This paper offers an analysis of the effect of micro-credit to financial inclusion. We use the survey data of Shandong Province in China since 2010 to 2016, and measure the index of financial inclusion to build the sample for empirical study. This paper examines the role of micro-credit for enlarging the coverage of financial service and decreasing the financial cost. Finally, we give the evidence that the development of micro-credit has the positive effect on promoting the financial inclusion.
Financial inclusion, defined as the use of formal financial service for all sectors of society at the affordable cost (Allen et al., 2016) , increasingly becomes a popular research field that attracts wide attention from academia, government, and industry. During the 2016 G20 summit, G20 leaders and scholars made great efforts trying to meet the challenge of promoting the financial inclusion around the world (GPFI, 2015) , especially to guide the developing countries to relieve the financial exclusion and improve the effect of financial service. In theory, it is crucial for those in the developing countries who are financially excluded to receive necessary financial services. This will significantly increase their ability of financing and investment and improve their economic situation through financial activities. Such an arrangement also contributes to poverty reduction and economic growth in these countries (Bruhn & Love, 2014) . In practice, the development of inclusive finance includes not only the access to various financial services at a reasonable cost for all individuals but also the expansion of the coverage of financial service through providing transparent regulations on financial institutions (Levine, 1999; Beck et al. 2007) .
In the past decades, many scholars studied financial exclusion and found that financial exclusion led to the lack of basic financial services and became an important cause of poverty. For instance, the lack of bank accounts makes liquidity management and payment difficult, which causes a high fee level associated with the use of money order or other cash services (Lusardi, 2010) ; alienated from banks and unable to get formal financial service, individuals in low income regions have to borrow from informal organizations and undertake higher rates of interest (Jones, 2001; Palmer & Conaty, 2002) ; the lack of financial institutions aggravates the gap between the supply of and the demand for basic financial service and raises the entry threshold of market to low income individuals (Branch & Cifuentes, 2001; Jones, 2006) . To solve these problems, many countries made efforts to design a new type of financial intermediary which provides micro-credit to meet the financial needs of the poor, for example, the local community unions which were established by hundreds of volunteers in Britain (Jones, 1999) . Supported by government policies and public grants, micro-credit expanded rapidly and brought positive effects to the low-income individuals during the 1980s and 1990s; however, the effects in recent years are not as apparent as before. Some scholars found a very limited effect of micro-credit on low income communities. Some even claimed that the new financial intermediary was doom to a failure (Dayson et al., 2001; Mckillop et al., 2007) . From this perspective, microfinance institutions may produce positive effects in increasing the coverage of financial services, but the effects may not be persistent. Relying on microfinance institutions cannot completely resolve the problem of financial exclusion and narrow the gap between the rich and the poor.
The development of inclusive finance has the potential to solve the problem of financial exclusion and, hence, the poverty; but the drawbacks are also obvious. If micro-credit can promote the development of inclusive finance and increase the inclusiveness of regional financial system, it should be included in the “Inclusive Financial System”. With this background, the research question of this paper is two-fold: 1) construct the Financial Inclusion Index (FII) according to the intrinsic requirement of inclusive finance, taking into account both the coverage of financial service and the cost of financial service; and 2) conduct an empirical analysis on the effects of micro-credit on the regional development of inclusive finance.
This paper is primarily based on the survey data of Shandong Province in China due to the following reasons. First, China is the largest developing economy with a big gap between rich and poor in the world. The problem of financial exclusion in some areas is more prominent. In recent years, China has made some important progress in the development of micro-credit and inclusive financial institutions and therefore becomes an ideal case of study. Second, China has not established a systematic statistical database that covers key variables such as account information, distribution of financial institutions, and interest rates charged by informal financing. Quantitative analysis for all Chinese provinces is not feasible. One possible way out is to focus on the representative provinces, which can mimic the average situation of the whole country in industrial structure, financial development level, population structure, etc. Following these criteria, Shandong Province is an appropriate pick.
The remainder of this paper is structured as follows. Section 2 describes our data and methodology for constructing the IFI. Section 3 presents the empirical analyses on the effects of micro-credit on financial inclusion. Section 4 concludes.
Inclusive financial system contains several dimensions. We follow a multidimensional approach to construct the index of financial inclusion (IFI). Our approach is similar to the one used by UNDP for computation of some well-known development indexes such as the HDI and the HPI1. As in the case of these indexes, we first calculate a sub-index index for each dimension of financial inclusion. For example, the dimension index for the ith dimension k i , is computed by the following equation
k i = λ i ( A i − m i ) / ( M i − m i ) (1)
where A i is the actual value of dimension i, M i is the upper limit of the value of dimension i, and m i is the lower limit of the value of dimension i. λ i is the weight attached to dimension I, which can be computed by Equation (2)
λ i = ( σ i / A ¯ i ) / ∑ i = 1 n ( σ i / A ¯ i ) (2)
where σ i is the standard deviation of dimension i and A i is the mean of dimension i. Equation (1) implies that 0 ≤ k i ≤ λ i . The higher the value of k , the more progress made on dimension i. Suppose we consider n dimensions of financial inclusion, then an overall state of financial inclusion can be represented by a point K i = ( K 1 , K 2 , ⋯ K n ) on the n-dimensional Cartesian space. In this space, the point O = ( 0 , 0 , ⋯ 0 ) represents the worst situation of financial inclusion while the point Λ = ( λ 1 , λ 2 ⋯ λ n ) represents the best situation. Then the IFI can be calculated by the normalized inverse Euclidean distance of the point K from point Λ = ( λ 1 , λ 2 ⋯ λ n ) . The exact formula is
I F I i = 1 − ( w 1 − d 1 ) 2 + ( w 2 − d 2 ) 2 + ⋯ + ( w n − d n ) 2 ( w 1 2 + w 2 2 + ⋯ + w n 2 ) (3)
The IFI defined above can then be used to measure financial inclusion on different levels of economic aggregation during different time periods.
To the best of our knowledge, there has not been an universally recognized system of indicators for the development of financial inclusion. Many research institutions, including Finscope, AFI and World Bank2 have established their own indicator system according to the definition of financial inclusion. Based on their studies (Vighneswara, 2014; Allen et al. 2016; GPFI, 2015) , this paper selects 11 indicators that reflect information concerning the distribution of financial institutions3, account usage, small business loan, and financing costs to establish the core set of Financial Inclusion Indicators. The number of bank branches, POS machines, ATM, and accounts are selected to measure the coverage of financial services; the loan interest rate of small and micro enterprises, the loan interest rate of agriculture-related loans, and the interest rate of informal finance are selected to reflect the cost of financial service and establish the cost index of financial service. Other indicators are selected to reflect the application of the loan, and the development of the Macro-economy and finance.
We have constantly performed an annual survey for more than 300 micro-credit organizations in all 17 prefectures of Shandong province since 20094. During this period, the research team of this paper has conducted the continuous tracking of all the micro-credit organizations every year and acquired its operation data quarterly. Indicators in
Making use of the methodology introduced before, we calculated the annual IFI for all 17 prefectures in Shandong province during 2010-2016 and constructed
Index | Indicator | |
---|---|---|
Index of Financial Inclusion | Bank Branch (No. of per 10,000 adult person) | Coverage index of financial service |
POS Machines (No. of per 10,000 adult person) | ||
ATM Machines (No. of per 10,000 adult person) | ||
Accounts (No. of per 10,000 adult person) | ||
The loan interest rate of small and micro enterprises | Cost index of financial service | |
The loan interest rate of agriculture-related loans | ||
The interest rate of informal finance | ||
loans/GDP | Other indicators for reflecting the application of the loan, Macro-economy and finance | |
Per capita deposits/Per capital disposable income | ||
Proportion of small and micro enterprises loans | ||
Proportion of agriculture-related loans |
the Coverage index of financial service and Cost index of financial service based on the nature of financial inclusion.
It can be seen from
To accurately study the influence of micro-credit on inclusive finance in each prefecture and mitigate the impacts of unobserved variables, this paper adopts the panel data model. Since this paper established the IFI at the prefecture level, for each year, we take the simple average of all micro-credit organizations within a prefecture to reflect the overall performance of this prefecture. All variables related to the micro-credit organizations are treated in this way. The major regression model I is specified as:
I F D I i = α + β i T i t + u i + ε i t
T i t = ( corporate , Δ capital , Δ loan , r l o a n , Δ custom , R N P L s , g p r o f i t , P l o a n < 50 ) i t
Bank Branch | POS Machines | ATM Machines | Accounts | Loans/GDP | Per capita deposits/Per capital disposable income | Proportion of small and micro enterprises loans | Proportion of agriculture related loans | The loan interest rate of small and micro enterprises | The loan interest rate of agriculture related loans | The interest rate of informal finance | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Jinan | Mean | 1.825 | 5.429 | 94.929 | 61,577.78 | 0.156 | 4.873 | 0.165 | 0.055 | 6.837 | 6.403 | 8.959 |
Std.Dev | 0.088 | 1.075 | 35.274 | 18,569.26 | 0.086 | 0.136 | 0.295 | 0.122 | 0.763 | 0.612 | 0.678 | |
Qingdao | Mean | 1.782 | 5.326 | 82.338 | 64,140.78 | 0.167 | 4.371 | 0.223 | 0.054 | 6.997 | 8.312 | 17.357 |
Std.Dev | 0.075 | 1.353 | 33.24 | 13,268.1 | 0.241 | 0.152 | 0.398 | 0.147 | 0.947 | 0.242 | 0.621 | |
Zibo | Mean | 0.035 | 4.229 | 72.671 | 67,654.14 | 0.596 | 1.503 | 0.264 | 0.361 | 7.621 | 6.991 | 8.495 |
Std.Dev | 0.003 | 1.803 | 38.040 | 9387.51 | 0.026 | 0.018 | 0.227 | 0.376 | 0.611 | 0.216 | 0.689 | |
Zaozhuang | Mean | 0.770 | 1.857 | 72.571 | 25,675.00 | 0.522 | 1.267 | 0.369 | 0.451 | 8.184 | 8.102 | 8.029 |
Std.Dev | 0.031 | 0.541 | 20.313 | 5349.06 | 0.206 | 0.046 | 0.367 | 0.18 | 0.471 | 0.507 | 0.528 | |
Dongying | Mean | 2.563 | 4.097 | 69.429 | 65,412.77 | 0.587 | 5.230 | 0.539 | 0.428 | 10.323 | 8.095 | 7.320 |
Std.Dev | 0.293 | 0.401 | 14.593 | 22,231.21 | 0.089 | 0.738 | 0.024 | 0.012 | 0.737 | 0.701 | 0.697 | |
Yantai | Mean | 1.763 | 3.961 | 47.529 | 35,087.57 | 0.662 | 4.550 | 0.324 | 0.421 | 7.2 | 6.6 | 8.880 |
Std.Dev | 0.324 | 0.754 | 6.024 | 7034.80 | 0.050 | 0.161 | 0.032 | 0.024 | 0.009 | 0.7 | 0.610 | |
Weifang | Mean | 1.511 | 1.993 | 102.286 | 27,851.71 | 0.840 | 1.860 | 0.199 | 0.574 | 10.1 | 9.142 | 6.622 |
Std.Dev | 0.055 | 0.719 | 56.680 | 12,778.82 | 0.049 | 0.051 | 0.026 | 0.362 | 0.721 | 0.797 | 1.050 | |
jining | Mean | 1.097 | 1.954 | 60.971 | 8897.42 | 0.585 | 1.546 | 0.516 | 0.465 | 8.796 | 7.784 | 7.606 |
Std.Dev | 0.120 | 0.871 | 28.370 | 803.534 | 0.057 | 0.055 | 0.012 | 0.045 | 0.489 | 0.375 | 0.602 | |
Taian | Mean | 0.704 | 1.459 | 40.357 | 53211.16 | 0.479 | 1.353 | 0.289 | 0.455 | 7.777 | 7.810 | 8.446 |
Std.Dev | 0.031 | 0.707 | 20.886 | 6822.65 | 0.289 | 0.933 | 0.301 | 0.237 | 0.617 | 0.622 | 0.785 | |
Weihai | Mean | 0.049 | 4.007 | 116.931 | 57,336.93 | 0.606 | 2.848 | 0.198 | 0.390 | 8.1 | 7.1 | 8.545 |
Std.Dev | 0.004 | 0.146 | 7.038 | 25,471.70 | 0.020 | 0.441 | 0.036 | 0.009 | 0.5 | 0.004 | 0.490 | |
Rizhao | Mean | 1.207 | 2.099 | 62.400 | 30166.75 | 0.962 | 2.217 | 0.338 | 0.189 | 9.08 | 7.550 | 6.471 |
Std.Dev | 0.120 | 0.561 | 28.453 | 8847.22 | 0.035 | 0.288 | 0.654 | 0.319 | 0.500 | 0.443 | 0.727 | |
Laiwu | Mean | 0.093 | 2.336 | 91.613 | 36,342.21 | 0.912 | 2.083 | 0.260 | 0.214 | 9.324 | 7.865 | 7.581 |
Std.Dev | 0.005 | 0.654 | 25.310 | 15,233.63 | 0.021 | 0.164 | 0.039 | 0.019 | 0.576 | 0.544 | 0.488 | |
Dezhou | Mean | 1.361 | 1.722 | 67.214 | 27,552.70 | 0.501 | 1.314 | 0.377 | 0.634 | 8.053 | 7.240 | 8.230 |
Std.Dev | 0.178 | 0.795 | 26.975 | 12,174.30 | 0.026 | 0.075 | 0.596 | 0.082 | 0.850 | 0.633 | 0.409 | |
Linyi | Mean | 1.012 | 1.756 | 33.864 | 13,113.39 | 0.705 | 2.297 | 0.172 | 0.492 | 8.174 | 7.104 | 8.331 |
Std.Dev | 0.099 | 0.929 | 22.759 | 4056.47 | 0.621 | 0.692 | 0.153 | 0.1 | 0.280 | 0.329 | 0.680 | |
Liaocheng | Mean | 1.002 | 1.736 | 70.693 | 24,067.31 | 0.589 | 1.177 | 0.251 | 0.632 | 8.258 | 7.148 | 8.318 |
Std.Dev | 0.037 | 0.875 | 28.187 | 11,121.44 | 0.011 | 0.055 | 0.066 | 0.027 | 0.588 | 0.534 | 0.468 | |
Binzhou | Mean | 1.060 | 2.239 | 82.331 | 36,253.87 | 0.734 | 2.157 | 0.265 | 0.724 | 9.3 | 7.4 | 8.1 |
Std.Dev | 0.044 | 0.826 | 33.121 | 13,956.15 | 0.549 | 0.228 | 0.481 | 0.106 | 0.8 | 0.5 | 0.273 | |
Heze | Mean | 0.519 | 1.264 | 20.279 | 19,046.28 | 0.617 | 0.860 | 0.183 | 0.649 | 7.672 | 6.517 | 8.857 |
Std.Dev | 0.122 | 0.295 | 3.920 | 10,067.79 | 0.042 | 0.074 | 0.028 | 0.018 | 1.635 | 0.869 | 0.691 |
Notes: All the Data are from Shandong Statistical Yearbook (2011-2017), Shandong Financial Yearbook (2011-2017) and the City Statistical Yearbook (2011-2017) of the 17 cities.
Index | Year | Jinan | Qingdao | Zibo | Zaozhuang | Dongying | Yantai | Weifang | Jining | Taian |
---|---|---|---|---|---|---|---|---|---|---|
Index of Financial Inclusion | 2010 | 0.035 | 0.099 | 0.010 | 0.022 | 0.030 | 0.082 | 0.005 | 0.021 | 0.013 |
2011 | 0.224 | 0.210 | 0.203 | 0.117 | 0.330 | 0.303 | 0.148 | 0.195 | 0.092 | |
2012 | 0.420 | 0.451 | 0.411 | 0.280 | 0.508 | 0.352 | 0.368 | 0.335 | 0.163 | |
2013 | 0.636 | 0.332 | 0.602 | 0.664 | 0.656 | 0.572 | 0.668 | 0.685 | 0.626 | |
2014 | 0.744 | 0.547 | 0.771 | 0.795 | 0.763 | 0.459 | 0.845 | 0.857 | 0.817 | |
2015 | 0.780 | 0.785 | 0.893 | 0.851 | 0.851 | 0.764 | 0.918 | 0.855 | 0.846 | |
2016 | 0.820 | 0.953 | 0.915 | 0.888 | 0.889 | 0.977 | 0.977 | 0.855 | 0.853 | |
Coverage index of financial service | 2010 | 0.002 | 0.087 | 0.000 | 0.000 | 0.000 | 0.002 | 0.000 | 0.000 | 0.000 |
2011 | 0.197 | 0.108 | 0.202 | 0.090 | 0.335 | 0.243 | 0.126 | 0.171 | 0.072 | |
2012 | 0.437 | 0.154 | 0.416 | 0.274 | 0.580 | 0.437 | 0.383 | 0.332 | 0.155 | |
2013 | 0.716 | 0.307 | 0.620 | 0.730 | 0.748 | 0.701 | 0.687 | 0.718 | 0.666 | |
2014 | 0.967 | 0.412 | 0.812 | 0.906 | 0.957 | 0.944 | 0.937 | 0.966 | 0.988 | |
2015 | 0.924 | 0.854 | 0.953 | 0.981 | 0.981 | 0.987 | 0.981 | 0.971 | 0.965 | |
2016 | 0.889 | 0.997 | 0.974 | 0.942 | 0.993 | 0.971 | 0.989 | 0.979 | 0.940 | |
Cost index of financial service | 2010 | 0.554 | 0.654 | 0.539 | 0.501 | 0.338 | 0.668 | 0.296 | 0.523 | 0.378 |
2011 | 0.785 | 0.504 | 0.272 | 0.834 | 0.680 | 0.632 | 0.718 | 0.689 | 0.629 | |
2012 | 0.128 | 0.229 | 0.047 | 0.150 | 0.181 | 0.013 | 0.134 | 0.104 | 0.068 | |
2013 | 0.184 | 0.314 | 0.287 | 0.169 | 0.317 | 0.261 | 0.607 | 0.278 | 0.156 | |
2014 | 0.137 | 0.521 | 0.351 | 0.275 | 0.293 | 0.276 | 0.075 | 0.368 | 0.314 | |
2015 | 0.351 | 0.658 | 0.488 | 0.437 | 0.583 | 0.461 | 0.283 | 0.589 | 0.497 | |
2016 | 0.588 | 0.784 | 0.646 | 0.654 | 0.789 | 0.648 | 0.289 | 0.910 | 0.547 |
Index | Year | Weihai | Rizhao | Laiwu | Dezhou | Linyi | Liaocheng | Binzhou | Heze |
---|---|---|---|---|---|---|---|---|---|
Index of Financial Inclusion | 2010 | 0.018 | 0.042 | 0.022 | 0.016 | 0.004 | 0.011 | 0.011 | 0.094 |
2011 | 0.227 | 0.363 | 0.212 | 0.148 | 0.072 | 0.158 | 0.169 | 0.208 | |
2012 | 0.522 | 0.532 | 0.499 | 0.400 | 0.263 | 0.424 | 0.485 | 0.407 | |
2013 | 0.660 | 0.618 | 0.649 | 0.667 | 0.589 | 0.706 | 0.691 | 0.534 | |
2014 | 0.781 | 0.694 | 0.798 | 0.865 | 0.903 | 0.900 | 0.777 | 0.771 | |
2015 | 0.843 | 0.737 | 0.833 | 0.917 | 0.944 | 0.917 | 0.838 | 0.770 | |
2016 | 0.921 | 0.775 | 0.848 | 0.954 | 0.969 | 0.915 | 0.844 | 0.756 | |
Coverage index of financial service | 2010 | 0.000 | 0.000 | 0.000 | 0.000 | 0.001 | 0.000 | 0.000 | 0.000 |
2011 | 0.213 | 0.370 | 0.182 | 0.115 | 0.059 | 0.139 | 0.144 | 0.130 | |
2012 | 0.536 | 0.609 | 0.516 | 0.398 | 0.255 | 0.411 | 0.499 | 0.442 | |
2013 | 0.780 | 0.842 | 0.721 | 0.683 | 0.608 | 0.727 | 0.711 | 0.706 | |
2014 | 0.967 | 0.937 | 0.907 | 0.907 | 0.960 | 0.971 | 0.834 | 0.901 | |
2015 | 0.983 | 0.977 | 0.964 | 0.964 | 0.970 | 0.973 | 0.845 | 0.946 | |
2016 | 0.979 | 0.992 | 1.000 | 1.000 | 0.982 | 0.941 | 0.855 | 0.970 |
Cost index of financial service | 2010 | 0.487 | 0.397 | 0.491 | 0.649 | 0.363 | 0.609 | 0.343 | 0.648 |
---|---|---|---|---|---|---|---|---|---|
2011 | 0.763 | 0.795 | 0.657 | 0.878 | 0.662 | 0.941 | 0.427 | 0.578 | |
2012 | 0.000 | 0.106 | 0.000 | 0.044 | 0.026 | 0.124 | 0.330 | 0.152 | |
2013 | 0.379 | 0.422 | 0.254 | 0.279 | 0.314 | 0.123 | 0.203 | 0.048 | |
2014 | 0.412 | 0.380 | 0.486 | 0.474 | 0.394 | 0.399 | 0.194 | 0.712 | |
2015 | 0.345 | 0.590 | 0.534 | 0.708 | 0.576 | 0.568 | 0.700 | 0.796 | |
2016 | 0.676 | 0.894 | 0.781 | 0.839 | 0.781 | 0.777 | 1.000 | 0.934 |
Notes: All the Data are from our survey. Because of the authorization clause, we could not provide the original data to any company and individual, but can use the data for theoretical study and publish the results.
where I F D I i denotes the overall IFI; corporate and Δ capital denote the number of micro-credit organizations5 and the increase rate of registered capital, respectively, both reflecting the scale of development of micro-credit organizations; Δ loan and Δ custom denote the increase rates of loans and customers, respectively, both reflecting the supply capacity of financial service; r l o a n denotes the loan interest rate and reflects the cost of the financial service; R N P L s and g p r o f i t denote the non-performing loan (NPL) ratio of micro-credit organizations and the increase rate of profit, respectively, both reflecting their performance and risk; P l o a n < 50 denotes the proportion of customers with the single account balance less than RMB 500,000: since China’s micro-credit organizations are still at the policy-driven stage and undertake necessary policy functions at present, this paper select the“ Proportion of customers with the single account balance no more than RMB 500,000” as the control variable which can reflect the realization of policy goals. α and β i are the coefficients to be estimated. u i + ε i t is the compound disturbance term, in which u i is the intercept term reflecting the individual heterogeneity and ε i t is the disturbance term.
We first perform the Hausman test to pick the appropriate estimation method. It turns out that the F-statistics is 58.53 with a p-value of zero. Therefore we pick the fixed effects model and report the estimation results in
To check the stability of our model, this paper adopts LLC Test and MV Test to analyze the residual of the above regression. The P-values of these Tests are 0.08 and 0.07, respectively, showing that the model’s residual is stationary.
From
Variable | Coefficients | z statistics | P value |
---|---|---|---|
corporate | 0.017 | 4.37 | 0*** |
Δ capital | −0.193 | −0.79 | 0.442 |
g p r o f i t | 0.002 | 0.13 | 0.9 |
Δ loan | 0.001 | 2.14 | 0.048** |
r l o a n | −2.63 | −2.89 | 0.011** |
R N P L s | −1.83 | −2.01 | 0.061* |
Δ custom | 0.022 | 2.66 | 0.017** |
P l o a n < 50 | 0.77 | 6.82 | 0*** |
α | 0.858 | 5.68 | 0*** |
Notes: This table presents the results of fixed effect model for the time period from 2009 to 2015. ***, **and* denote the statistical significance at levels of 1%, 5%, and 10% respectively.
receive various financial services. Second, the coefficients of g p r o f i t and I F D I i are not statistically significant at the level of 10%, indicating that the internal operation of micro-credit organizations are not able to affect the whole financial market at the initial stage of financial inclusion6. Third, there are significantly positive correlations among Δ custom , Δ loan and I F D I i , showing a positive influence on the development of financial inclusion when the micro-credit organizations increase the scale of credit and expand their customers. This finding is consistent with existing studies on micro-credit. Fourth, there is a significantly negative correlation between r l o a n and I F D I i . Decreasing the loan rate will promote the development of financial inclusion. This is consistent with the goal of “providing quality financial services at the affordable cost”. When the loan rate of micro-credit organizations is lower, the customer will obtain financial services with a lower cost. Fifth, there is a significantly negative correlation between R N P L s and I F D I i , which indicates that the current operation risks of micro-credit organizations can affect the financial market. In particular, under the background of China ’s economic slowdown and increasing exposure of regional financial risks, financial market’s vulnerability gradually increases. When the NPL increases rapidly, it increases market friction, raises market threshold, and impedes the development of financial inclusion.
To summarize, the existence of micro-credit organizations has a positive influence on the development of financial inclusion. By establishing new institutions, increasing loans at reasonable interest rate level and attracting more customers, micro-credit organizations play a role in improving the access of financial services and lowering the entry cost. In the next section, we will further explore the nature of financial inclusion and investigate whether the micro-credit organizations are able to expand financial service coverage and lower the financial service costs.
The notion of inclusive finance includes two key components: to expand the coverage of financial service and to reduce the cost of financial service (Beck et al. 2007) . In what follows, we further check the impacts of micro-credit on the Coverage index and the Cost index of financial service.
The model II specification is similar:
I F D I ′ i = α ′ + β ′ i T ′ i t + u ′ i + ε ′ i t
T ′ i t = ( corporate , Δ capital , g p r o f i t , R N P L s , P l o a n < 50 , Δ custom A , Δ custom B ) i t
where I F D I ′ i denotes the Coverage index of financial service; Δ custom A denotes the growth rate of agriculture-related customers; Δ custom B denotes the growth rate of micro business customers; other variables share the same meanings as before.
The F-statistics of the Hausman Test is 59.53 with a p-value close to zero, lending support to a fixed effects model. The estimation results are reported in
Variable | Coefficients | z statistics | P value |
---|---|---|---|
corporate | 0.019 | 3.53 | 0.003*** |
Δ capital | −0.014 | −1 | 0.334 |
g p r o f i t | 0.007 | 2.42 | 0.028** |
R N P L s | −2.31 | 2.23 | 0.04** |
P l o a n < 50 | 0.943 | 5.99 | 0*** |
Δ custom A | 0.007 | 2.92 | 0.01** |
Δ custom B | 0.003 | 0.29 | 0.774 |
α | 0.525 | 4.67 | 0*** |
Notes: This table presents the results of fixed effect model. ***, **and* denote the statistical significance at levels of 1%, 5%, and 10% respectively.
loans to agriculture-related customers can expand the coverage of financial service, but increasing loans to micro business customers cannot. The main reason is because the total capital of micro-credit organizations is limited in rural area. If they focus on the micro business customers that needs more loans, the number of the customers cannot increase a lot; meanwhile, micro-credit organizations will have to compete with other financial institutions for lending to micro business customers. This will also weaken its improvement on financial service coverage. However, if they focus on agriculture-related customers (individual farmers in most cases), the number of customers will increase greatly, complementing other financial institutions and improving the coverage of financial services. Third, there is a significantly positive correlation between g p r o f i t and I F D I ′ i , implying that increasing the profit of micro-credit organizations helps to improve the coverage of financial services.
The Model III is specified as:
I F D I ″ i = α ″ + β ″ i T ″ i t + u ″ i + ε ″ i t
T ″ i t = ( corporate , Δ capital , g p r o f i t , r l o a n , R N P L s , P l o a n < 50 , P l o a n s ) i t
where I F D I ″ denotes the Coverage index of financial service cost; P l o a n s denotes the proportion of short-term loan; other variables share the same meanings as before.
The F-statistics of the Hausman test becomes 24.72 with a p-value of 0.001. The fixed effects model is estimated and the results are reported in
Variable | Regression coefficients | z statistics | P value |
---|---|---|---|
corporate | 0.001 | 0.22 | 0.831 |
Δ capital | 0.021 | 0.99 | 0.337 |
g p r o f i t | 0.012 | 5.63 | 0*** |
r l o a n | −7.99 | −7.61 | 0*** |
R N P L s | −2.701 | −1.93 | 0.072* |
P l o a n < 50 | 0.218 | 1.29 | 0.215 |
P l o a n s | 0.037 | 1.64 | 0.093* |
α ‴ | 1.45 | 5.99 | 0 |
Note: 1. This table presents the results of Fixed effect model. ***, **and* denote the statistical significance at levels of 1%, 5%, and 10% respectively.
short-term loans of micro-credit organizations can help to reduce the cost of financial services. The result is closely related to the origin of micro-credit organizations in China : in regional financial market, micro-credit organizations have more advantages in providing short-term loans. On the one hand, compared with informal finance such as private lending, the interest rate of micro-credit organizations is relatively low, and the cost advantage is obvious; on the other hand, compared with the formal finance such as commercial banks and rural credit cooperatives, the short-term lending through micro-credit organizations is relatively flexible. Therefore increasing the proportion of short-term loans of micro-credit organizations can help meet more demand for short-term capital of different enterprises, reduce the lending from informal finance, and effectively reduce the cost of financial services.
To summarize, the existence of micro-credit organizations has a positive influence on reducing the cost of financial services and increasing the coverage of the financial services. It provides significant evidence that the micro-credit organizations are able to make the financial service more available and affordable, and cause the same effect to the development of financial inclusion.
To sum up, scale expansion, internal management improvement, and credit expansion all produce positive impacts on the development of inclusive finance. As for the mechanism, increasing the supply of funds is the key to fill in the “gap” between the supply of and the demand for financial services. Following the nature of inclusive finance, this paper further carried out empirical research on whether development of micro-credit organizations is helpful to improve the coverage of financial services and to reduce the financial service cost. The empirical results in this paper show that the improved management of micro-credit organizations (e.g., expansion of scale, increasing the proportion of short-term loans, decreasing the loan interest rate, etc.) can help improve the coverage of financial services and reduce the financial service cost, confirming the positive effect on the overall development of inclusive finance. Meanwhile, we also found the difference of the effects of the increase rate of registered capital, non-performing loan ratio and other variables on each sub-index. This illustrates the uncertain effects of these factors on the development of inclusive finance and motivates future research questions in this field. Moreover, because of the large geographical area, there are significant variations across different regions regarding poor population distribution and natural endowments in China . How to take into account the impacts of geography and natural factors on the interaction between inclusive finance and micro-credit organizations is also left for future research.
We greatly appreciate the financial support of National Natural Science Foundation of China (71333009, 71273155, and 71503147) and the Special Fund of Shandong Social Science Foundation (16BJRJ01).
The authors declare no conflicts of interest regarding the publication of this paper.
Yi, J. H., Zhang, R., & Guo, F. (2018). Can Micro-Credit Promote Financial Inclusion? The Evidence from China. Journal of Financial Risk Management, 7, 428-441. https://doi.org/10.4236/jfrm.2018.74023