three financial behaviors of lending, money management, and financial counseling are 0.0442, 0.0789, and 0.0000, and it can be assumed that financial literacy is an endogenous variable for the three financial behaviors of lending, money management, and financial counseling at the 10%, 10%, and 1% confidence levels, respectively. It indicates that there are unmeasured omitted variables that increase the tendency of individuals to engage in borrowing, money management, and financial counseling behaviors while increasing financial literacy. Meanwhile, when using the IVprobit model, the estimated coefficients of financial literacy on borrowing, money management, and financial counseling behaviors are all significantly larger and all are correlated at the 1% confidence level, suggesting that if the endogeneity of financial literacy is ignored, it will underestimate the impact of financial literacy on the three financial behaviors of borrowing, money management, and financial counseling. Whereas one possible explanation for the insignificant endogenous variable of financial literacy on insurance behavior is that most of the insurance purchased by individuals is mandatory such as motor vehicle insurance, which does not help much in improving financial literacy. The results of the two-stage regression thus further suggest that increased financial literacy does increase the likelihood that individuals will engage in borrowing, money management, and financial counseling behaviors. The estimation results of the two-step IV Probit model are shown in Table 6.

Table 6.

The two steps iv probit model estimates the results

variable	First step	Second step
variable	First step	Lending behavior	Financial behavior	Insurance behavior	Financial counseling
Financial literacy	-	1.0969*** [0.3007]	0.8917*** [0.3166]	0.2822 [0.3006]	2.6891*** [0.3612]
Age	-0.0078*** [0.0006]	0.0069* [-0.0021]	-0.0093* [0.004]	-0.0048 [0.0078]	0.0192*** [0.0033]
Gender	0.0945 [0.0165]	0.3406*** [0.0693]	0.0341*** [0.0599]	0.1267* [0.0603]	-0.1366* [0.0834]
Educational background	0.1645*** [0.0112]	0.0371 [0.0642]	0.0645 [0.0684]	0.0588 [0.0681]	-0.1878** [0.0789]
Family income	0.0792*** [0.0089]	0.1535*** [0.0378]	0.0685* [0.0372]	0.169*** [0.0422]	-0.0945** [0.0451]
Constant term	1.6184*** [0.0687]	-3.3872*** [0.5957]	-2.6119*** [0.5923]	-0.6695 [0.5818]	-5.9412*** [0.7002]
IV	0.1305*** [0.0128]	-	-	-	-
Adjusted R²	0.3561	-	-	-	-
Wald check	-	0.0442	0.0789	0.7865	0.0000

4.3.3

Robustness Tests

In order to test the robustness of the results, this paper adopts two methods to measure the level of financial literacy of individual households by summing up the scores of the answered questions and excluding the sub-sample over 60 years old. First, drawing on Lusardi & Michell’s method, regression analysis is done on borrowing, money management, insurance, and financial counseling behaviors respectively, using the summing of response question scores as an indicator of financial literacy level. The robustness test is shown in Table 7. The level of financial literacy (ratings summed up) has a significant positive effect on the occurrence of borrowing, money management, insurance, and financial counseling behaviors by individuals, which are all significant at the 1% level. Considering that the financial behavior of the sample over 60 years old may not be the same as that of other rural residents, the paper removes the respondents over 60 years old from the sample for estimation again, and a total of 180 samples are removed. It can be found that after excluding these samples, the effect of financial literacy level on individuals’ financial behavior is basically the same as the previous results, and the effect of control variables on financial behavior is also basically the same as the previous results.

Table 7.

Robustness test

variable	Financial literacy score test				Remove the samples from over 60 years old
variable	Lending behavior	Financial behavior	Insurance behavior	Financial counseling	Lending behavior	Financial behavior	Insurance behavior	Financial counseling
Financial literacy	0.0248 [0.0035]	0.0197 [-0.0013]	0.019 [0.0043]	0.0461 [0.0084]	0.7107 [0.0742]	0.327 [0.0667]	0.3491 [0.0649]	0.7703 [0.0684]
age	0.0124 [0.0025]	-0.011 [0.006]	-0.0071 [0.0079]	0.0033 [0.0049]	0.0106 [-0.0015]	-0.01 [0.0085]	0.0019 [0.0032]	-0.0056 [0.0045]
gender	0.3922 [0.0554]	0.0791 [0.0585]	0.118 [0.0585]	0.0608 [0.0532]	0.3725 [0.0572]	0.0794 [0.0656]	0.0879 [0.067]	0.0593 [0.061]
Educational background	0.106 [0.0331]	0.1652 [0.0342]	0.0394 [0.0338]	0.1466 [0.0332]	0.1165 [0.0385]	0.166 [0.034]	0.0287 [0.0351]	0.15 [0.0371]
Family income	0.191 [0.0295]	0.111 [0.0277]	0.151 [0.032]	0.0701 [0.0313]	0.1854 [0.0326]	0.104 [0.027]	0.135 [0.0255]	0.1145 [0.0549]
Constant term	-2.588 [0.247]	-1.691 [0.2491]	-0.809 [0.2325]	-2.493 [0.2478]	-3.1 [0.2663]	-1.64 [0.2637]	-0.7328 [0.2415]	-2.3701 [0.2569]
Adjusted R²	0.1139	0.0804	0.052	0.1092	0.1023	0.0525	0.0382	0.0941

4.4

Heterogeneity of financial behavior

Heterogeneity test of personal financial behavior, i.e., the median is divided into high financial literacy and low financial literacy, and the geographical location of the sample is divided into the eastern and central-western regions of Province A, so as to test the heterogeneity of personal financial behavior by financial literacy through the sub-sample.

4.4.1

Regional heterogeneity

The test for regional heterogeneity is shown in Table 8. The NIIC coefficients in column (1) are positive at the 5% significance level, while the NIIC coefficients in column (2) are insignificantly negative, which reflects the regional heterogeneity of the impact of financial literacy on individuals’ financial behaviors, i.e., individuals in the eastern part of Province A have significantly more rational financial behaviors relative to individuals in the central and western regions of Province A. This may be due to the abundance and variety of financial innovation products in the eastern part of Province A. Finance and individuals are more closely related, so individuals’ awareness of financial security and self-protection ability gradually increase, and personal financial literacy promotes individuals to make correct financial decisions and improve their own financial literacy.

Table 8.

Regional heterogeneity survey

behavior	(1) Eastern region	(2) Midwest
Finlit	0.1512** (0.055)	-0.0856 (0.1999)
NIIC	0.1249** (0.0522)	0.1605 (0.1743)
importance	0.1826* (0.0905)	0.6437 (0.5111)
Control variable	yes	yes
Regional fixation effect	yes	yes
N	270	50
LR	65.15	28.45
Pseudo R²	0.0558	0.0548
Prob > chi²	0.000	0.1882

4.4.2

Financial literacy heterogeneity

The financial literacy heterogeneity test is shown in Table 9. As can be seen from the table, from the perspective of financial literacy, when individual financial literacy is high, personal financial literacy helps their financial behavior, while for individuals with low financial literacy, the effect on their financial behavior may be negative, i.e., the effect of personal financial literacy on individual financial behavior can vary depending on the level of financial literacy.

Table 9.

Financial literacy heterogeneity survey

Behavior	(1) High financial quality	(2) Low financial literacy
NIIC	0.1702 (0.0851)	0.1083 (0.071)
Importance	0.2838 (0.1395)	0.1202 (0.1393)
Control variable	yes	yes
N	145
LR	49.32	31.25
Pseudo R²	0.0691	0.6737
Prob > chi²	0.0005	0.0008

5

Conclusion

Good financial literacy will have an impact on personal financial behavior and contribute to the stable development of the financial market. The article uses the population of province A as an example to study the relationship between people’s financial behavior and financial literacy in this province. The findings of this article include: 1)

After analyzing the financial literacy of the population in Province A by applying the factor analysis method to measure financial literacy, it was found that the mean and median of the index were 65.14 and 69.64 respectively, and the population with scores around 70 occupied the most, i.e., the level of personal financial literacy of individuals in Province A was generally high.

2)

After regression analysis of financial literacy and financial behavior of the population in Province A by using the probit model, it is found that financial literacy has a significant positive impact on financial behavior, and different group characteristics have a significant impact on financial behavior.

Idioma:: Inglés

Calendario de la edición:: 1 veces al año
Temas de la revista:: Ciencias de la vida, Ciencias de la vida, otros, Matemáticas, Matemáticas aplicadas, Matemáticas generales, Física, Física, otros

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A Study of Individual Financial Behavior and Financial Literacy under the Development of Digital Financial Technology

Xiao Liu

Publicado en línea: 19 mar 2025

Recibido: 24 oct 2024

Aceptado: 12 feb 2025

DOI: https://doi.org/10.2478/amns-2025-0458

Palabras claveFactor analysis model, Measurement analysis, Personal financial behavior, Financial literacy

© 2025 Xiao Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Palabras clave
Factor analysis model, Measurement analysis, Personal financial behavior, Financial literacy