5.2.1. Data Processing

Same as above, we regard all the drawn data normalized and its characteristic of volatility is in Figure 5. We use the method of augmented Dickey-Fuller (ADF) test to identify the data stationarity. (There are some differences compared in Section 5.1. For the quarterly data existing obvious seasonality, we need go further difference for normalized data) and the results of ADF test are shown in Table 3.

**Figure 5.** The characteristic of volatility on related variables (Quarter).



The numbers in brackets are *p* value. \*, \*\* and \*\*\* indicate the 10%, 5% and 1% significance level, respectively.

#### 5.2.2. Impulse Response Analysis

Similar with monetary policy, we also document four 2-dimensional VAR models, and they are Agri-LoanG to GDP, Agri-Loan to Oil Price, Enter-Loan to GDP and Enter-Loan to Oil Price. The optimal lag order of four VAR models is determined by Akaike information criterion and the values are described in Table 4.


Figure 6 gives the results of the impulse response of four VAR models. The four graphs of Figure 6 all show that economic fundamental has a short-term negative effect on financial inclusion factors and they all gradually fade away after a period of fluctuation. Specifically, the shapes of Figure 6a,c are similar, which shows the Agri-Loan is not sensitive to the shock of GDP, but the Enter-Loan displays sensitivity faced to the shock of GDP. Compared with Figure 6b,d, the responses of financial inclusion factors are similar faced to shock of the oil price, and it shows the oil prices have an impact on financial inclusion factor. This unexpected negative shock indicates that the financial and economic development of the sample area is not coordinated, and there is no mutual promotion between the two. The empirical results can be analyzed with the present economic and financial situation in China. With the slowdown of China's GDP growth, industrial upgrading and structural adjustment have become a new direction of economic development. Inclusive finance needs to keep up with the pace of the economy by contributing to the industry development and improving the intrinsic capability of the underdeveloped regions, so as to achieve coordinated development with the economy.

The negative effects shown in the empirical results can be divided into two cases. On one hand, since the financial inclusion cannot catch up with the pace of GDP and even suppress its growth in the long term. The government must take forceful measures to allocate the financial resources rationally and promote more fund to the financial inclusion sector, especially in rural and county areas where the financial inclusion should focus on. At the same time, financial inclusion as a specific form of financial liberalization can have a positive cumulative effect on the economy (Peng et al., 2014) [43]. On the other hand, high development of financial inclusion without a good economy is not sustainable. It must make full use of the role of finance in economic development and commit themselves to developing the economy, so that financial resources can be effectively used. In summary, the economy can have a good interaction with the financial inclusion by way of economy and finance coordinating.

**Figure 6.** Impulse response graph.
