International experience indicates that, in the villages of many developing countries, including China, access to financial markets, as well as the degree of know-how prevailing with regard to benefitting from these markets through procurement of credit, are far from optimistic, and thus rural households are confronted with severe credit constraints [1–3]; additionally, it is observed that such a situation tends to be worse in regions characterised by a relatively backward level of economic development. In recent years, the Chinese government has gradually come to realise that the backwardness of rural economic development has had an adverse impact on the effectiveness-of-access enjoyed by the people living in these regions to financial markets; resultantly, there is an insidious impact on the increase in the real income of rural dwellers. Considering this situation, therefore, a series of revolution measures has been adopted by the government. However, the new round of rural financial revolution policies has not resulted in the needed results. Thus, overcoming the credit predicament among peasant households in the rural areas of China still remains a significant task to be accomplished. Therefore, it has become particularly important to identify the influencing factors of the credit constraint for Chinese rural households.

With regard to the influencing factors of credit constraint, relevant scholars have brought out a series of theories and empirical analyses. For example, [4] have opined that the essential problem of rural household credit constraint lies in the credit distribution of the financial organisations; [5] has studied the credit constraint of the rural household from the aspects of political participation and the financial participation, and the result has shown that both the political participation and the financial participation have an obvious impact on the rural household’s credit constraints. The rural households with political participation bear a rate of being constrained by the credit that is 1.25% lower than that of the rural households without political participation, and the rural households with financial participation have a rate of being constrained by the credit that is 29.4% lower than that of the rural households without financial participation. In their research, [6] find that the rural households with loan note will have a credit constraint intensity that is perceptibly reduced by 10.57% and a production-type credit constraint that is perceptibly reduced by 24.02%. In the meantime, the rural households with loan note enjoy perceptibly less credit constraints in rural production than those without loan notes. However, few researches have studied the effect of financial literacy on the rural household’s credit constraints.

In fact, the financial literacy bears a variety of influences on the economic behaviours of the family or the rural household, such as start-up, stock participation and insurance purchase. For example, in their study, [7] have ascertained that the financial literacy of correctly calculating the deposit interest rate and correctly identifying the stock and fund risks can increase the start-up rate of the family and the household; [9, 8] based on their analysis, have discovered that by promoting the family’s loan channel preference, the financial literacy could level-up the family’s credit demand and credit accessibility, improve the family’s attitude towards the risks, attenuate or minimise the inhibiting effect of credit constraint on the entrepreneurship, and enhance the family’s entrepreneurial intention; [10] find that the higher the interviewees’ financial literacy level, the more varied their financial product investments will be, and a greater financial literacy level would also ensure that their investment would not be limited to just one; according to the research of [11], the higher the interviewees’ financial literacy, the higher their risk preference will be, and therefore, the family stock market engagement rate will accordingly be increased; also, [12] have ascertained based on their analysis that the increase of financial literacy will level-up the households’ possibility of attending commercial insurance as well as their participation degree.

Therefore, it is reasonable to believe that the financial literacy also could affect the easing of the rural household’s credit constraint; however, the related articles are relatively limited. Within these limited researches, the following can be mentioned: [13–15] opines that the financial literacy level of the borrowers is bound up with their credit quality. The higher the borrowers’ financial literacy level, the stronger their repayment ability, and therefore, the possibility of credit constraint may be attenuated; [13] have carried research on the effect of the village’s financial environment on the rural household’s credit constraint, and it is proved by the research result that the policies of the rural credit cooperative have an obvious impact on reducing the rural household’s credit constraint, laterally reflecting the effect of financial literacy on the credit constraint; [14] have also found that the improvement of the financial literacy level and the education level is good for increasing the household’s formal credit gain and reducing the informal credit preference; [15] based on their analysis, have discovered that the higher the financial literacy level, the higher the family formal credit gain rate will be, and this is good for easing the family credit constraint. However, few articles have considered both the formal and informal situations, or the sample choosing between credit demand and credit constraint.

It is worth noting that, to achieve the research object of this thesis, when building the model, a few problems were encountered that needed to be solved. On the one hand, when studying the factors affecting the rural household’s credit constraint, the credit demand shall be controlled, for a part of the credit demand that has not shown up due to the market failure, in case of losing control, will result in the sample choosing error [16]. Consider the formal credit constraint for an example: not all rural households would be constrained by credit, for only the rural households with credit demand would be constrained by credit. Therefore, it is not hard to find that the rural households for formal credit constraint research are not randomly drawn from the totality, and a certain extent of sample choosing issue has existed. On the other hand, the endogeneity problem should be taken into consideration for the effect of financial literacy on the rural household credit constraint: for example, the omitted variable (such as the unobservable ability of the rural household) may affect the financial literacy level and the credit constraint situation of the rural household. When the financial literacy eases the rural household’s credit constraint, the rural household might improve its financial literacy level through easing the credit constraint. The two aforesaid aspects all can result in the endogeneity problems, and the current researches could have inspected one of them at most; for example, [17] have studied and surveyed the endogeneity error caused by the sample choosing, while they have only noticed the endogeneity error caused by the omitted variable and bidirectional causality. Therefore, to solve the aforesaid problems, a new model and measure should be adopted.

The data adopted by this thesis have been drawn from the rural household investigation data of Shaanxi and Yunnan in 2016, mainly covering the basic family situations of Shaanxi and Yunnan during the period 2013–2015. Heckprobit model (hereafter referred to as IV-Heckprobit model) has been adopted to study the effect of financial literacy on the rural household’s credit constraint and its change law. Compared with the existing researches, this thesis has enhanced the following aspects: firstly, the financial literacy aspect has been introduced directly into the model, empirically analysing the impact of the interviewees’ financial literacy level on their formal and informal credit demand and constraint, resulting in flourishing and deepening of the content and level of the credit constraint research; secondly, on this basis, by probing into the issue of how the financial literacy has adapted and changed concomitantly with the marketisation process, as well as with the increase in the formal financial development and the rural household income, this thesis offers a comprehensive and profound conclusion. Thirdly, adopting the IV-Heckprobit model has solved the estimation error caused by sample choosing and endogeneity.

The rest of this thesis will be arranged as follows: the second part will be an analysis on the related theories, which presents the theoretical hypothesis of the effect of financial literacy on the rural household’s credit behaviour; the third part is the explanation and analysis of the source of the data used by this thesis to choose and introduce the variable; the fourth part involves building the corresponding measurement model, bringing up the estimation result and surveying the effect of financial literacy on the rural household’s credit behaviours; the fifth part presents a discussion of the model, and the results derived from testing the same, to probe into how the aforesaid effects have changed concomitantly with the marketisation process, the formal financial development and the rural household’s income increase; the final part arrives at a conclusion and presents the corresponding policies and opinions.

In terms of theory, financial literacy is a specific human capital. In the economic behaviours of the rural household, financial literacy can promote the investor’s information processing capability, reduce the family’s cost of attending the financial market to expand the family fortunes’ scale and enhance the consumption preference on durable goods. Beck et al. observe that if people are not familiar with financial products, they would not show any interest in the same and thus not contribute to the prevailing demand. Financial literacy reflects people’s mastery over financial concepts and application of these elements of knowledge to fund management. Through studying the built financial education demand model, Marcolin and Abraham have entered the relevant parameters of financial literacy, observed financial output changes such as individual credit, deposit and investment behaviours, and finally proposed their positive-correlation relationship. A group of people lacking sufficient financial literacy, or who are altogether financially illiterate, will always perform some behaviours that are bad from the perspective of the financial benefits involved in financial activities, and this inference is the same as the research result of Lusarid and Mitchel. In the meantime, the financial literacy level has an obvious impact on the family’s formal credit constraint situation; the richer the financial literacy of the interviewee, the lower the interviewee’s possibility of being constrained by formal credit will be.

The financial literacy could promote the rural household’s cognitive competence, enhance the rural household’s repayment will and lower down the rural household’s credit default risk, and as a result it will affect the rural household’s credit demand.

As the financial market develops rapidly, the financial literacy will have an increasingly bigger effect on the family credit behaviour. The specific factors that go into determining the manner in which and extent up to which financial literacy can be expected to impact the credit behaviour is explained in terms of the following: Firstly, the levelling-up of the financial literacy level could promote the rural household’s cognition degree towards the credit business and financial products of different financial organisations, helpful for the rural household to make a wiser decision [18] and affecting the credit demand of the rural household laterally. In case the rural household does not understand or is not familiar with the financial products and credit policies of the financial organisations, the rural household might think they would not be able to gain the credit and choose to give up the credit application [19, 21] in which case the credit demand would be reduced. On the one hand, the cognitive deficiency of the rural household is the main reason for the rural household to be affected by the credit constraint. On the other hand, the rural household of a higher financial literacy level will carefully and rationally consider its credit behaviour, objectively think about the credit default cost and then reasonably deal with its credit demand. Secondly, to a certain extent, the financial literacy level of the rural household could reflect its credit rating. The higher the financial literacy level of the rural household, the more it cares about its credit rating, and therefore, the credit demand will be higher and the credit constraint will be lower.

The financial literacy could increase the rural household’s yield rate, to affect the rural household’s loan repayment ability and bring down the credit constraints.

The specific factors that go into determining the manner in which and extent up to which financial literacy can be expected to impact the household’s yield rate is explained in terms of the following: Firstly, the financial literacy could help the rural household make a rational decision, increase the rural household’s family income, enhance its loan repayment ability and then bring down the rural household credit constraint (Lusardi et al., 2013). On the one hand, it is hard for the rural household with lower financial literacy make a correct and rational financial plan, and such an irrational financial decision will bring about a negative effect on the rural household [22]; for example, a family financial crisis may be raised, the rural household’s loan repayment ability will be affected, and as a side effect, both the credit demand and constraint of the rural household will be affected. On the other hand, as soon as the rural household with a lower financial literacy level makes a mistake in financial decisions, it is hard for them to adopt effective remedial measures, and as a result, more wrong decisions will be made and the rural household’s possibility of credit constraint will be increased. Secondly, in the case that the household pays close attention to financial and economic information and undergoes financial courses to improve financial computing power and predictive inference level, the family financial demand will be released and the financial exclusion rate will be perceptibly reduced [23].

The used data used in this thesis has been drawn from the rural household investigation data of Shaanxi and Yunnan in 2016, mainly involving the basic family information of Shaanxi and Yunnan for the period 2003–2005. This investigation has adopted the step sampling way to survey the regions in Shaanxi and Yunnan, gaining detailed information about 600 rural families with regard to parameters such as population characteristics, income, property and credit. After screening out the invalid samples with information gaps and filling errors, there are 530 valid samples, with a questionnaire effective rate of 88.33%. This has provided a better data support to the thesis to analyse the effect of financial literacy on the rural household’s credit availability.

This questionnaire survey has investigated several basic features of the rural households; however, bearing in mind the need to maintain the thesis length at an acceptable level, this thesis will only make a brief report. On the aspect of gender, the proportion of the male households and that of the female households are, respectively, 89.06% and 10.94%; on the age aspect, the people are aged mainly between 30 years and 59 years, taking up 77.36%; on the education aspect, the people with middle school education background are the most, taking up 41.89% of the whole sample, and the ones without primary-school education background or of junior-college education background or higher have taken up 4.53% and 5.09%, respectively; with regard to the aspect of political background, the majority of the households are not party members, for the ones of party membership are just 16.79%.

Inquire the households with Questions 1 and 2; use Table 1 for reference. In case the household has chosen Option ①, this household will be considered as the one without credit demand. Based on this, and based on whether the household has a credit demand, the binary dependent variable _{1} is established – when the household does not have a credit demand, the variable takes the value of 0, and when it does, it takes 1.

Questions 1 and 2 involved in the questionnaire

Question | Answers and options | |
---|---|---|

Q1 | In the recent 3 years, in case of loan demand and contact with the bank or credit cooperatives, which option fits your reality the most? | ① No need to borrow |

Q2 | In the recent 3 years, in case of loan demand and contact with family members, friends and neighbours, which option fits your reality the most? | ① No need to borrow |

The two questions in the questionnaire of the same credit demand (Q1 and Q2) will test whether the household is constrained by the credit. In case the rural household’s answer to these two question is ⑤, this rural household does not suffer from credit constraint, or it is constrained by credit. Based on this, and based on whether the rural household is constrained by credit, the binary dependent _{2} is established – when the rural household does not suffer from credit constraint, this variable takes the value of 0, and otherwise it takes 1.

When inquired with Q1, in case the rural household answers with ‘No need to borrow’, it indicates the rural household does not have formal credit demand, and in the absence of this answer, the rural household will be considered as having formal credit demand. Based on this, and based on whether the rural household has the formal credit demand, the binary dependent variable _{1} is established – when the rural household does not have a formal credit demand, this variable takes the value of 0, and otherwise it takes 1.

For the aforesaid Q1, if the rural household answers as ‘How much is applied will be permitted’, then it means the rural household is not constrained by formal credit. In case of answer with Option ②, ③, or ④, the rural household is considered to be constrained by credit. Based on this, and based on whether the rural household suffers from formal credit, the binary dependent variable _{2} is established – when the rural household is not constrained by credit, this variable takes the value of 0, and otherwise it takes 1.

The aforesaid question 2 is used to test whether the rural household has an informal credit demand. In case the rural household answers as ‘No need to borrow money’, it means the household does not have an informal credit demand, and in case any other answer is obtained this indicates that the rural household does have such a demand. Based on this, and based on whether the rural household has informal credit demand, the binary dependent variable _{3} is established, and when the rural household does not have an informal credit demand, this variable takes the value of 0, and otherwise it takes 1.

For Q2, in case the rural household’s answer is Option ⑤, the rural household is considered as having no informal credit constraint. In case the rural household answers with one of the three options other than Options ① and⑤, the rural household is considered as suffering an informal credit constraint. Based on this, and based on whether there is an informal credit constraint, the binary dependent variable _{4} is established, and when the rural household does not have an informal credit constraint, this variable takes the value of 0, and otherwise it takes 1.

The core variable in this thesis is the financial literacy. By referring to the work methods of the mainstream studies in the literature dealing with this topic [24], this thesis has set up questions about financial literacy, involving interest rate, currency inflation, and the comparison between interest rate and currency inflation. However, the dependency of the aforesaid questions is very high, and the existing documents have processed from the following angles: the first is to simply integrate the correctness of the financial literacy, and the advantage of such a method is that it is concise and explicit; the second is to make use of methods such as principal component analysis (PCA) and factor analysis (FA), to draw the common factor and eliminate the dependency between variables. This thesis has adopted the way of extracting the common factor to process the financial literacy.

Scholars such as Zhichao et al. have opined that, when the rural household answers the financial questions in the questionnaire, ‘I don’t know’ and ‘Wrong’ are two different concepts, for they reflect different financial literacy levels. In case the rural household answers as ‘I don’t know’, it means the rural household does not understand the basic concepts. When the household answers as ‘Wrong’, the rural household could be considered as having a certain degree of understanding over the basic concepts of financial literacy; however, such a degree is not sufficient to enable the respondent to correctly answer the financial questions.

By learning from the previous researches, this thesis builds two dummy variables for each financial question according to the answers, to measure the financial concept (answer as ‘I don’t know’ = 1, others = 0) and the financial computing ability (correct answer = 1, others = 0), and an iteration principal FA will be carried out on the six dummy variables produced by the three financial questions, to get the factor comprehensive score and define it as Financial Literacy 1.

By referring to the previous researches, this thesis has introduced the control variables as follows (for detailed information, please refer to Table 2). The householder’s personal characteristic variable (householder’s gender, age, education background, risk attitude and whether he is a party member or not), the family member’s social characteristic variable (labour force proportion, family size, family yearly income and social expenditure), financial environment characteristic variable (the distance to the bank and deposit) and regional characteristic variable (whether it is Shaanxi province or not).

Variable definition and descriptive statistics

Credit constraint | d2 | Whether suffer from credit constraint during 2013–2015: yes = 1, no = 0 | 488 | 0.926 | 0.262 | 0 | 1 |

Credit demand | d1 | Whether have credit demand during 2013–2015: yes = 1, no = 0 | 530 | 0.921 | 0.270 | 0 | 1 |

Formal credit constraint | y2 | Whether suffer from formal credit constraint during 2013–2015: yes = 1, no = 0 | 472 | 0.646 | 0.479 | 0 | 1 |

Formal credit demand | y1 | Whether have formal credit demand during 2013–2015: yes = 1, no = 0 | 530 | 0.891 | 0.312 | 0 | 1 |

Informal credit constraint | y4 | Whether suffer from informal credit constraint during 2013–2015 | 436 | 0.833 | 0.374 | 0 | 1 |

Informal credit demand | y3 | Whether have informal credit demand during 2013–2015: yes = 1, no = 0 | 530 | 0.823 | 0.382 | 0 | 1 |

Financial Literacy 1 | FL1 | Set up six dummy variables according to the three questions (interest rate, currency inflation, and comparison between currency inflation and interest rate); each financial question will bring up two dummy variables; through the FA, get Financial Literacy 1’s score | 530 | 0.577 | 0.326 | 2.19e–07 | 1 |

Financial Literacy 2 | FL2 | Number of correctly answering the financial questions: correct for 0 = 0, correct for 1 = 1, correct for 2 = 2, correct for 3 = 3 | 530 | 1.349 | 1.014 | 0 | 3 |

Householder gender | genr | Household gender: male = 1, female = 0 | 530 | 0.891 | 0.312 | 0 | 1 |

Household’s education background | degree1 | Householder’s education background: didn’t attend primary school = 1, primary school = 2, middle school and junior college = 4, junior college and higher = 5 | 530 | 2.953 | 0.932 | 1 | 5 |

Householder’s age | age | Householder’s age in 2015; unit: years of age | 530 | 47.23 | 12.01 | 22 | 89 |

Risk attitude | attitude | Preferring risk = 1, not preferring risk = 0 | 530 | 0.585 | 0.493 | 0 | 1 |

Labour force proportion | labour | The proportion of family labour force in the overall number, unit:% | 530 | 0.570 | 0.227 | 0.143 | 1 |

Family size | family | Family size in 2015 | 530 | 4.345 | 1.572 | 1 | 13 |

Distance to financial organisations | dista | Distance to the nearest financial organisation, take the logarithm; unit: kilometre | 530 | 1.256 | 0.628 | 0 | 3.434 |

Family yearly income | income | Total family income in 2015, unit: 10,000 yuan | 530 | 10.47 | 0.714 | 8.517 | 13.82 |

Social expenditure | social | Family’s social expenditure for interaction, unit: 1000 yuan | 530 | 6.200 | 3.131 | 0 | 10.82 |

Political identification | polic | Whether is party member: yes = 1, no = 0 | 530 | 0.168 | 0.374 | 0 | 1 |

Family deposit | saving | Whether the family has deposits in 2015, yes = 1, no = 0 | 530 | 0.579 | 0.494 | 0 | 1 |

Shaanxi | sx | Whether belongs to Shaanxi region, yes = 1, no = 0 | 530 | 0.449 | 0.498 | 0 | 1 |

Highest education background | degree2 | The highest education background of the family members: didn’t attend primary school = 1, primary school = 2, middle school = 3, junior school and junior college = 4, junior college and higher = 5; the instrumental variable of Financial Literacy 1 | 530 | 3.774 | 1.042 | 1 | 5 |

The village’s average Financial literacy level | average | The average value of the Financial Literacy 1 of the same village will be the instrumental variable of Financial Literacy 1 | 530 | 0.577 | 0.099 | 0.349 | 0.705 |

Technical training | train | Whether attended a technical training during 2013–2015: attended = 1, no = 0 | 530 | 0.353 | 0.478 | 0 | 1 |

FA, factor analysis

To ensure the model could be estimated under both phases, credit demand and credit constraint, it was ensured that the variables in these two phases could not be totally the same. Therefore, the risk attitude of the householder will be introduced as a cognitive variable into the model of credit demand phase; it is not introduced into the credit constraint phase, since it will be easier for the householder with risk preference to have credit demand in the absence of a direct consequential impact on the credit constraint (Table 2).

According to the previous researches, the credit behaviours of the rural household could be successively divided into two phases: credit demand and credit constraint. Therefore, when studying the effect of financial literacy on the rural household’s credit constraint, the bias error caused by the sample selectivity must be overcome. In the meantime, due to the endogeneity problems in the financial literacy, in order to overcome both the endogeneity problem and sample selectivity problem, this thesis has adopted IV-Heckprobit model, to effectively correct the estimation error caused by endogeneity and sample selectivity. The model is set as follows:

Credit demand and credit constraint:_{1} and _{2} are the actually observed two-valued indexes, signifying, respectively, whether the rural household has credit demand (overall) and whether it is under credit constraint (overall);

In order to probe into the analysis on the credit behaviours of the rural household, this thesis has divided the credit demands into formal and informal credit demands according to the previous researches, and divided the credit constraints into formal and informal credit constraints, to further survey the credit behaviours of the rural household. The detailed model is as follows:

Formal credit demand and credit constraint:

Informal credit demand and credit constraint:_{1}, _{2}, _{3} and _{4} are all the actually observed two-valued indexes, meaning, respectively, whether the rural household has the demand of formal credit, whether the household is under formal credit constraint, whether the rural household has the demand of informal credit, and whether the rural household is under informal credit constraint. In this respect, the instrumental variable’s estimation of financial literacy is the same as that in Formula (3).

In this thesis, the financial literacy bears a more obvious endogeneity problem. Firstly, it’s hard to measure the householder’s capacity, since an issue of omitted variable exists; secondly, depending on three financial questions, the task of measuring the financial literacy level of the householder bears an obvious measuring error problem; and finally, the extent of the financial literacy will affect the credit behaviour of the rural household, and in the meantime, the previous credit experience of the rural household also has an impact on the financial literacy. Therefore, there is a two-way causal relationship between the financial literacy and the credit behaviour in this thesis. In order to gain the exact and reliable conclusion, this thesis has tried to choose the appropriate instrumental variable to effectively correct the estimation error.

By taking lessons from the research of this thesis has chosen the Village Average Financial Literacy Level as the first instrumental variable, which has been calculated using Financial Literacy 1. The Village Average Financial Literacy Level means the average financial literacy level of the rural households in the village (obtained from the average values of the financial literacy level of the rural households in the same village), affecting the financial literacy level of an individual rural household and without affecting its credit behaviour. Besides, this thesis has chosen the highest education background of the family members (excluding the householder) as the second instrumental variable. The family credit behaviour is mainly determined by the householder; therefore, although the education background of other family members might affect the household’s financial literacy level, there is no direct contact between it and the credit behaviour of the householder.

Meanwhile, the financial literacy will affect the family income and the householder’s risk attitude, and the family income will also affect the family social expenditure and deposit. Therefore, this thesis has carried out the Pearson correlation test on financial literacy, household risk attitude, family yearly income and social expenditure, as well as a significance test. (Results are shown as Table 3.)

Correlation coefficient table

FL1 | 1 | ||||

Income | 0.2369** | 1 | |||

Social | 0.1421** | 0.3634** | 1 | ||

Saving | 0.2593** | 0.2116** | 0.2805** | 1 | |

Attitude | 0.3035** | 0.1126** | −0.0207 | 0.1120** | 1 |

As seen from the data in Table 3, although there is a prominent correlativity existing in the relationship between financial literacy, family yearly income, social expenditure, deposit and household risk attitude, the correlation coefficients between these five variables are expressed based on pairs, and the highest one is 0.3634, between household income and social expenditure. Therefore, bringing these variables into the model would not result in the problem of multicollinearity.

This part will empirically analyse the effect of financial literacy on the credit demand and credit constraint of the rural family.

Table 4 has reported the estimation results of financial literacy, credit demand and credit constraint analysed from the IV-Heckprobit model. In this regard, corr(e.d1,e.d2) is a correlation coefficient between Formulas (1) and (2), and it is prominent at a level of 5%, which shows that when estimating the rural household credit constraint formula, the problem of sample selectivity exists, indicating that the choice of the IV-Heckprobit model has been a rational one. Besides, in consideration of endogeneity problem, this thesis has used the village average financial literacy and the highest education background of family member as the instrumental variables to carry out the two-phase estimation, as shown as Formula (3). From the result, we can infer that the highest education background of family member and the village average financial literacy are prominent at the levels of 1% and 10%, respectively. Additionally, the test result has been reported under the table, corr (e.FL1,e.d1) is remarkable in level of 1%, indicating the model bears an endogeneity problem and has no weak instrumental variable problem.

Financial literacy, credit demand and credit constraint

FL1 | 1.699 | 2.964*** | — |

(1.059) | (0.441) | — | |

genr | −0.224 | 0.509** | −0.013 |

(0.330) | (0.212) | (0.043) | |

degree1 | 0.066 | −0.060 | 0.016 |

(0.112) | (0.082) | (0.016) | |

age | 0.012 | 0.006 | −0.002* |

(0.008) | (0.006) | (0.001) | |

attitude | — | -0.044 | — |

— | (0.126) | — | |

labour | −0.161 | −0.737** | 0.100 |

(0.444) | (0.321) | (0.064) | |

family | −0.018 | 0.044 | 0.006 |

(0.059) | (0.055) | (0.010) | |

dista | −0.266** | −0.065 | 0.017 |

(0.133) | (0.115) | (0.021) | |

income | −0.219 | −0.153 | 0.065*** |

(0.147) | (0.115) | (0.020) | |

social | −0.017 | −0.020 | −0.004 |

(0.030) | (0.026) | (0.005) | |

polic | −0.274 | −0.134 | 0.010 |

(0.215) | (0.185) | (0.036) | |

saving | −0.015 | −0.079 | 0.098*** |

(0.231) | (0.190) | (0.029) | |

sx | Controlled | ||

average | — | — | 0.608*** |

— | — | (0.148) | |

degree2 | — | — | 0.021* |

— | — | (0.012) | |

Constant | 2.776** | 0.908 | −0.585*** |

(1.392) | (1.110) | (0.223) | |

corr(e.d1,e.d2) | — | — | −0.241** |

corr(e.FL1,e.d2) | — | — | −0.384 |

corr(e.FL1,e.d1) | — | — | −0.749*** |

Formula (2) is the estimation result of financial literacy and credit demand. It is shown that the financial literacy (FL1) has a prominent positive effect on the credit demand of the rural household, and the higher the financial literacy of the rural household, the bigger the possible credit demand will be. The reasons might be as follows: On the one hand, when the rural household of a higher financial literacy level understands more about the loan application conditions and procedures of the financial organisation, the credit constraint caused by lack of financial literacy will be reduced, while the financial credit demand will be increased [25]; on the other hand, when the rural household improves its financial literacy level, it is more willing or it is easier for it to make use of the financial instruments to alter the current status of lack of innovation and investment opportunity and transform it in a positive direction – for example, the rural household can afford to entertain a more positive attitude towards participation in investment, and thus start-up businesses by availing credit, which would release the household’s potential credit demand. Besides, the householder’s gender also has a remarkable influence on his credit demand; the male householder’s demand on credit will be more perceptible. This is possibly attributable to the fact that in a rural family, the male householder is the economic kingpin of the family, and therefore, in order to promote the family’s living conditions and economic income, his demand on credit will be increased.

Formula (1) represents the estimation result of financial knowledge and credit constraint. The result indicates that the effect of financial knowledge on the credit constraint is not prominent, which is inconsistent with the cognition of this thesis. Bearing comprehensive financial knowledge, the rural household has a certain degree of understanding of the credit policies and interest rates of the organisations and the capital operation procedures of the organisations. In the meantime, a higher financial knowledge level could drive the wealth accumulation of the rural family, promote the rural household’s repayment capacity and reduce the rural household’s constraint met in the credit process. There are reasons for the phenomenon that the effect of financial literacy on the rural household’s credit constraint is not prominent. The credit constraints encountered by the rural households can be divided into formal and informal credit constraints, according to the financial organisation partition; therefore, in the following subsection, the relationship between financial literacy and credit constraint will be studied from these two aspects, namely formal and informal credit constraints.

Table 5 indicates the estimation results of financial literacy and formal credit demand, formal credit constraint and informal credit demand, informal credit constraint. Formulas (4)–(6) represent the financial literacy’s estimation result of formal credit demand and constraint, while Formulas (7)–(9) represent the financial literacy’s estimation result on informal credit demand and constraint. In this regard, corr(e.y1,e.y2) and corr(e.y3,e.y4) are, respectively, prominent at the levels of 5% and 10%, indicating that a sample selectivity problem exists in the rural household credit constraint estimation formula. Besides, Formulas (6) and (8), individually, are the estimation results of the instrumental variable, showing that the village average financial literacies are prominent at the level of 1 in both formulas, and additionally, orr(e.FL1,e.y2), corr(e.FL1,e.y1) and corr(e.y3,e.y4) are remarkable at the levels of 1%, 5% and 10%, respectively, indicating that the formula of rural household formal credit constraint and informal credit constraint does not bear a weak instrumental variable problem but the endogeneity problem.

Financial literacy and credit demand, credit constraint: formal and informal

FL1 | −2.131*** | 2.206*** | — | 1.491 | 2.558*** | — |

(0.795) | (0.798) | — | (0.971) | (0.581) | — | |

genr | −0.269 | 0.581*** | −0.015 | 0.116 | 0.405** | −0.014 |

(0.263) | (0.214) | (0.043) | (0.227) | (0.189) | (0.043) | |

degree1 | 0.146** | −0.079 | 0.022 | 0.063 | −0.008 | 0.019 |

(0.071) | (0.086) | (0.016) | (0.090) | (0.080) | (0.016) | |

age | −0.001 | −0.001 | −0.002* | 0.008 | 0.007 | −0.002* |

(0.006) | (0.007) | (0.001) | (0.007) | (0.006) | (0.001) | |

attitude | — | 0.090 | — | — | −0.431*** | — |

— | (0.146) | — | — | (0.137) | — | |

labour | 0.220 | −0.601* | 0.112* | −0.406 | −0.553* | 0.106* |

(0.334) | (0.339) | (0.064) | (0.351) | (0.291) | (0.064) | |

family | 0.094* | 0.098 | 0.008 | −0.074 | −0.001 | 0.007 |

(0.056) | (0.062) | (0.010) | (0.049) | (0.044) | (0.010) | |

dista | −0.199* | −0.090 | 0.018 | −0.087 | −0.043 | 0.018 |

(0.115) | (0.117) | (0.021) | (0.112) | (0.103) | (0.021) | |

income | 0.105 | −0.200 | 0.067*** | −0.275** | −0.119 | 0.066*** |

(0.115) | (0.125) | (0.021) | (0.124) | (0.107) | (0.020) | |

social | −0.058** | −0.016 | −0.003 | 0.022 | −0.046* | −0.004 |

(0.026) | (0.027) | (0.005) | (0.024) | (0.025) | (0.005) | |

polic | 0.110 | −0.098 | 0.014 | −0.330* | 0.096 | 0.012 |

(0.158) | (0.202) | (0.036) | (0.176) | (0.178) | (0.036) | |

saving | 0.074 | 0.097 | 0.095*** | −0.134 | −0.157 | 0.096*** |

(0.170) | (0.224) | (0.029) | (0.188) | (0.166) | (0.029) | |

sx | Controlled | |||||

average | — | — | 0.657*** | — | — | 0.649*** |

— | — | (0.144) | — | — | (0.143) | |

degree2 | — | — | 0.003 | — | — | 0.011 |

— | — | (0.013) | — | — | (0.012) | |

Constant | 0.340 | 1.739 | −0.608*** | 3.115*** | 0.848 | −0.603*** |

(1.016) | (1.158) | (0.223) | (1.146) | (1.002) | (0.223) | |

corr(e.y1,e.y2) | –0.353** | — | ||||

corr(e.FL1,e.y2) | 0.557*** | — | ||||

corr(e.FL1,e.y1)∣ | –0.523** | — | ||||

corr(e.y3,e.y4) | — | –0.528* | ||||

corr(e.FL1,e.y4) | — | –0.350 | ||||

corr(e.FL1,e.y3) | — | –0.577*** | ||||

Sample number | 530 |

Formula (5) represents the estimation result of the financial literacy on the formal credit demand. As can be seen from Table 5, the effect of financial literacy on the formal credit demand is positive and remarkable at the significance level of 1%. As respects the control variable, the householder gender has a prominent positive effect; this is evidenced by the fact that compared with the female householder, the male householder has a more remarkable demand on the credit. This observation can possibly be attributed to the fact that in the common rural family, the male generally is the economic kingpin. In order to promote the living conditions and level-up the family income, his demand on credit will be enhanced. Formula (8) represents the financial literacy’s estimation result on the informal credit demand. Compared with Formula (5), both of them have the same effect on the financial literacy variable and the householder gender variable. For the rural households under informal credit constraint, the risk attitude has a prominent negative effect on its credit demand, and the rural household preferring risks has a credit demand smaller than that of the rural household avoiding risks.

As can be seen from Formula (4), the financial knowledge bears an obvious negative effect on the formal credit constraint, indicating that the higher the rural household’s knowledge level, the smaller the formal credit constraint will be. The reasons may be as follows: On the one hand, under the situation that the rural household does not possess a good understanding of the loan products, procedures and policies of the financial organisation, it arrives at the misunderstanding that there is no possibility of obtaining the loan and then gives up the application [26]; on the other hand, in case that the rural household gets a better understanding of the credit policies, its formal credit demand will be enhanced and the formal credit constraint will be reduced [27]. Besides, the higher the householder’s education background, the more his knowledge of finance, security and investment; accordingly, the better he understands the loan policies of the financial organisation, the more reduced would the possibility be of his facing a credit constraint. When the family size is perceptibly positive in the confidence coefficient of 10%, it is consistent as expected. The family size reflects the family dependency coefficient; accordingly, the smaller the family size, the lesser the number of elder persons and children will be; therefore, the smaller the family dependency coefficient, the greater the limitation will be in the rate of being constrained by formal credit. The yearly family social interaction expenditure under the confidence coefficient of 5% has passed the significance test, and the social interaction reflects the rural household’s social relationship. The more the social expenditure, the better the rural household’s interpersonal relationship will be, and the more the reduction will be in the rate of credit constraints faced by rural households. The farther the distance to the financial branch, the less willing the rural household going to the financial organisation or branch will be, and therefore, the lower would the credit constraint be.

Formula (7) represents the analysis result of the effect of financial literacy on the informal credit constraint. It is shown that the financial knowledge has a positive impact on the informal credit constraint and such an effect is not prominent, which is inconsistent with the expectation of this thesis. As regards the aspect of control variable, a variety of variables such as the rural household’s gender, nationality and family size all are not prominent at the significance level of 5% or even 10%. The reason for such a phenomenon might be the fact that informal credit means the rural household borrows money from non-financial institutions, such as civil organisations, family members and friends; however, in reality, there is a situation that even a rural household bears a certain degree of financial knowledge, and its family members or friends are not willing to remain in touch owing to its bad reputation, resulting in an informal credit constraint and a phenomenon that the core variable (financial literacy) is not prominent in the informal credit constraint model.

Hence, the non-significant effect of financial knowledge on the rural household’s overall credit constraint might be the result of the impact of the informal credit constraint in the overall credit constraints. In the areas of a lower economic level, compared with the formal financial organisations, the rural households prefer to borrow money from their family, relatives and friends. Even though a rural household bears a higher level of financial literacy, it will be constrained by informal credit due to its bad reputation, resulting in the consequence of the estimation of the effect of financial literacy on the overall credit constraint not being prominent.

A regression will be carried out with the variable- the number of correctly answering the financial questions replacing Financial Literacy 1 (FL1). For details: correct for 0 time = 0, correct for once = 1, correct for twice = 2, correct for 3 times = 3. The regression result is shown as Table 6, basically consistent with that in Table 4.

Robustness test 1

FL2 | 0.194 | 0.906*** | −0.683** | 0.687*** | 0.443 | 0.789*** |

(0.091) | (0.078) | (0.276) | (0.251) | (0.291) | (0.198) | |

Sample number | 530 |

In order to exclude the effect of extreme values, this thesis will firstly sort out according to the family income, delete the data in the top 5% and the low-ranking 5%, and regress the rest of data. As shown in Table 7, under the significance level of 1%, the financial literacy has a positive effect on the overall credit demand and a negative impact on the formal credit constraint, indicating that the results of Table 4 are robust.

Robustness test 2

FL1 | 1.385 | 2.810*** | −2.232*** | 1.824* | 0.998 | 2.338*** |

(1.165) | (0.579) | (0.646) | (0.955) | (1.105) | (0.663) | |

Sample size | 476 |

According to the factor of whether the householder has attended the technical training, the thesis has divided the samples into two groups: Team with Technical Training and Team without Technical Training. As shown in Table 8, for the householder who has ever attended the technical training, the financial knowledge does not have a prominent effect on the overall credit constraint and formal credit constraint but does have a positive significance impact on the informal credit constraint at the level of 5%; for the rural household that has remained without attending the technical training, the financial literacy does not have a prominent influence on the overall credit constraint and the informal credit constraint but does have a negative significance effect on the formal credit constraint at the level of 10%.

Heterogeneity analysis 1

FL1 | 0.696 | −0.045 | 1.117** | 0.505 | −0.509* | 0.436 |

(0.793) | (0.353) | (0.538) | (0.341) | (0.265) | (0.310) | |

Sample size | 187 | 343 |

The reasons may be as follows: Commonly, the peasant attending technical trainings would have a higher income; his demand on credit would not be strong; as there is an enhancement in the financial knowledge level, the tendency to seek informal credit is lower, and therefore, it is easier for the peasant of a higher financial literacy level to be constrained by informal credit; for the rural household that has remained without attending the technical training and with relatively lower income, it would be easier for its members to conduct themselves according to norms of credit-worthy behaviour. As the financial literacy level increases, such a rural household understands the procedures and conditions of formal credit better, and thus it is harder for it to be constrained by formal credit.

The thesis has divided groups according to the factor of whether the householder prefers risk. As shown in Table 9, for the rural household preferring risks, the financial literacy does not have a prominent effect on overall credit constraint and formal credit constraint but has a positive impact on the informal credit constraint at the level of 10%; for the rural household without risk preference, the financial literacy does not have a remarkable impact on the formal credit constraint but has a positive effect on overall credit constraint and informal credit constraint at the levels of 10% and 5%. Besides, compared with the group without risk preference, the financial literacy of the risk preference group has a weaker impact on the informal credit constraint.

Heterogeneity analysis 2

FL1 | 0.580 | −0.406 | 0.579* | 1.152* | −0.485 | 1.071** |

(0.357) | (0.280) | (0.326) | (0.589) | (0.355) | (0.457) | |

Sample size | 310 | 220 |

The reasons may be the following: the rural household with risk preference, compared with the one without risk preference, bears a stronger wish for expanding the production scale and enjoys a higher income that increases concomitantly with the levelling-up of financial literacy; therefore, compared with the rural household that does not prefer risks, its financial literacy has a relatively weaker positive effect on the informal credit constraint.

Since the preamble has analysed the effect of financial literacy on the rural household’s credit constraint, the analysis result has shown that the financial literacy could effectively ease the rural household’s credit constraint. As the marketisation develops, the rural household income increases and the formal finance grows, how will the influence of financial literacy on the rural household credit constraint change? This latter part of the thesis will emphasise on the study on this issue.

To probe into the changing effects of financial literacy on the rural household credit constraint in the marketisation progress (the restaurant consumption expenditure of the peasant household in the overall food expenditure in 2015 has been used to make the measurement), the thesis has introduced the interaction terms of financial literacy and peasant household marketisation degree, with detailed results shown in Table 10.

Extending study 1

FL1 | 1.395* | 0.772 | −2.905*** | 0.709 | 2.513*** | 0.884 |

mark | 1.354 | 1.060 | −1.303* | 0.711 | 1.695** | 0.767 |

mark*FL1 | −0.657 | 1.631 | 1.409* | 0.981 | −1.224 | 1.106 |

Sample size | 531 |

As shown in Table 10, at the level of 10%, the easing effect of financial literacy on the formal credit constraint will be perceptibly weaker as the marketisation progresses; besides, the influence of financial literacy on the overall credit constraint and informal credit constraint will decline slightly as the marketisation develops. This indicates that the rural household with a higher financial literacy level bears an highly evident declining advantage in financing through formal financial channels and a slightly declining disadvantage in financing through informal financial channels.

To explore the effect of financial literacy on the rural household credit constraint in the increasing progress of peasant income, the thesis has introduced the interaction terms of financial literacy and rural household family net income, as shown in Table 11.

Extending study 2

FL1 | 10.380*** | 3.807 | −2.712 | 2.739 | 12.780*** | 3.193 |

income | 0.238 | 0.276 | 0.067 | 0.197 | 0.320 | 0.238 |

income*FL1 | −0.820** | 0.382 | 0.058 | 0.255 | −1.079*** | 0.329 |

Sample size | 531 |

As shown in Table 11, the enhancing effect of financial literacy on the overall credit constraint and the informal credit constraint will get weaker as the rural household income increases, prominent at the levels of 5% and 1%; besides, the easing effect of financial knowledge on the formal credit constraint will be weaker as the rural household income grows, which is not remarkable. It is indicated that the rural household with a higher financial literacy level will have an highly evident declining disadvantage in financing through informal channels and a slightly declining advantage in financing through formal channels.

To explore the changes of the effect of financial literacy on the credit constraint of the rural household in the formal financial development progress (the median of the distance between the village and the nearest formal financial organisation or branch is used as the basis for computation), the thesis has introduced the interaction terms of financial literacy and formal financial development, as shown in Table 12.

Extending study 3

FL1 | 2.443* | 1.474 | −2.727** | 1.095 | 2.680** | 1.288 |

distance | 0.337 | 0.344 | 0.010 | 0.247 | 0.025 | 0.280 |

distance*FL1 | −0.303 | 0.507 | 0.343 | 0.355 | −0.581* | 0.405 |

Sample size | 531 |

As shown in Table 12, the enhancing effect of financial literacy on the informal credit constraint will be weaker as there is progress in formal financial development, which is prominent at the level of 10%; besides, the easing effect of the function of financial literacy on the formal credit constraint has been weaker as the formal financial development progresses; and, similarly, the enhancing effect of financial literacy on the overall credit constraint declines, which is also concomitant with the progress in the formal financial development; these patterns are not remarkable. It is shown that the rural household with a higher financial literacy level has a highly evident declining disadvantage in financing through informal financial channels and a slightly declining advantage in financing through formal financial channels.

Based on the investigation data obtained from the rural households in Shaanxi and Yunnan in 2015, on the premise of considering endogeneity and sample selectivity, the effects of financial literacy on the credit behaviours of the rural household have been studied. The empirical study has shown that the financial literacy bears a positive prominent effect on the overall credit demand, the formal credit demand and the informal credit demand. The promotion of financial literacy level will stimulate the rural household’s credit demand; in the meantime, the financial literacy has no significant effect on the overall credit constraint and the informal credit constraint has but a prominent negative effect on the formal credit constraint. This has indicated that the levelling-up of financial literacy is good for easing the formal credit constraint that the rural household is confronted with. Besides, the financial literacy has different influences on the credit constraint according to the answer to whether or not the rural household has attended the technical training, and whether or not risks are preferred.

The research result of this thesis bears a meaning that is expected to provide an enlightening insight for the policy-making function. Firstly, the levelling-up of financial literacy could stimulate the rural household’s credit demand, and it bears a significant meaning of improving the peasant income and promoting economic growth. Therefore, targeting the problematic phenomenon that the villages share, namely the weakness by way of lack of financial literacy, a variety of channels should be opened for financial literacy education to promote the overall financial literacy level of the rural households in China. Secondly, the promotion of financial literacy level could effectively restrain the formal credit constraint that the rural households face; therefore, when the financial organisations are expanding the financial business and reducing the financial service thresholds, they should make endeavours to correct the rural households’ cognitive bias on financial institutions and guide the rural households to use the financial services correctly and reasonably. In addition, the financial knowledge should be popularised and made simple and easy to understand. Owing to different genders, education backgrounds, risk attitudes and financial literacies, the various households are characterised by their projection of varying demands on the financial system, all of which, nonetheless, need a basic degree of financial literacy. Therefore, the popularisation of financial knowledge should be implemented in an effective and reasonable way, keeping in mind the requirements that apply for different people.

#### Questions 1 and 2 involved in the questionnaire

Question | Answers and options | |
---|---|---|

Q1 | In the recent 3 years, in case of loan demand and contact with the bank or credit cooperatives, which option fits your reality the most? | ① No need to borrow |

Q2 | In the recent 3 years, in case of loan demand and contact with family members, friends and neighbours, which option fits your reality the most? | ① No need to borrow |

#### Heterogeneity analysis 1

FL1 | 0.696 | −0.045 | 1.117** | 0.505 | −0.509* | 0.436 |

(0.793) | (0.353) | (0.538) | (0.341) | (0.265) | (0.310) | |

Sample size | 187 | 343 |

#### Correlation coefficient table

FL1 | 1 | ||||

Income | 0.2369 |
1 | |||

Social | 0.1421 |
0.3634 |
1 | ||

Saving | 0.2593 |
0.2116 |
0.2805 |
1 | |

Attitude | 0.3035 |
0.1126 |
−0.0207 | 0.1120 |
1 |

#### Variable definition and descriptive statistics

Credit constraint | d2 | Whether suffer from credit constraint during 2013–2015: yes = 1, no = 0 | 488 | 0.926 | 0.262 | 0 | 1 |

Credit demand | d1 | Whether have credit demand during 2013–2015: yes = 1, no = 0 | 530 | 0.921 | 0.270 | 0 | 1 |

Formal credit constraint | y2 | Whether suffer from formal credit constraint during 2013–2015: yes = 1, no = 0 | 472 | 0.646 | 0.479 | 0 | 1 |

Formal credit demand | y1 | Whether have formal credit demand during 2013–2015: yes = 1, no = 0 | 530 | 0.891 | 0.312 | 0 | 1 |

Informal credit constraint | y4 | Whether suffer from informal credit constraint during 2013–2015 | 436 | 0.833 | 0.374 | 0 | 1 |

Informal credit demand | y3 | Whether have informal credit demand during 2013–2015: yes = 1, no = 0 | 530 | 0.823 | 0.382 | 0 | 1 |

Financial Literacy 1 | FL1 | Set up six dummy variables according to the three questions (interest rate, currency inflation, and comparison between currency inflation and interest rate); each financial question will bring up two dummy variables; through the FA, get Financial Literacy 1’s score | 530 | 0.577 | 0.326 | 2.19e–07 | 1 |

Financial Literacy 2 | FL2 | Number of correctly answering the financial questions: correct for 0 = 0, correct for 1 = 1, correct for 2 = 2, correct for 3 = 3 | 530 | 1.349 | 1.014 | 0 | 3 |

Householder gender | genr | Household gender: male = 1, female = 0 | 530 | 0.891 | 0.312 | 0 | 1 |

Household’s education background | degree1 | Householder’s education background: didn’t attend primary school = 1, primary school = 2, middle school and junior college = 4, junior college and higher = 5 | 530 | 2.953 | 0.932 | 1 | 5 |

Householder’s age | age | Householder’s age in 2015; unit: years of age | 530 | 47.23 | 12.01 | 22 | 89 |

Risk attitude | attitude | Preferring risk = 1, not preferring risk = 0 | 530 | 0.585 | 0.493 | 0 | 1 |

Labour force proportion | labour | The proportion of family labour force in the overall number, unit:% | 530 | 0.570 | 0.227 | 0.143 | 1 |

Family size | family | Family size in 2015 | 530 | 4.345 | 1.572 | 1 | 13 |

Distance to financial organisations | dista | Distance to the nearest financial organisation, take the logarithm; unit: kilometre | 530 | 1.256 | 0.628 | 0 | 3.434 |

Family yearly income | income | Total family income in 2015, unit: 10,000 yuan | 530 | 10.47 | 0.714 | 8.517 | 13.82 |

Social expenditure | social | Family’s social expenditure for interaction, unit: 1000 yuan | 530 | 6.200 | 3.131 | 0 | 10.82 |

Political identification | polic | Whether is party member: yes = 1, no = 0 | 530 | 0.168 | 0.374 | 0 | 1 |

Family deposit | saving | Whether the family has deposits in 2015, yes = 1, no = 0 | 530 | 0.579 | 0.494 | 0 | 1 |

Shaanxi | sx | Whether belongs to Shaanxi region, yes = 1, no = 0 | 530 | 0.449 | 0.498 | 0 | 1 |

Highest education background | degree2 | The highest education background of the family members: didn’t attend primary school = 1, primary school = 2, middle school = 3, junior school and junior college = 4, junior college and higher = 5; the instrumental variable of Financial Literacy 1 | 530 | 3.774 | 1.042 | 1 | 5 |

The village’s average Financial literacy level | average | The average value of the Financial Literacy 1 of the same village will be the instrumental variable of Financial Literacy 1 | 530 | 0.577 | 0.099 | 0.349 | 0.705 |

Technical training | train | Whether attended a technical training during 2013–2015: attended = 1, no = 0 | 530 | 0.353 | 0.478 | 0 | 1 |

#### Financial literacy and credit demand, credit constraint: formal and informal

FL1 | −2.131*** | 2.206*** | — | 1.491 | 2.558*** | — |

(0.795) | (0.798) | — | (0.971) | (0.581) | — | |

genr | −0.269 | 0.581*** | −0.015 | 0.116 | 0.405** | −0.014 |

(0.263) | (0.214) | (0.043) | (0.227) | (0.189) | (0.043) | |

degree1 | 0.146** | −0.079 | 0.022 | 0.063 | −0.008 | 0.019 |

(0.071) | (0.086) | (0.016) | (0.090) | (0.080) | (0.016) | |

age | −0.001 | −0.001 | −0.002* | 0.008 | 0.007 | −0.002* |

(0.006) | (0.007) | (0.001) | (0.007) | (0.006) | (0.001) | |

attitude | — | 0.090 | — | — | −0.431*** | — |

— | (0.146) | — | — | (0.137) | — | |

labour | 0.220 | −0.601* | 0.112* | −0.406 | −0.553* | 0.106* |

(0.334) | (0.339) | (0.064) | (0.351) | (0.291) | (0.064) | |

family | 0.094* | 0.098 | 0.008 | −0.074 | −0.001 | 0.007 |

(0.056) | (0.062) | (0.010) | (0.049) | (0.044) | (0.010) | |

dista | −0.199* | −0.090 | 0.018 | −0.087 | −0.043 | 0.018 |

(0.115) | (0.117) | (0.021) | (0.112) | (0.103) | (0.021) | |

income | 0.105 | −0.200 | 0.067*** | −0.275** | −0.119 | 0.066*** |

(0.115) | (0.125) | (0.021) | (0.124) | (0.107) | (0.020) | |

social | −0.058** | −0.016 | −0.003 | 0.022 | −0.046* | −0.004 |

(0.026) | (0.027) | (0.005) | (0.024) | (0.025) | (0.005) | |

polic | 0.110 | −0.098 | 0.014 | −0.330* | 0.096 | 0.012 |

(0.158) | (0.202) | (0.036) | (0.176) | (0.178) | (0.036) | |

saving | 0.074 | 0.097 | 0.095*** | −0.134 | −0.157 | 0.096*** |

(0.170) | (0.224) | (0.029) | (0.188) | (0.166) | (0.029) | |

sx | Controlled | |||||

average | — | — | 0.657*** | — | — | 0.649*** |

— | — | (0.144) | — | — | (0.143) | |

degree2 | — | — | 0.003 | — | — | 0.011 |

— | — | (0.013) | — | — | (0.012) | |

Constant | 0.340 | 1.739 | −0.608*** | 3.115*** | 0.848 | −0.603*** |

(1.016) | (1.158) | (0.223) | (1.146) | (1.002) | (0.223) | |

corr(e.y1,e.y2) | –0.353** | — | ||||

corr(e.FL1,e.y2) | 0.557*** | — | ||||

corr(e.FL1,e.y1)∣ | –0.523** | — | ||||

corr(e.y3,e.y4) | — | –0.528* | ||||

corr(e.FL1,e.y4) | — | –0.350 | ||||

corr(e.FL1,e.y3) | — | –0.577*** | ||||

Sample number | 530 |

#### Robustness test 1

FL2 | 0.194 | 0.906*** | −0.683** | 0.687*** | 0.443 | 0.789*** |

(0.091) | (0.078) | (0.276) | (0.251) | (0.291) | (0.198) | |

Sample number | 530 |

#### Extending study 3

FL1 | 2.443* | 1.474 | −2.727** | 1.095 | 2.680** | 1.288 |

distance | 0.337 | 0.344 | 0.010 | 0.247 | 0.025 | 0.280 |

distance*FL1 | −0.303 | 0.507 | 0.343 | 0.355 | −0.581* | 0.405 |

Sample size | 531 |

#### Heterogeneity analysis 2

FL1 | 0.580 | −0.406 | 0.579* | 1.152* | −0.485 | 1.071** |

(0.357) | (0.280) | (0.326) | (0.589) | (0.355) | (0.457) | |

Sample size | 310 | 220 |

#### Extending study 2

FL1 | 10.380*** | 3.807 | −2.712 | 2.739 | 12.780*** | 3.193 |

income | 0.238 | 0.276 | 0.067 | 0.197 | 0.320 | 0.238 |

income*FL1 | −0.820** | 0.382 | 0.058 | 0.255 | −1.079*** | 0.329 |

Sample size | 531 |

#### Robustness test 2

FL1 | 1.385 | 2.810*** | −2.232*** | 1.824* | 0.998 | 2.338*** |

(1.165) | (0.579) | (0.646) | (0.955) | (1.105) | (0.663) | |

Sample size | 476 |

#### Extending study 1

FL1 | 1.395* | 0.772 | −2.905*** | 0.709 | 2.513*** | 0.884 |

mark | 1.354 | 1.060 | −1.303* | 0.711 | 1.695** | 0.767 |

mark*FL1 | −0.657 | 1.631 | 1.409* | 0.981 | −1.224 | 1.106 |

Sample size | 531 |

#### Financial literacy, credit demand and credit constraint

FL1 | 1.699 | 2.964*** | — |

(1.059) | (0.441) | — | |

genr | −0.224 | 0.509** | −0.013 |

(0.330) | (0.212) | (0.043) | |

degree1 | 0.066 | −0.060 | 0.016 |

(0.112) | (0.082) | (0.016) | |

age | 0.012 | 0.006 | −0.002* |

(0.008) | (0.006) | (0.001) | |

attitude | — | -0.044 | — |

— | (0.126) | — | |

labour | −0.161 | −0.737** | 0.100 |

(0.444) | (0.321) | (0.064) | |

family | −0.018 | 0.044 | 0.006 |

(0.059) | (0.055) | (0.010) | |

dista | −0.266** | −0.065 | 0.017 |

(0.133) | (0.115) | (0.021) | |

income | −0.219 | −0.153 | 0.065*** |

(0.147) | (0.115) | (0.020) | |

social | −0.017 | −0.020 | −0.004 |

(0.030) | (0.026) | (0.005) | |

polic | −0.274 | −0.134 | 0.010 |

(0.215) | (0.185) | (0.036) | |

saving | −0.015 | −0.079 | 0.098*** |

(0.231) | (0.190) | (0.029) | |

sx | Controlled | ||

average | — | — | 0.608*** |

— | — | (0.148) | |

degree2 | — | — | 0.021* |

— | — | (0.012) | |

Constant | 2.776** | 0.908 | −0.585*** |

(1.392) | (1.110) | (0.223) | |

corr(e.d1,e.d2) | — | — | −0.241** |

corr(e.FL1,e.d2) | — | — | −0.384 |

corr(e.FL1,e.d1) | — | — | −0.749*** |

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