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2444-8656
First Published
01 Jan 2016
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access type Open Access

Difference-in-differences test for micro effect of technological finance cooperation pilot in China

Published Online: 20 May 2022
Volume & Issue: AHEAD OF PRINT
Page range: -
Received: 26 Jul 2021
Accepted: 06 Dec 2021
Journal Details
License
Format
Journal
eISSN
2444-8656
First Published
01 Jan 2016
Publication timeframe
2 times per year
Languages
English
Abstract

By establishing difference in differences (DID) and propensity score matching-double difference method (PSM-DID) 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 A-share 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 high-quality development of enterprises in the new era.

Keywords

Introduction

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, Guanzhong-Tianshui 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.

From the macro perspective, the first technological finance pilot improved the efficiency of local financial development, increased the proportion of government technology expenditures [3] and guided the fiscal input in technology [4], which improved the level of technological innovation in pilot areas [5], especially in the eastern and central regions, and large-scale cities [4]. The effect on the promotion of technological innovation is significant in the regions with high levels of local government efficiency and initial innovation, and low-level cities [5]. The technological finance pilot has promoted the economic development of the pilot area through technological innovation and industrial structure optimisation [6, 7]. From the micro perspective, the technological finance pilot in 2011 improved the innovation level of private enterprises [8], improved the production efficiency of small and medium high-tech enterprises [9] and competitiveness of enterprises in the pilot area [10].

The existing literature has made a more in-depth 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 high-quality 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 matching-double difference method (PSM-DID) models, based on the financial data from Shanghai and Shenzhen A-share listed companies in China from 2008 to 2019.

Research design
Sample and data

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 non-pilot 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.

Model construction and variable measurement

The DID method is a commonly used method to evaluate the effect of policy implementation. The technological finance pilot can be regarded as a quasi-natural experiment. The value of the enterprises in the pilot and non-pilot 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 non-random and there is a self-selection problem, this paper draws on the existing research methods of technological finance pilot effects, uses PSM-DID [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. Valueijt=βi0+βi1DIDit+βControlijt+λt+μj+εit {\rm{Valu}}{{\rm{e}}_{ijt}} = {\beta _{i0}} + {\beta _{i1}}{\rm{DI}}{{\rm{D}}_{it}} + \beta {\rm{Contro}}{{\rm{l}}_{ijt}} + {\lambda _t} + {\mu _j} + {\varepsilon _{it}}

Among them, the subscripts i, j and t represent the pilot batch, enterprise and time of technological finance, respectively. Valueijt, the explained variable – corporate value, is expressed with TobinQ value, drawing on He Ying et al. [11]. DIDit is used to explain the implementation of technological finance pilot policies, which is the crossover term (DIDit = Treatit × Impit) of the dummy variable (Treatit) of the enterprise experimental group or control group and the policy implementation dummy variable (Impit). If the city where the sample company is located is the first (the second) batch of pilot cities of technological finance, the value of Treatit is 1, otherwise it is 0. The value of Impit is 1, otherwise it is 0 after the first (second) pilot policy is implemented. Controlit is a set of control variables that affect the corporate value. Based on existing literature [12,13,14], this paper selects the following variables: RI (corporate risk-taking), Size (corporate Size), Roe (Return on Net Assets), Nprs (Net Sales Interest Rate), Tat (Turnover of total assets), Beta (market Beta) and Eps (Earnings per share); and Table 1 shows definition and measurement of these variables. λt is the time fixed effect, μj is the firm fixed effect, and ɛit is the random disturbance item. This paper focuses on the value of the coefficients βi1 and their significance.

Definition and measurement of variables

Variables Definition Measurement

Value Corporate value Value1ijt = market value/assetsValue2ijt = market value/(total assets-net intangible assets-net good will)
DID The implementation of technological finance pilot policies DIDit = Treatit × ImpitTreat: enterprise is (not) in experimental (control) groupImpit: the policy implementation dummy variable
RI Corporate risk-taking RT1ijt=Σt=1T(Adj_Roaijt1TΣt=1TAdj_Roaijt)2T1 RT{1_{ijt}} = \sqrt {{{\Sigma _{t = 1}^T{{\left( {Adj\_Ro{a_{ijt}} - {1 \over T}\Sigma _{t = 1}^TAdj\_Ro{a_{ijt}}} \right)}^2}} \over {T - 1}}} RT 2ijt = Max (Ad j_Roaijt) − Min(Ad j_Roaijt)Ad j_Roaijt is adjusted return on total assets by industry; T is the number of periods
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.

Empirical analysis
Descriptive statistics

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 non-pilot 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 non-pilot 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

Before pilot After pilot After – before
Mean Median Mean Median Mean difference Median difference

Panel A The first technological finance pilot

Enterprises in the non-pilot 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 **

Panel B the second technological finance pilot

Enterprises in the non-pilot 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**

Note: *, ** and *** indicates that the difference between the means of T-test and between the medians of Kruskal–Wallis test is significant at the level of 1%, 5% and 10%, respectively.

Benchmark regression

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, risk-taking 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, risk-taking and corporate value

Variables Panel A: First technological finance pilot Panel B: Second technological finance pilot
(1) (2) (3) (4)
Value 1 Value 2 Value 1 Value 2

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
R2 0.755 0.736 0.473 0.466
r2_a 0.715 0.691 0.469 0.462

Note: *, ** and **indicate that the regression coefficient is significant at the level of 1%, 5%, and 10%, respectively, similarly hereinafter.

Robustness test
Double difference common trend test

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 cross-product 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

Panel A: The first technological finance pilot Panel B: The second technological finance pilot
Variables (1)Value 1 (2)Value 2 Variables (1)Value 1 (2)Value 2

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
R2 0.505 0.505 R2 0.465 0.459
r2_a 0.496 0.495 r2_a 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.

Fig. 1

The test chart of the common trend of two technological finance pilots

PSM-DID method test
PSM core matching

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 ex-ante differences between research objects and effectively separate the real results of policy impact. Due to the large-scale 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 PSM-DID 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 model-based 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 in-depth using the research method PSM-DID, 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 non-pilot areas with closer propensity scores with those in pilot areas as the control group. To ensure the high-quality 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.

Fig. 2

The common value range of the propensity scores of the enterprises in the experimental group and control group of two technological finance 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 χ2 value after the matching has been significantly reduced. The average treatment effect ATT estimated values of two technological finance pilots are 0.065 and −0.172 respectively, and the corresponding T values are 2.14 and −3.70, exceeding the critical value of 1.96 (−1.96), so the matching effect is significant, which means that the matching variable is weak in explaining whether an enterprise participates in the technological finance pilot. It can be considered that the enterprises in the pilot cities are independent and random relative to the matched samples.

The balance test results of the PSM of two technological finance pilots

Variable Sample Mean difference test Standardised difference test

Mean of experiment group Mean of control group T test (P-value) Standardised difference Decline (%)

Panel A: The first technological finance pilot

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. T-stat
ATT 2.841 2.777 0.065 0.030 2.140
LR χ2 before matching 130.510
LR χ2 after matching 20.300

Panel B: The second technological finance pilot

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. T-stat
ATT 2.204 2.376 −0.172 0.047 −3.700
LR χ2 before matching 70.420
LR χ2 after matching 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 follow-up PSM-DID regression samples exclude the enterprises that are not in the common support area.

Analysis of PSM-DID regression results

Table 6 reports the robustness test results of the impact on the corporate value of two technological finance pilots. The DID regression coefficients βi1 in Panel A and Panel B of Table 6 have not changed substantially. The previous empirical research conclusions are relatively robust.

The robustness test results

Variable Panel A: The first technological finance pilot Panel B: The second technological finance pilots
Value 1 Value 2 Value 1 Value 2
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
R2 0.755 0.735 0.474 0.466
Conclusions and reasons
Conclusions

Based on the quasi-natural experiments of two technological finance pilots in China, this paper constructs DID and PSM-DID models based on the data of Shanghai and Shenzhen A-share 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.

Reasons
Reason for the positive effect of the first technological finance pilot in China

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 risk-taking [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 multi-level capital markets, alleviated the strong financing constraints faced by the enterprises and broadened the financing channels of the enterprises. The emergence of the multi-party 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.

Reason for the negative effect of the second technological finance pilot in 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 high-risk 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 [?]. The investment and financing of the enterprises in the pilot areas have become more conservative, innovation performance and productivity are reduced, the sustainable development capabilities are poor, the awareness of actively taking risks has weakened, and the value of the enterprises has decreased significantly [?]. The crowding-out effect of the second technological finance pilot is present. To sum up, the second technological finance pilot reduced the value of the enterprises in the pilot area of China.

Recommendations

Considering the corporate value effect of the two technological finance pilots, this paper puts forward the following suggestions.

(1) Improve the technological finance strategy, build a high-quality developing technological finance demonstration city layout and support system, and accelerate the regional balanced development of technological finance. Considering the heterogeneity of the corporate value effects of the two pilots, the government should improve the strategic position of technological finance strategy and implement a discretionary technological finance strategy. Based on the different characteristics of the economic development, industrial chain and technological financial service system in the eastern, central and western regions, guide each region to strengthen the creation of characteristic technological finance demonstration cities and accelerate the exchanges between different regional technological finance pilot cities, especially the eastern and central regions, which can promote the rapid development and upgrading of the technology and finance in the western region. Promote the optimisation of the financial environment of various cities, promote the effective allocation of the systems, resources, technology, etc., and promote the regional coordinated development of the technology and finance in our country to lay a solid foundation for the enhancement and high-quality development of corporate value.

(2) Maintain the stable and healthy development of the multi-level capital market, promote the innovation of systems and mechanisms, and create a scientific and technological financial system in which the market and the government effectively cooperate. In the capital markets of different states, the two technological finance pilots have a heterogeneous impact on the value of state-owned enterprises and non-state-owned enterprises. The corporate value of the state-owned enterprises (foreign-funded enterprises) has been improved most significantly in the first (second) technological finance pilots, while that of the public enterprises and private enterprises have declined most significantly in the second technological finance pilot. Therefore, in the implementation of the technological and financial strategy, the government should actively promote the ‘joint action’ mechanism of relevant departments, financial institutions, private enterprises, public enterprises, foreign-funded enterprises and state-owned enterprises, establish and improve a targeted system, take the market as the leading factor, and guide the society capital to actively participate in the practice of technology and finance innovation, and encourage the effective integration of technology, finance, and private capital to realise the high-quality development of financial enterprises and non-financial enterprises.

Fig. 1

The test chart of the common trend of two technological finance pilots
The test chart of the common trend of two technological finance pilots

Fig. 2

The common value range of the propensity scores of the enterprises in the experimental group and control group of two technological finance pilots
The common value range of the propensity scores of the enterprises in the experimental group and control group of two technological finance pilots

Regression results of the relationship between technological finance pilots, risk-taking and corporate value

Variables Panel A: First technological finance pilot Panel B: Second technological finance pilot
(1) (2) (3) (4)
Value 1 Value 2 Value 1 Value 2

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
R2 0.755 0.736 0.473 0.466
r2_a 0.715 0.691 0.469 0.462

Single factor test of the main variables of two technological finance pilots

Before pilot After pilot After – before
Mean Median Mean Median Mean difference Median difference

Panel A The first technological finance pilot

Enterprises in the non-pilot 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 **

Panel B the second technological finance pilot

Enterprises in the non-pilot 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

Variable Panel A: The first technological finance pilot Panel B: The second technological finance pilots
Value 1 Value 2 Value 1 Value 2
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
R2 0.755 0.735 0.474 0.466

The balance test results of the PSM of two technological finance pilots

Variable Sample Mean difference test Standardised difference test

Mean of experiment group Mean of control group T test (P-value) Standardised difference Decline (%)

Panel A: The first technological finance pilot

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. T-stat
ATT 2.841 2.777 0.065 0.030 2.140
LR χ2 before matching 130.510
LR χ2 after matching 20.300

Panel B: The second technological finance pilot

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. T-stat
ATT 2.204 2.376 −0.172 0.047 −3.700
LR χ2 before matching 70.420
LR χ2 after matching 10.860

Definition and measurement of variables

Variables Definition Measurement

Value Corporate value Value1ijt = market value/assetsValue2ijt = market value/(total assets-net intangible assets-net good will)
DID The implementation of technological finance pilot policies DIDit = Treatit × ImpitTreat: enterprise is (not) in experimental (control) groupImpit: the policy implementation dummy variable
RI Corporate risk-taking RT1ijt=Σt=1T(Adj_Roaijt1TΣt=1TAdj_Roaijt)2T1 RT{1_{ijt}} = \sqrt {{{\Sigma _{t = 1}^T{{\left( {Adj\_Ro{a_{ijt}} - {1 \over T}\Sigma _{t = 1}^TAdj\_Ro{a_{ijt}}} \right)}^2}} \over {T - 1}}} RT 2ijt = Max (Ad j_Roaijt) − Min(Ad j_Roaijt)Ad j_Roaijt is adjusted return on total assets by industry; T is the number of periods
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

Panel A: The first technological finance pilot Panel B: The second technological finance pilot
Variables (1)Value 1 (2)Value 2 Variables (1)Value 1 (2)Value 2

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
R2 0.505 0.505 R2 0.465 0.459
r2_a 0.496 0.495 r2_a 0.461 0.454

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