Can fintech make corporate investments more efficient? A study on financing constraints and agency conflicts

Figures & data

Figure 1. China’s economic growth and investment efficiency (2002–2020).

Note: The data were obtained from the China Statistical Yearbook.

Source: Authors.

Table 1. Keywords related to the level of fintech.

Perspective	Keywords
Conceptual perspective	Fintech, Internet finance, Digital finance
Technical perspective	Artificial intelligence, Blockchain, Cloud computing, Big data, Mobile internet, Biometrics, Internet of things, 5 G, Quantum computing
Functional perspective	Online financing, Online loans, Crowdfunding, Equity crowdfunding, Digital notes, Digital currency, Digital payments, Online payments, Mobile payments, Third-party payments
Service perspective	Internet banking, Online banking, Unmanned banking, E-banking

Source: Authors.

Table 2. Descriptive statistics of variables.

Definition	Variable	Obs	Mean	S.D.	Min	Max
Investment efficiency	Inveff_R	11977	0.0533	0.0882	0.0000	1.9582
Under-investment	Underinv_R	7665	0.0418	0.0350	0.0000	0.5148
Over-investment	Overinv_R	4312	0.0738	0.1370	0.0000	1.9582
Fintech development level	Fintech	11977	7.4153	2.9820	1.3863	12.6253
Corporate size	Size	11977	22.2529	1.3214	19.7228	26.2981
Corporate age	Age	11977	2.2661	0.6423	1.0986	3.2189
Corporate nature	State	11977	0.4088	0.4916	0.0000	1.0000
Cash holding level	Cash	11977	0.2106	0.1657	0.0146	0.9228
Equity balance	Top10	11977	0.5680	0.1540	0.2174	0.8991
Board size	Bsize	11977	8.7256	1.7228	5.0000	15.0000
Board independence	Indep	11977	0.3739	0.0536	0.3333	0.5714
Economic development	Gdp	11977	0.1010	0.0483	−0.0339	0.2521
Financial development	Finance	11977	1.4571	0.5924	0.4021	3.1519

Source: Authors.

Table 3. Baseline regression results.

	(1)	(2)	(3)	(4)
	Inveff_R	Inveff_R	Inveff_R	Inveff_R
Fintech	−0.0002	−0.0010***	−0.0016***	−0.0016***
	(0.0004)	(0.0003)	(0.0004)	(0.0004)
Size			−0.0011	−0.0014
			(0.0010)	(0.0009)
Age			0.0057***	0.0057***
			(0.0021)	(0.0021)
State			−0.0135***	−0.0140***
			(0.0020)	(0.0021)
Cash			0.1025***	0.1009***
			(0.0130)	(0.0126)
Top10			0.0296***	0.0291***
			(0.0056)	(0.0060)
Bsize			−0.0010*	−0.0009*
			(0.0005)	(0.0005)
Indep			−0.0032	−0.0048
			(0.0151)	(0.0138)
Gdp			0.0133	0.0010
			(0.0248)	(0.0253)
Finance			0.0003	0.0008
			(0.0017)	(0.0017)
Constant	0.0549***	0.0604***	0.0511**	0.0606***
	(0.0025)	(0.0025)	(0.0214)	(0.0199)
Industry	NO	YES	YES	NO
Year	NO	YES	YES	NO
Year × Industry	NO	NO	NO	YES
Obs	11977	11977	11977	11977
${\mathrm{R}}^2$	0.0358	0.0936	0.1197	0.1467

Note: *, **, and *** indicate that the variable is significant at the level of 10%, 5%, and 1%, respectively. Standard errors clustered at the city level in parentheses.

Source: Authors.

Figure 2. The relationship between of fintech and investment efficiency: graphical analysis.

Note: Fintech and investment efficiency in the figure are the city means of the sample corporates.

Source: Authors.

Table 4. Dynamic effects test.

	(1)	(2)	(3)	(4)
	Inveff_R	Inveff_R	Inveff_R	Inveff_R
L.Fintech	−0.0016***
	(0.0004)
L2.Fintech		−0.0021***
		(0.0006)
L3.Fintech			−0.0025***
			(0.0008)
L4.Fintech				−0.0033***
				(0.0011)
Controls	YES	YES	YES	YES
Year × Industry	YES	YES	YES	YES
Obs	9088	6674	4659	2920
${\mathrm{R}}^2$	0.2169	0.2328	0.2440	0.2819

Note: *, **, and *** indicate that the variable is significant at the level of 10%, 5%, and 1%, respectively. Standard errors clustered at the city level in parentheses.

Source: Authors.

Table 5. Heterogeneity analysis.

	State-owned	Non-state-owned	Non-growth	Growth
	(1) Inveff_R	(2) Inveff_R	(3) Inveff_R	(4) Inveff_R
Fintech	−0.0005	−0.0019***	−0.0002	−0.0021***
	(0.0004)	(0.0005)	(0.0003)	(0.0007)
Controls	YES	YES	YES	YES
Year × Industry	YES	YES	YES	YES
Obs	4896	7081	4113	5858
${\mathrm{R}}^2$	0.2427	0.2160	0.3253	0.2443
	Better corporate governance	Weaker corporate governance	Higher industry competition	Lower industry competition
	(5) Inveff_R	(6) Inveff_R	(7) Inveff_R	(8) Inveff_R
Fintech	−0.0007	−0.0018***	−0.0008	−0.0021***
	(0.0008)	(0.0004)	(0.0005)	(0.0006)
Controls	YES	YES	YES	YES
Year × Industry	YES	YES	YES	YES
Obs	4308	7669	6780	5197
${\mathrm{R}}^2$	0.3443	0.1643	0.1044	0.1725

Note: *, **, and *** indicate that the variable is significant at the level of 10%, 5%, and 1%, respectively. Standard errors clustered at the city level in parentheses.

Source: Authors.

Table 6. Replacement investment efficiency, fintech and remove special samples.

	Replacement investment efficiency		Replacement fintech		Remove special samples
	(1) Inveff_B	(2) Inveff_C	(3) Inveff_R	(4) Inveff_R	(5) Inveff_R	(6) Inveff_R
Fintech	−0.0016***	−0.0014***			−0.0016***	−0.0016***
	(0.0003)	(0.0004)			(0.0004)	(0.0004)
FintechI			−0.0002***
			(0.0001)
FintechU				−0.0002***
				(0.0000)
Controls	YES	YES	YES	YES	YES	YES
Year × Industry	YES	YES	YES	YES	YES	YES
Obs	10393	10403	11977	11977	9548	9722
${\mathrm{R}}^2$	0.1596	0.1774	0.2015	0.2017	0.2020	0.2107

Note: *, **, and *** indicate that the variable is significant at the level of 10%, 5%, and 1%, respectively. Standard errors clustered at the city level in parentheses.

Source: Authors.

Table 7. Instrumental variable method.

	(1)	(2)
	Inveff_R	Inveff_R
Fintech	−0.0030***	−0.0070***
	(0.0006)	(0.0016)
Kleibergen-Paap rk LM statistic	628.631	288.715
	[0.0000]	[0.0000]
Kleibergen-Paap rk Wald F statistic	811.103	137.166
	{22.30}	{22.30}
Hansen J statistic P-value	0.5959	0.9528
Controls	YES	YES
Year × Industry	YES	YES
Obs	11977	9160
${\mathrm{R}}^2$	0.0450	0.0341

Note: *, **, and *** indicate that the variable is significant at the level of 10%, 5%, and 1%, respectively. Standard errors clustered at the city level in parentheses, p-values in middle parentheses, and Stock-Yogo weak ID test critical values (10% maximal IV size) in large parentheses.

Source: Authors.

Table 8. Regression results of under-investment and over-investment.

	(1)	(2)
	Underinv_R	Overinv_R
Fintech	−0.0003**	−0.0028***
	(0.0001)	(0.0009)
Controls	Yes	Yes
year × industry	Yes	Yes
Coefficient difference test	P = 0.0044
Obs	7665	7665
${\mathrm{R}}^2$	0.3787	0.3787

Note: *, **, and *** indicate that the variable is significant at the level of 10%, 5%, and 1%, respectively. Standard errors clustered at the city level in parentheses.

Source: Authors.

Table 9. Fintech, financing constraints and corporate under-investment.

	(1)	(2)	(3)	(4)
	Inveff_R	Underinv_R	Inveff_R	Underinv_R
Fintech	−0.0017***	−0.0003*	−0.0013***	−0.0003*
	(0.0004)	(0.0002)	(0.0005)	(0.0002)
FC1_D	0.0056	0.0048***
	(0.0035)	(0.0015)
Fintech × FC1_D	0.0006	−0.0003*
	(0.0004)	(0.0002)
FC2_D			0.0018	0.0043**
			(0.0043)	(0.0017)
Fintech × FC2_D			−0.0005	−0.0004*
			(0.0006)	(0.0002)
Controls	YES	YES	YES	YES
Year × Industry	YES	YES	YES	YES
Obs	11977	7665	11977	7665
${\mathrm{R}}^2$	0.1399	0.2970	0.1469	0.2932

Note: *, **, and *** indicate that the variable is significant at the level of 10%, 5%, and 1%, respectively. Standard errors clustered at the city level in parentheses.

Source: Authors.

Table 10. Fintech, agency conflict, and corporate over-investment.

	(1)	(2)	(3)	(4)
	Inveff_R	Overinv_R	Inveff_R	Overinv_R
Fintech	−0.0011**	−0.0021**	−0.0014***	−0.0022**
	(0.0005)	(0.0009)	(0.0004)	(0.0009)
AC1_D	0.0088*	0.0175**
	(0.0046)	(0.0082)
Fintech × AC1_D	−0.0008	−0.0018*
	(0.0006)	(0.0010)
AC2_D			0.0100***	0.0231***
			(0.0031)	(0.0072)
Fintech × AC2_D			−0.0004	−0.0013*
			(0.0004)	(0.0008)
Controls	YES	YES	YES	YES
Year × Industry	YES	YES	YES	YES
Obs	11977	4312	11977	4312
${\mathrm{R}}^2$	0.1371	0.2178	0.1481	0.2418

Note: *, **, and *** indicate that the variable is significant at the level of 10%, 5%, and 1%, respectively. Standard errors clustered at the city level in parentheses.

Source: Authors.