By establishing difference in differences (DID) and propensity score matchingdouble difference method (PSMDID) models, the paper investigates the effect of the two pilot policies which promotes the combination of technology and finance based on the enterprise value with the available data from Shanghai and Shenzhen Ashare listed companies in China from 2008 to 2019. Empirical results show that the first and second pilots of technological finance cause a significant increase and decrease in the enterprise value, respectively. These conclusions validate the effectiveness of China's technological finance policy from the enterprise value and provide empirical data support for optimising technological finance policies of the government and highquality development of enterprises in the new era.
Keywords
 differenceindifferences model
 PSMDID model
 micro effect
 technological finance cooperation pilot
The practical experience of the sustainable development of technological finance in the United States, Britain, Germany, Japan and other countries shows that government policy guidance and financial support are important factors for technological finance to boost economic growth [1, 2]. China launched the first pilot of the practice of technological finance in October 2011 and identified 16 regions including Zhongguancun National Independent Innovation Demonstration Zone, Tianjin, and Shanghai as the first batch of pilot areas to promote the integration of technology and finance (hereinafter referred to as the first pilot of technological finance).
On October 20, 2011, China launched the first pilot of the practice of technological finance, and identified 16 regions including Zhongguancun National Independent Innovation Demonstration Zone, Tianjin, and Shanghai, Jiangsu Province, Hangwenhuyong Zone of Zhejiang Province, Hewubang Innovation Demonstration Zone of Anhui Province, Wuhan, Guangfowan Zone of Guangdong Province, Chongqing, Jinyang, GuanzhongTianshui Economic Zone of Shanxi Province, Dalian, Qingdao and Shenzhen as the first batch of pilot areas to promote the integration of technology and finance.
In June 2016, 9 cities including Zhengzhou, Xiamen and Ningbo were identified as the second batch of pilot areas to promote the integration of technology and finance (hereinafter referred to as the second pilot of technology and finance).On June 14, 2016, 9 cities including Zhengzhou, Xiamen, Ningbo, Jinan, Nanchang, Guiyang, Yinchuan, Baotou and Shenyang were identified as the second batch of pilot areas to promote the integration of technology and finance.
The existing literature has made a more indepth quantitative analysis on the innovation effect of China's technological finance pilot based on the macro and micro data from 2003 to 2016. However, the second pilot of technological finance was implemented in 9 cities in 2016, which represented the difference in differences (DID) experimental group, but the control group had flaws. In addition, only the first pilot of technological finance was used to evaluate the overall effect of China's technological finance pilot policy, and the reliability of the conclusions is still questionable. The aim of the technological finance pilot is to provide enterprises with a highquality system, environment, capital and other resources, ease financing constraints, and then provide impetus for corporate innovation. This motivates us to think about whether the value of enterprises in the pilot area is affected when evaluating the effects of technological finance pilot policies. Are the corporate value effects of the two pilots consistent? In view of this, this paper analysis the impact of technological finance pilot on corporate value with DID and propensity score matchingdouble difference method (PSMDID) models, based on the financial data from Shanghai and Shenzhen Ashare listed companies in China from 2008 to 2019.
This paper takes 2011 and 2016 technological finance pilots in China as exogenous events and selects listed companies in the two pilot regions/cities as the experimental group. The listed companies in the nonpilot area are the control group. The changes in the corporate value of these two groups of companies before and after the pilot are analysed. The periods from 2008 to 2016 and from 2011 to 2019 are used as the sample periods for two technological finance pilots to ensure the reliability of the research conclusions. This paper excludes ST and PT companies. Continuous variables are processed with 1% upper and lower tailings, and finally, a total of 30,083 company annual observation values are obtained in two batches of technological finance pilots. The data in this paper come from CSMAR, WIND and RESSET databases.
The DID method is a commonly used method to evaluate the effect of policy implementation. The technological finance pilot can be regarded as a quasinatural experiment. The value of the enterprises in the pilot and nonpilot areas may change before and after the pilot. By analysing the changes before and after the pilot, the impact of the technological finance pilot on corporate value can be derived. Considering that technological finance pilots may be nonrandom and there is a selfselection problem, this paper draws on the existing research methods of technological finance pilot effects, uses PSMDID [5,6,7], and further conducts empirical tests to eliminate sample selection deviations and reverse causality to make sure the robustness of the results. The DID method was used to construct a model (1) to test the impact of technological finance pilots on corporate value.
Among them, the subscripts
Definition and measurement of variables
Value  Corporate value  Value1_{ijt} = market value/assets 
DID  The implementation of technological finance pilot policies  DID_{it} = Treat_{it} × Imp_{it} 
RI  Corporate risktaking 

Size  Enterprise size  Natural logarithm of total assets 
Roe  Return on net assets  Net profit/net assets 
Nprs  Net sales interest rate  Net profit/sales income 
Tat  Turnover of total assets  Operating income/total assets 
Beta  Market Beta  Risk factor weighted by market capitalisation 
Eps  Earnings per share  Net profit attributable to common shareholders/weighted average of common shares issued in the current period 
DID, difference in difference.
Table 2 reports the single factor test results of the main variables of two technological finance pilots. Panel A of Table 2 shows that after the first technological finance pilot, the value (mean) of the enterprises in the pilot areas has increased significantly, while the value (mean) of the enterprises in the nonpilot areas has not increased significantly. Panel B of Table 2 shows that after the second technological finance pilot, the value (mean, median) of the enterprises in both the nonpilot and pilot areas of technological finance has shown a significant downward trend. Obviously, the first (second) technological finance pilot had a significant positive (negative) impact on corporate value.
Single factor test of the main variables of two technological finance pilots
Enterprises in the nonpilot areas  Value 1  2.726  2.172  2.696  2.017  −0.030***  −0.155*** 
Value 2  2.916  2.318  2.982  2.184  −0.066***  −0.134***  
Enterprises in the pilot areas  Value 1  2.689  2.195  2.890  2.190  0.201***  −0.005 
Value 2  2.860  2.330  3.157  2.340  0.297***  0.010 **  
Enterprises in the nonpilot areas  Value 1  2.493  1.912  2.213  1.740  −0.280***  −0.172*** 
Value 2  2.729  2.048  2.517  1.959  −0.212***  −0.089***  
Enterprises in the pilot areas  Value 1  2.303  1.762  2.068  1.601  −0.235***  −0.161*** 
Value 2  2.554  1.915  2.334  1.807  −0.220***  −0.108** 
Table 3 reports the regression results of the model (1). The regression results in Panel A of Table 3 show the impact of the first technological finance pilot on corporate value. The year and corporate fixed effects are controlled in the regression results. It can be seen from Panel A of Table 3 that the models that use Value 1 and Value 2 to measure corporate value in columns (1) and (2), the DID regression coefficients are 0.129 and 0.140 at the 1% level, respectively, indicating that the first technological finance pilot has improved the value of the enterprises in the pilot areas. Panel B of Table 3 shows the impact of the second technological finance pilot on corporate value. The DID regression coefficients in columns (3) and (4) of Panel B in Table 3 are −0.101 and −0.130 at 10% level, respectively, which indicate that the second technological finance pilot has reduced the value of enterprises in the pilot area. From the perspective of control variables, the correlation between corporate value, risktaking and Size, Roe, Tat, Beta, and Eps is basically consistent with the existing research. It can be seen that after controlling other factors, the first (second) technological finance pilot significantly increased (reduced) corporate value.
Regression results of the relationship between technological finance pilots, risktaking and corporate value
DID  0.129*** (3.374)  0.140*** (3.062)  −0.101* (−1.829)  −0.130** (−2.021) 
RT1  0.080*** (3.989)  3.221*** (13.970)  
RT2  0.059*** (4.357)  1.949*** (13.471)  
Size  −1.063*** (−47.203)  −0.970*** (−36.124)  −0.651*** (−65.413)  −0.713*** (−61.238) 
Roe  2.022*** (13.588)  2.427*** (13.680)  1.482*** (9.638)  1.665*** (9.256) 
Nprs  −0.026 (−0.217)  0.086 (0.617)  0.084 (0.839)  0.151 (1.300) 
Tat  0.383*** (8.040)  0.423*** (7.449)  −0.080** (−2.567)  −0.160*** (−4.405) 
Beta  −0.164*** (−3.822)  −0.177*** (−3.472)  −0.588*** (−12.931)  −0.697*** (−13.097) 
Eps  0.067* (1.701)  −0.056 (−1.184)  0.395*** (11.040)  0.407*** (9.741) 
Constant  25.749*** (51.739)  23.941*** (40.344)  17.178*** (74.850)  18.959*** (70.580) 
Year/firm Fe  Control  Control  Control  Control 
Observations  17,435  17,435  12,648  12,648 
0.755  0.736  0.473  0.466  
0.715  0.691  0.469  0.462 
The premise of DID is that the common trend hypothesis is established, that is, the experimental group and the control group have the same changing trend before the policy is implemented. To test this hypothesis, this paper uses regression models and common trend graphs to verify. To reflect the dynamic effects of technological finance pilots on corporate value, the crossproduct term (Treat × Year) of corporate dummy variables and time dummy variables in the technological finance pilot areas are used as explanatory variables, and control variables are added to construct a regression model. The regression results are shown in Table 4.
Test results of the common trend of corporate value in two technological finance pilot areas
pre_2  0.031 (0.319)  0.031 (0.321)  pre_4  −0.108 (−1.095)  −0.048 (−0.450) 
pre_1  −0.031 (−0.335)  −0.037 (−0.406)  pre_3  −0.063 (−0.564)  −0.046 (−0.399) 
current  0.021 (0.232)  0.021 (0.236)  pre_2  −0.030 (−0.205)  −0.058 (−0.353) 
post_1  0.044 (0.504)  0.044 (0.508)  pre_1  −0.189 (−1.191)  −0.236 (−1.045) 
post_2  0.185** (2.143)  0.186** (2.149)  Current  −0.195*** (−2.774)  −0.207** (−2.133) 
post_3  0.223*** (2.590)  0.223*** (2.596)  post_1  −0.201*** (−2.614)  −0.244*** (−2.869) 
post_4  0.365*** (4.324)  0.365*** (4.326)  post_2  −0.131** (−2.531)  −0.172*** (−3.224) 
post_5  0.297*** (3.612)  0.297*** (3.620)  post_3  0.017 (0.132)  −0.013 (−0.101) 
Control  Yes  Yes  Control  Yes  Yes 
Constant  22.054*** (103.494)  22.046*** (103.484)  Constant  17.795*** (24.906)  19.661*** (23.231) 
Observations  17,659  17,657  Observations  12,648  12,648 
0.505  0.505  0.465  0.459  
0.496  0.495  0.461  0.454 
Panel A of Table 4 shows that the regression coefficients of the years before (pre_2, pre_1), current year (current) and 1 year after (post_1) the first technological finance pilot are not significant at 10% level, that is, before the implementation of the first pilot policy, there is no significant difference between the experimental group and the control group. The regression coefficients of every year after the first technological finance pilot (post_2, post_3, post_4, post_5) are significant, indicating that the impact of the first technological finance pilot on the value of the enterprise is lagging, and the lag period is 1 year. Panel B of Table 4 shows that the regression coefficients of the years (pre_4, pre_3, pre_2, pre_1) before the second technological finance pilot are not significant at 10% level, that is, before the second pilot, there is no significant difference between the experimental group and the control group. The regression coefficients of the current year (current) and the following 2 years (post_1, post_2) of the second technological finance pilot are significant at 5% level, indicating that the second technological finance pilot has a direct impact on the value of the enterprise, and there is no lag period, and the impact duration lasts for 2 years.
Figure 1 shows the common trend test chart of the enterprise value in the areas of two technological finance pilots. Panel A of Figure 1 shows the changing trend of the corporate value of the first pilot. Before the first pilot in 2011, the change curve of the corporate value of the control group and the experimental group was close to overlapping, the trend was roughly the same, and the difference was not obvious. After the first pilot, the two groups still had the same trend, but the growth rate of the corporate value of the experimental group was significantly greater than that of the control group with an obvious difference, and the parallel trend assumption was satisfied. Panel B of Figure 1 shows the changing trend of corporate value in the second technological finance pilot. Before the second pilot in 2016, the change curve of the corporate value of the control group and the experimental group was close to overlapping (without considering the occurrence of the stock market crash in 2015), and the changing trend was basically the same without an obvious difference. But after the implementation of the second pilot policy, the trends of the two groups were still the same, but the decline in corporate value of the experimental group was significantly greater than that of the control group, the difference was obvious, and the parallel trend hypothesis was satisfied. Above all, the common trend test of the experimental group and the control group of two technological finance pilots is established. The DID can be used to estimate the implementation effect of two technological finance pilots, which shows that the conclusions of this paper are robust.
The DID model is mostly used to quantitatively evaluate the implementation effect of public policies or projects in econometrics. Based on the data obtained from natural experiments, DID model can effectively control the exante differences between research objects and effectively separate the real results of policy impact. Due to the largescale public policy, it is difficult to ensure the complete randomness of the sample allocation between the policy implementation group and the control group. This leads to the existence of selection bias and a counterfactual framework, which may lead to some errors in our direct evaluation of policy effects. Propensity score matching (PSM) is a useful approach when only observed characteristics are believed to affect programme participation. PSM constructs a statistical comparison group that is based on a model of the probability of participating in the treatment, using observed characteristics. The advantages of PSMDID method are as follows: (1) It can control the group differences of unobservable; (2) It is most robust and efficient in removing the biases due to covariates and in estimating the treatment effect on the treated; (3) Matched sampling relaxes the DID identification restrictions, making modelbased adjustments less sensitive to the model specification, and this reduced sensitivity again facilitates the estimation of parsimonious parametric approximations of the average treatment effect on the treated.
This paper studies the effects of the two technological finance pilots on enterprises’ value indepth using the research method PSMDID, conducting empirical tests to eliminate sample selection bias and reverse causality. Referring to the existing literature, this paper selects the propensity score core matching method to determine the sample enterprises in the control group, estimates the propensity scores of the enterprises in the experimental group (pilot area) under the established corporate characteristics and matches the enterprises in nonpilot areas with closer propensity scores with those in pilot areas as the control group. To ensure the highquality application of the PSM method, we carried out a balance test and a joint support test on the sample of the enterprises before and after the matching. The results are shown in Table 5 of the balance test results of the two pilot PSMs, and Figure 2 of the common value range of the propensity score of the experimental group and the control group in two pilots.
Panels A and Panel B of Table 5 show that in the technological finance pilot events, the standardisation differences after the explanatory variables are matched have dropped significantly, and the logistic regression estimated
The balance test results of the PSM of two technological finance pilots
Size  Before matching  21.885  22.021  7.080 (0.000)  −10.700  95.000 
After matching  21.884  21.891  −0.360 (0.719)  −0.500  
Beta  Before matching  1.089  1.076  3.470 (0.001)  5.200  79.700 
After matching  1.089  1.087  0.680 (0.495)  1.100  
Roe  Before matching  0.089  0.080  5.250 (0.000)  8.000  92.500 
After matching  0.089  0.088  0.410 (0.683)  0.600  
Nprs  Before matching  0.092  0.086  5.420 (0.000)  5.100  60.900 
After matching  0.092  0.095  3.950 (0000)  −2.000  
Tat  Before matching  0.071  0.668  5.420 (0.000)  8.200  25.000 
After matching  0.071  0.678  3.950 (0.000)  6.100  
Eps  Before matching  0.377  0.363  1.99 (0.047)  3.000  56.800 
After matching  0.377  0.382  −0.850 ((0.394)  −1.300  
Treated  Controls  Difference  S.E.  
ATT  2.841  2.777  0.065  0.030  2.140  
LR 
130.510  
LR 
20.300  
Size  Before matching  21.278  22.264  0.360 (0.722)  1.100  −29.900 
After matching  22.278  22.260  0.360 (0.718)  1.400  
Beta  Before matching  1.078  1.076  0.230 (0.822)  0.700  97.400 
After matching  1.078  1.078  0.000 (0.996)  −0.000  
Roe  Before matching  0.080  0.069  3.130 (0.002)  9.400  43.500 
After matching  0.080  0.074  1.360 (0.174)  5.300  
Nprs  Before matching  0.072  0.072  −0.002 (0.982)  −0.100  60.900 
After matching  0.072  0.072  −0.010 (0.993)  0.000  
Tat  Before matching  0.747  0.636  8.520 (0.000)  22.500  43.500 
After matching  0.747  0.684  3.070 (0.002)  12.700  
Eps  Before matching  0.379  0.324  3.74 (0.000)  11.100  39.500 
After matching  0.379  0.346  1.69 (0.092)  6.700  
Treated  Controls  Difference  S.E.  
ATT  2.204  2.376  −0.172  0.047  −3.700  
LR 
70.420  
LR 
10.860 
Figure 2 illustrates the results of the joint support hypothesis test of two technological finance pilots. Panel A and Panel B of Figure 2 show that most of the enterprises in the experimental group and control group fall within the common support area, and only a few samples in the two groups are not in the common support area, indicating that the sample data formed by PSM matching in this paper meets the common support hypothesis. To ensure the accuracy of the estimation, the followup PSMDID regression samples exclude the enterprises that are not in the common support area.
Table 6 reports the robustness test results of the impact on the corporate value of two technological finance pilots. The DID regression coefficients
The robustness test results
DID  0.132*** (3.436)  0.143*** (3.136)  −0.099* (−1.801)  −0.129** (−1.997) 
RT1  0.079*** (3.918)  3.226*** (13.990)  
RT2  0.057*** (4.262)  1.948*** (13.459)  
Size  −1.062*** (−47.180)  −0.969*** (−36.098)  −0.653*** (−65.485)  −0.715*** (−61.271) 
Roe  2.032*** (13.648)  2.441*** (13.751)  1.513*** (9.795)  1.707*** (9.442) 
Nprs  −0.039 (−0.332)  0.068 (0.483)  0.103 (1.025)  0.158 (1.345) 
Tat  0.381*** (7.987)  0.420*** (7.388)  −0.079** (−2.536)  −0.159*** (−4.381) 
Beta  −0.163*** (−3.813)  −0.176*** (−3.461)  −0.591*** (−12.972)  −0.699*** (−13.121) 
Eps  0.066* (1.664)  −0.058 (−1.227)  0.391*** (10.933)  0.405*** (9.660) 
Constant  25.734*** (51.719)  23.921*** (40.322)  17.216*** (74.925)  18.996*** (70.612) 
Firm/year Fe  Control  Control  Control  Control 
Observations  17,433  17,433  12,628  12,628 
0.755  0.735  0.474  0.466 
Based on the quasinatural experiments of two technological finance pilots in China, this paper constructs DID and PSMDID models based on the data of Shanghai and Shenzhen Ashare listed companies from 2008 to 2019, to study the effect of the technological finance pilots on corporate value. This study found that the first technological finance pilot improved corporate value in the pilot area, while the second technological finance pilot led to a decline in corporate value in the pilot area. Following are the reasons for the different effects of two technological finance pilots.
Technological finance pilots have an impact on corporate value by alleviating financing constraints, expanding financing space, stimulating technological innovation, promoting investment, and so on. Since 2000, Chinese enterprises have faced strong financing constraints and have a weak sense of risktaking [15]. In 2011, the first technological finance pilot was conducted. Government departments, financial institutions and social capital in the pilot areas actively responded. The emergence of diversified technological financial products, funds and subsidies, as well as the promotion of effective integration of enterprises and multilevel capital markets, alleviated the strong financing constraints faced by the enterprises and broadened the financing channels of the enterprises. The emergence of the multiparty sharing mechanism of the enterprise's innovative investment risks has promoted the enthusiasm of corporate financing and expanded R&D investment [4]. Information platforms have reduced information asymmetry, financial resource mismatch rates have decreased, and innovation performance has improved [3]. Technological finance promotes the technological innovation of the enterprises [1, 2], enhances the competitiveness of the enterprises, and then has a positive impact on the value of the enterprises [10]. So the first technological finance pilot improved the value of the enterprises in the pilot area of China.
After the second technological finance pilot in 2016, the size of stock market funds has shrunk severely due to the stock market crash, the investors’ uncertain expectations about the future returns of equity investments have increased, and financial institutions have tightened credit policies due to capital constraints. The cost and difficulty of external refinancing of the enterprises have increased sharply, and the financial constraints have increased, producing a negative impact on the investment and financing behaviour of the enterprises. The value of the enterprises has dropped significantly. Compared with the first technological finance pilot, the number of cities in the second pilot was reduced, and the signal effect of the policy implementation on the capital market was weakened. In the context of the sluggish capital market, different regions have actively carried out the strategy of ‘deleveraging and cost reduction’ to promote the enterprises that are not in pilot areas with technological finance policies and funds, etc. for corporate innovation to increase the highrisk technological innovative investment. As when the corporate management faces market crises, their management style tends to be more conservative, which affects current and future risk decisions [
Considering the corporate value effect of the two technological finance pilots, this paper puts forward the following suggestions.
Regression results of the relationship between technological finance pilots, risktaking and corporate value
DID  0.129 
0.140 
−0.101 
−0.130 
RT1  0.080 
3.221 

RT2  0.059 
1.949 

Size  −1.063 
−0.970 
−0.651 
−0.713 
Roe  2.022 
2.427 
1.482 
1.665 
Nprs  −0.026 (−0.217)  0.086 (0.617)  0.084 (0.839)  0.151 (1.300) 
Tat  0.383 
0.423 
−0.080 
−0.160 
Beta  −0.164 
−0.177 
−0.588 
−0.697 
Eps  0.067 
−0.056 (−1.184)  0.395 
0.407 
Constant  25.749 
23.941 
17.178 
18.959 
Year/firm Fe  Control  Control  Control  Control 
Observations  17,435  17,435  12,648  12,648 
0.755  0.736  0.473  0.466  
0.715  0.691  0.469  0.462 
Single factor test of the main variables of two technological finance pilots
Enterprises in the nonpilot areas  Value 1  2.726  2.172  2.696  2.017  −0.030 
−0.155 
Value 2  2.916  2.318  2.982  2.184  −0.066 
−0.134 

Enterprises in the pilot areas  Value 1  2.689  2.195  2.890  2.190  0.201 
−0.005 
Value 2  2.860  2.330  3.157  2.340  0.297 
0.010 

Enterprises in the nonpilot areas  Value 1  2.493  1.912  2.213  1.740  −0.280 
−0.172 
Value 2  2.729  2.048  2.517  1.959  −0.212 
−0.089 

Enterprises in the pilot areas  Value 1  2.303  1.762  2.068  1.601  −0.235 
−0.161 
Value 2  2.554  1.915  2.334  1.807  −0.220 
−0.108 
The robustness test results
DID  0.132*** (3.436)  0.143*** (3.136)  −0.099* (−1.801)  −0.129** (−1.997) 
RT1  0.079*** (3.918)  3.226*** (13.990)  
RT2  0.057*** (4.262)  1.948*** (13.459)  
Size  −1.062*** (−47.180)  −0.969*** (−36.098)  −0.653*** (−65.485)  −0.715*** (−61.271) 
Roe  2.032*** (13.648)  2.441*** (13.751)  1.513*** (9.795)  1.707*** (9.442) 
Nprs  −0.039 (−0.332)  0.068 (0.483)  0.103 (1.025)  0.158 (1.345) 
Tat  0.381*** (7.987)  0.420*** (7.388)  −0.079** (−2.536)  −0.159*** (−4.381) 
Beta  −0.163*** (−3.813)  −0.176*** (−3.461)  −0.591*** (−12.972)  −0.699*** (−13.121) 
Eps  0.066* (1.664)  −0.058 (−1.227)  0.391*** (10.933)  0.405*** (9.660) 
Constant  25.734*** (51.719)  23.921*** (40.322)  17.216*** (74.925)  18.996*** (70.612) 
Firm/year Fe  Control  Control  Control  Control 
Observations  17,433  17,433  12,628  12,628 
0.755  0.735  0.474  0.466 
The balance test results of the PSM of two technological finance pilots
Size  Before matching  21.885  22.021  7.080 (0.000)  −10.700  95.000 
After matching  21.884  21.891  −0.360 (0.719)  −0.500  
Beta  Before matching  1.089  1.076  3.470 (0.001)  5.200  79.700 
After matching  1.089  1.087  0.680 (0.495)  1.100  
Roe  Before matching  0.089  0.080  5.250 (0.000)  8.000  92.500 
After matching  0.089  0.088  0.410 (0.683)  0.600  
Nprs  Before matching  0.092  0.086  5.420 (0.000)  5.100  60.900 
After matching  0.092  0.095  3.950 (0000)  −2.000  
Tat  Before matching  0.071  0.668  5.420 (0.000)  8.200  25.000 
After matching  0.071  0.678  3.950 (0.000)  6.100  
Eps  Before matching  0.377  0.363  1.99 (0.047)  3.000  56.800 
After matching  0.377  0.382  −0.850 ((0.394)  −1.300  
Treated  Controls  Difference  S.E.  
ATT  2.841  2.777  0.065  0.030  2.140  
LR 
130.510  
LR 
20.300  
Size  Before matching  21.278  22.264  0.360 (0.722)  1.100  −29.900 
After matching  22.278  22.260  0.360 (0.718)  1.400  
Beta  Before matching  1.078  1.076  0.230 (0.822)  0.700  97.400 
After matching  1.078  1.078  0.000 (0.996)  −0.000  
Roe  Before matching  0.080  0.069  3.130 (0.002)  9.400  43.500 
After matching  0.080  0.074  1.360 (0.174)  5.300  
Nprs  Before matching  0.072  0.072  −0.002 (0.982)  −0.100  60.900 
After matching  0.072  0.072  −0.010 (0.993)  0.000  
Tat  Before matching  0.747  0.636  8.520 (0.000)  22.500  43.500 
After matching  0.747  0.684  3.070 (0.002)  12.700  
Eps  Before matching  0.379  0.324  3.74 (0.000)  11.100  39.500 
After matching  0.379  0.346  1.69 (0.092)  6.700  
Treated  Controls  Difference  S.E.  
ATT  2.204  2.376  −0.172  0.047  −3.700  
LR 
70.420  
LR 
10.860 
Definition and measurement of variables
Value  Corporate value  Value1_{ijt} = market value/assets 
DID  The implementation of technological finance pilot policies  DID_{it} = Treat_{it} × Imp_{it} 
RI  Corporate risktaking 

Size  Enterprise size  Natural logarithm of total assets 
Roe  Return on net assets  Net profit/net assets 
Nprs  Net sales interest rate  Net profit/sales income 
Tat  Turnover of total assets  Operating income/total assets 
Beta  Market Beta  Risk factor weighted by market capitalisation 
Eps  Earnings per share  Net profit attributable to common shareholders/weighted average of common shares issued in the current period 
Test results of the common trend of corporate value in two technological finance pilot areas
pre_2  0.031 (0.319)  0.031 (0.321)  pre_4  −0.108 (−1.095)  −0.048 (−0.450) 
pre_1  −0.031 (−0.335)  −0.037 (−0.406)  pre_3  −0.063 (−0.564)  −0.046 (−0.399) 
current  0.021 (0.232)  0.021 (0.236)  pre_2  −0.030 (−0.205)  −0.058 (−0.353) 
post_1  0.044 (0.504)  0.044 (0.508)  pre_1  −0.189 (−1.191)  −0.236 (−1.045) 
post_2  0.185** (2.143)  0.186** (2.149)  Current  −0.195*** (−2.774)  −0.207** (−2.133) 
post_3  0.223*** (2.590)  0.223*** (2.596)  post_1  −0.201*** (−2.614)  −0.244*** (−2.869) 
post_4  0.365*** (4.324)  0.365*** (4.326)  post_2  −0.131** (−2.531)  −0.172*** (−3.224) 
post_5  0.297*** (3.612)  0.297*** (3.620)  post_3  0.017 (0.132)  −0.013 (−0.101) 
Control  Yes  Yes  Control  Yes  Yes 
Constant  22.054*** (103.494)  22.046*** (103.484)  Constant  17.795*** (24.906)  19.661*** (23.231) 
Observations  17,659  17,657  Observations  12,648  12,648 
0.505  0.505  0.465  0.459  
0.496  0.495  0.461  0.454 
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