Access to credit and labour productivity: a new insight from Vietnamese firms

Figures & data

Table 1. Descriptive statistics of variables.

Variable	Mean	Std. Dev	Min	Max
Labour productivity (log.)	11.388	1.332	−0.059	18.135
Access to credit	0.402	0.490	0.000	1.000
Firm age	15.236	10.123	1.000	75.000
Assets (log.)	13.650	1.824	4.595	19.567
Registration	0.582	0.493	0.000	1.000
Investment	0.543	0.498	0.000	1.000
Network size	32.308	39.875	0.000	978.000
Distance	0.684	0.809	0.000	2.000
Credit history	0.305	0.460	0.000	1.000
Party member	0.095	0.293	0.000	1.000
Owner’s age	46.509	10.420	17.000	94.000
Gender (Male = 1)	0.669	0.471	0.000	1.000
Education	0.250	0.433	0.000	1.000
Location (Urban = 1)	0.359	0.480	0.000	1.000
PCI ranking	31.233	15.758	3.000	62.000
Number of observations	6080

Table 2. Labour productivity and firm size by access to credit.

Variable	Access to credit (ATC)		Non-access to credit (NATC)
	Mean	Std. Dev	Mean	Std. Dev
Labour productivity (thousand VND)	231,024.20	1,569,839	143,111.40	242,097.80
Labour productivity (log.)	11.53	1.30	11.29	1.34
Total assets (thousand VND)	6,206,400	17,500,000	2,664,634	9,030,910
Total assets (log.)	14.07	1.84	13.37	1.76
Number of observations	2445		3630
Percent	40.25		59.75

Table 3. First stage regression: determinants of access to credit.

Variable	Random-effects logit		Pooled logit		Correlated random-effects logit
	Coef	Odd Ratios	Coef	Odd Ratios	Coef	Odd Ratios
	[1]	[2]	[3]	[4]	[5]	[6]
Firm age	−0.007 (0.006)	0.993 (0.006)	−0.007 (0.004)	0.993 (0.004)	−0.003 (0.007)	0.997 (0.007)
Assets (log.)	0.195*** (0.035)	1.215*** (0.043)	0.164*** (0.028)	1.178*** (0.033)	0.170*** (0.059)	1.185*** (0.070)
Registration	0.123 (0.116)	1.131 (0.131)	0.077 (0.099)	1.080 (0.107)	0.282* (0.145)	1.325* (0.192)
Investment	1.201*** (0.106)	3.323*** (0.351)	1.094*** (0.088)	2.987*** (0.263)	1.071*** (0.121)	2.917*** (0.353)
Network size	0.001*** (0.0004)	1.001*** (0.0004)	0.001*** (0.0004)	1.001*** (0.0004)	0.002*** (0.001)	1.002*** (0.001)
Distance	2.432*** (0.100)	11.379*** (1.136)	2.173*** (0.080)	8.785*** (0.704)	2.239*** (0.105)	9.384*** (0.984)
Credit history	0.595*** (0.113)	1.813 (0.206)	0.545*** (0.097)	1.725*** (0.168)	0.395*** (0.125)	1.484*** (0.185)
Party member	0.437*** (0.166)	1.549*** (0.257)	0.411*** (0.134)	1.508*** (0.201)	0.440*** (0.167)	1.553*** (0.259)
Owner’s age	−0.007 (0.005)	0.993 (0.005)	−0.005 (0.004)	0.995 (0.004)	0.000 (0.008)	1.000 (0.008)
Gender (Male = 1)	−0.150 (0.107)	0.861 (0.092)	−0.127 (0.088)	0.880 (0.077)	−0.310** (0.148)	0.734** (0.108)
Education	0.020 (0.115)	1.020 (0.118)	−0.016 (0.103)	0.985 (0.101)	0.074 (0.139)	1.077 (0.150)
Location (Urban = 1)	−1.098*** (0.145)	0.333*** (0.048)	−0.938*** (0.111)	0.392*** (0.044)	−0.884*** (0.152)	0.413*** (0.063)
PCI ranking	−0.008** (0.003)	0.992** (0.003)	−0.007** (0.003)	0.993** (0.003)	−0.007* (0.004)	0.993* (0.004)
Intercept	−4.927*** (0.527)	0.007*** (0.004)	−4.259*** (0.405)	0.014*** (0.006)	−4.337*** (0.699)	0.013*** (0.009)
Year effects	Yes	Yes	Yes	Yes	Yes	Yes
Mundlak correction	No	No	No	No	Yes	Yes
Observations	6080	6080	6080	6080	6080	6080

This table presents the determinants of credit access. Dependent variable is access to credit. Columns [1], [3], and [5] denote the values of coefficients in random-effects logit, pooled logit, and correlated random-effects logit regressions, respectively. *, **, and *** denote significance at the 10, 5, and 1% levels, correspondingly. Robust standard errors are in parentheses shown in Columns [2], [4], and [6].

Table 4. Testing for endogeneity.

	𝜒^2(1)	p-Value
	[1]	[2]
Durbin-Wu-Hausman test for endogeneity of regressors	5.551**	[0.018]
Sanderson-Windmeijer test of excluded instruments	840.390***	[0.000]
Kleibergen-Paap rk LM test for underidentification	559.350***	[0.000]
Hansen-J test for overidentification of all instruments	0.572	[0.449]

This table presents the test for endogeneity. Dependent variable is labour productivity. Columns [1] and [2] denote 𝜒² and p-value, respectively. **, and *** denote significance at the 5% and 1% levels, correspondingly. p-Value is shown in brackets.

Table 5. Second stage IV regression: access to credit and labour productivity.

Variable	Random-effects IV		Fixed-effects IV		Pooled OLS IV
	[1]	[2]	[3]	[4]	[5]	[6]
Access to credit	0.247***	(0.058)	0.241***	(0.066)	0.242***	(0.054)
Firm age	−0.007***	(0.002)	0.001	(0.002)	−0.010***	(0.002)
Assets (log.)	0.165***	(0.012)	0.142***	(0.030)	0.169***	(0.014)
Registration	0.106**	(0.041)	0.041	(0.037)	0.118***	(0.041)
Investment	−0.042	(0.039)	−0.028	(0.039)	−0.056	(0.038)
Network size	0.0004**	(0.000)	0.0002	(0.000)	0.001**	(0.000)
Party member	−0.025	(0.058)	0.006	(0.069)	−0.037	(0.053)
Owner’s age	−0.006***	(0.002)	−0.006**	(0.003)	−0.005***	(0.002)
Gender (Male = 1)	0.074**	(0.036)	−0.026	(0.054)	0.111***	(0.034)
Education	0.082**	(0.039)	0.082	(0.051)	0.072*	(0.038)
Location (Urban = 1)	0.278***	(0.049)			0.248***	(0.043)
PCI ranking	0.001	(0.001)	0.003**	(0.001)	0.0003	(0.001)
Intercept	8.744***	(0.175)			8.702	(0.177)
Year effects	Yes		Yes		Yes
R-squared	0.130		0.134		0.198
Number of observations	6080		6080		6080

This table presents the impact of access to credit on labour productivity. Dependent variable is labour productivity. Columns [1], [3], and [5] denote the values of coefficients in random-effects IV, fixed-effects IV, and pooled OLS IV regressions, respectively. *, **, and *** denote significance at the 10, 5, and 1% levels, correspondingly. Robust standard errors are in parentheses shown in Columns [2], [4], and [6].

Table 6. Difference of labour productivity by access to credit.

	Non-access to credit	Access to credit	t-Stat
Observations	3630	2445
Mean	11.2909	11.5334	−6.9861***
Standard error	0.0223	0.0263
Standard deviation	1.3439	1.3003

This table presents the difference of labour productivity by access to credit. Dependent variable is labour productivity. *** denotes significance at the 1% level. The main hypothesis is the difference between mean logarithm of labour productivity of non-credit access firms and that of credit access ones. The t-test hypothesis is H₀: difference = 0, and H_a: difference < 0.

Table 7. Further empirical results on access to credit and labour productivity.

Variable	Random-effects IV		Fixed-effects IV		Pooled OLS IV
	[1]	[2]	[3]	[4]	[5]	[6]
Panel A: Competition
Access to credit	0.064***	(0.015)	0.059***	(0.017)	0.065***	(0.015)
Firm age	−0.007***	(0.002)	0.001	(0.002)	−0.010***	(0.002)
Assets (log.)	0.154***	(0.013)	0.139***	(0.018)	0.157***	(0.012)
Registration	0.085**	(0.041)	0.042	(0.050)	0.090**	(0.040)
Investment	−0.059	(0.040)	−0.041	(0.043)	−0.076*	(0.041)
Network size	0.000**	(0.000)	0.000	(0.000)	0.001***	(0.000)
Industry-level competition	0.517***	(0.139)	−1.176	(4.593)	0.492***	(0.110)
Party member	−0.012	(0.058)	0.011**	(0.073)	−0.022	(0.054)
Owner’s age	−0.006***	(0.002)	−0.006	(0.003)	−0.005***	(0.002)
Gender (Male = 1)	0.070*	(0.036)	−0.029*	(0.048)	0.107***	(0.033)
Education	0.080**	(0.039)	0.081	(0.045)	0.069*	(0.039)
Location (Urban = 1)	0.282***	(0.050)			0.259***	(0.041)
PCI ranking	0.001	(0.001)	0.003**	(0.002)	0.000	(0.001)
Intercept	8.559***	(0.204)	10.214**	(3.982)	8.551***	(0.180)
Year effects	Yes		Yes		Yes
R-squared	0.201		0.123		0.202
Panel B: Firm growth
Access to credit	0.064***	(0.015)	0.059***	(0.017)	0.065***	(0.015)
Firm age	−0.007***	(0.002)	0.001	(0.002)	−0.010***	(0.002)
Assets (log.)	0.161***	(0.012)	0.138***	(0.018)	0.166***	(0.011)
Registration	0.104**	(0.041)	0.040	(0.050)	0.115***	(0.040)
Investment	−0.055	(0.040)	−0.042	(0.043)	−0.069*	(0.041)
Network size	0.000**	(0.000)	0.000	(0.000)	0.001***	(0.000)
Firm growth	0.268***	(0.046)	0.238***	(0.049)	0.291***	(0.048)
Party member	−0.015	(0.058)	0.014	(0.073)	−0.026	(0.054)
Owner’s age	−0.006***	(0.002)	−0.006**	(0.003)	−0.005***	(0.002)
Gender (Male = 1)	0.076**	(0.036)	−0.029	(0.048)	0.115***	(0.033)
Education	0.079**	(0.039)	0.081*	(0.045)	0.066*	(0.039)
Location (Urban = 1)	0.288***	(0.050)			0.260***	(0.041)
PCI ranking	0.001	(0.001)	0.004**	(0.002)	0.000	(0.001)
Intercept	8.879***	(0.181)	9.191***	(0.269)	8.830***	(0.165)
Year effects	Yes		Yes		Yes
R-squared	0.203		0.172		0.204

This table presents the impact of access to credit on labour productivity. Dependent variable is labour productivity. Columns [1], [3], and [5] denote the values of coefficients in random-effects IV, fixed-effects IV, and pooled OLS IV regressions, respectively. *, **, and *** denote significance at the 10, 5, and 1% levels, correspondingly. Robust standard errors are in parentheses shown in Columns [2], [4], and [6]. Number of observations is 6080.

Table A1. Description of variables.

Variable	Definition	Literature
Dependent variable
Labour productivity (log.)	The ratio of revenue to the number of labours of a firm.	Xuefeng & Yaşar, 2016; Waheed, 2017
Independent variables
Access to credit	Dummy variable, taking 1 if a firm obtained either short term or long term formal loan(s) or if a firm had informal loan(s), and 0 otherwise.	Le, 2012; Nguyen & Luu, 2013; Tran & Santarelli, 2013
Firm age	The age of firm in years, calculated by the difference between the survey year and firm’s establishment year.	Motta, 2020; Rahaman, 2011
Assets (log.)	Firm economic size, measured by the logarithm of total assets.	Rahaman, 2011
Registration	Dummy variable, taking 1 if a firm registered under the Law on Enterprises or if a firm has Business Registration Application and 0 otherwise.	Archer, 2021; Wellalage & Locke, 2016
Investment	Dummy variable, taking 1 if a firm has made any investment since the last survey and 0 otherwise.	Tran & Santarelli, 2013
Network size	The number of business associations which a firm is a member of.	Tran & Santarelli, 2013
Party member	Dummy variable, taking 1 if the owner is a member of the Communist Party and 0 otherwise.	Nguyen & Luu, 2013
Owner’s age	The age of owner/manager in years, calculated by the difference between the surveying year and the owner’s year of birth.	Nguyen et al., 2019
Gender	Dummy variable, taking 1 if owner’s gender is male and 0 if female.	Motta, 2020; Nguyen et al., 2019
Education	Dummy variable, taking 1 if the owner completed an undergraduate or a postgraduate program and 0 otherwise (No professional education, Vocational education, Technical secondary education).	Nguyen et al., 2019; Nguyen & Luu, 2013
Urban	Dummy variable, taking 1 if a firm is located in urban cities (Ha Noi, Hai Phong, Ho Chi Minh City) and 0 otherwise.	Nguyen & Luu, 2013
PCI ranking	The ranking of a province based upon its overall score of Provincial Competitiveness Index.	Cao, 2014; Le, 2012
Control variables
Distance	Ordered variable, taking 0 if no distance, 1 if the distance is below 5 km, and 2 if the distance is above 5 km.	Ellis et al., 2010
Credit history	Dummy variable, taking 1 if a firm has borrowed from the main creditor (formal or informal) before and 0 otherwise.	Krasniqi, 2010

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Data availability statement

The data that support the findings of this study are available from the corresponding author, Van Thu Pham, upon reasonable request.