Firm exporting and productivity: what if productivity is no longer a black box

Figures & data

Figure 1. Export, export per person ratio and export-GDP ratio. Source: Statistical yearbook (various issues) from Vietnam general statistical offices.

Table 1. The distribution of SMEs (by employees).

	Year
	2000	2001	2002	2003	2004	2005	2006	2007	2008
Distribution
Total number of firms (including SMEs)	42,288	51,680	62,908	72,012	91,756	112,950	131,318	155,771	205,689
SMEs (percentage in total)	92%	93%	93%	94%	95%	96%	96%	96%	97%
Average growth rate of SMEs		23%	22%	15%	29%	24%	17%	19%	33%
Micro enterprises	54%	54%	53%	51%	53%	56%	61%	61%	62%
Small enterprises	34%	35%	37%	39%	38%	37%	32%	33%	33%
Medium enterprises	4%	4%	4%	3%	3%	3%	3%	3%	2%

Sources: Anh, Mai, Nhat, and Chuc (2011), (calculations based on Enterprise Census 2001–2009).

Table 2. Random effects dynamic probit estimates.^a

Variables	(1)	(2)	(3)	(4)	(5)
Export(t−1)	1.3143** (0.287)	1.3410** (0.284)	1.3160** (0.285)	1.3229** (0.283)	1.3231** (0.283)
Levin & Petrin TFP(t)	0.0023** (0.001)
Stochastic frontier TFPc (t)		1.6207** (0.373)
LP(t)			0.0029* (0.001)
TFP(t−1)				−0.0000 (0.000)
LP(t−1)					−0.0001 (0.001)
Firm age(t−1)	−0.0065 (0.006)	−0.0060 (0.006)	−0.0065 (0.006)	−0.0065 (0.006)	−0.0065 (0.006)
Firm size(t−1)	0.0029* (0.001)	0.0035** (0.001)	0.0032* (0.001)	0.0033* (0.001)	0.0032* (0.001)
Capital intensity(t−1)	0.0000 (0.000)	0.0000 (0.000)	−0.0000 (0.000)	0.0000 (0.000)	0.0001 (0.000)
Trade relationship (t−1)	0.6175** (0.232)	0.6252** (0.232)	0.6156** (0.231)	0.6127** (0.230)	0.6130** (0.231)
Average wage(t−1)	0.0016 (0.007)	−0.0025 (0.006)	0.0022 (0.006)	0.0032 (0.006)	0.0034 (0.006)
Credit constraint(t−1)	0.1201 (0.149)	0.1301 (0.150)	0.1251 (0.148)	0.1227 (0.148)	0.1228 (0.148)
Innovation(t−1)	0.2230^+ (0.116)	0.2132^+ (0.116)	0.2256^+ (0.115)	0.2270* (0.114)	0.2270* (0.114)
Government support(t−1)	−0.0293 (0.110)	−0.0584 (0.111)	−0.0286 (0.110)	−0.0342 (0.110)	−0.0344 (0.110)
Urban dummy	0.1401 (0.106)	0.1274 (0.106)	0.1480 (0.106)	0.1686 (0.105)	0.1688 (0.105)
Join-stock ownership	0.7885** (0.255)	0.6277* (0.259)	0.8103** (0.255)	0.8206** (0.254)	0.8207** (0.254)
Private ownership	0.5719** (0.126)	0.4981** (0.126)	0.5859** (0.125)	0.6012** (0.124)	0.6014** (0.124)
Partnership ownership	0.7136** (0.224)	0.6098** (0.226)	0.7203** (0.224)	0.7114** (0.223)	0.7111** (0.223)
Low tech	0.2079* (0.100)	0.1840^+ (0.099)	0.2006* (0.100)	0.1831^+ (0.098)	0.1827^+ (0.098)
Year 2009	0.1404 (0.107)	0.2248* (0.109)	0.1433 (0.106)	0.1487 (0.106)	0.1487 (0.106)
Constant	−2.7691** (0.209)	−2.9928** (0.220)	−2.7742** (0.209)	−2.7356** (0.204)	−2.7347** (0.204)
Observations	4992	4992	4992	4992	4992

Notes: Robust standard errors in parentheses; (**), (*), and (⁺) indicate levels of significance at 1%, 5%, and 10% respectively. The estimated coefficients are reported. The base categories for ownership are household ownership, while reference group for low tech dummy is a combined group of medium and high tech sectors.

^aAs a robustness check, the above model is re-estimated using fixed effect estimations for the whole sample. In addition, firm exporting is much various among levels of technology (Ministry of Industry and Trade of Vietnam & United Nations Industrial Development Organisation, 2011). Hence, we also consider the role of export participation on productivity growth and its decompositions in sub-samples according technological level classification (See Vu, Lim, Holmes, & Doan, 2013) for Vietnamese Technological level classification). However, the similar results are created in all cases, and they are available on requests.

Table 3. Fixed effect IV estimates (GMM estimation).^a

Variables	LP	Levinson–Petrin TFPc	Stochastic frontier
			TFPc	TPc	TEc	SEc
	(1)	(2)	(3)	(4)	(5)	(6)
Export	0.1722 (0.173)	−0.0016 (0.216)	0.0141 (0.031)	0.0026 (0.006)	−0.0000 (0.000)	0.0113 (0.027)
Firm size	−0.0081** (0.002)	0.0034 (0.003)	0.0097** (0.001)	0.0013** (0.000)	−0.0000 (0.000)	0.0084** (0.001)
Firm size squared	0.0000* (0.000)	−0.0000 (0.000)	−0.0000** (0.000)	−0.0000** (0.000)	0.0000^+ (0.000)	−0.0000** (0.000)
Firm age	−0.0016 (0.002)	−0.0021 (0.002)	0.0004 (0.000)	0.0001 (0.000)	0.0000 (0.000)	0.0004 (0.000)
Average wage	0.0887** (0.010)	0.0617** (0.011)	0.0016 (0.001)	0.0006* (0.000)	0.0000** (0.000)	0.0010 (0.001)
Innovation dummy	0.0233 (0.028)	0.0631* (0.029)	−0.0050 (0.006)	0.0001 (0.001)	−0.0000 (0.000)	−0.0052 (0.005)
Low tech sectors	0.0431 (0.062)	0.0203 (0.061)	−0.0115 (0.011)	−0.0009 (0.002)	−0.0001 (0.000)	−0.0105 (0.009)
Household ownership	0.0146 (0.078)	0.1004 (0.069)	0.0017 (0.014)	−0.0063* (0.003)	−0.0001 (0.000)	0.0081 (0.012)
Year 2009	−0.0201 (0.018)	−0.0896** (0.020)	−0.0414** (0.003)	−0.0294** (0.001)	−0.0014** (0.000)	−0.0104** (0.003)
Observations	3328	3328	3328	3328	3328	3328
Centred R-squared	0.194	0.080	0.322	0.588	0.871	0.280
Excluded instruments	Trade relationship and ethnicity of owner	Trade relationship and ethnicity of owner	Trade relationship and ethnicity of owner	Trade relationship and ethnicity of owner	Trade relationship and ethnicity of owner	Trade relationship and ethnicity of owner
Weak identification test (Cragg–Donald Wald F statistic) [Stock–Yogo weak id test critical value at 10%]	408.939 [19.93]	408.939 [19.93]	408.939 [19.93]	408.939 [19.93]	408.939 [19.93]	408.939 [19.93]
Hansen J statistic (overid test) [p-value in bracket]	0.701 [0.4023]	1.341 [0.2468]	2.445 [0.117]	0.000 [0.985]	0.167 [0.682]	3.029 [0.082]

Notes: Robust clustered standard errors in parentheses; **significance at 1%, * significance at 5%, + significance at 10%. The models also account for sector differences.

^aOne may worry that minority ethnicity can affect productivity through other network related effects that do not have to do with exports per se. We conduct just-identified specification with using only ‘minority ethnicity as an IV, we find that this is a weak IV.

Table

Null hypothesis	Log-likelihood	Test statistics (λ)	Critical value^a		Decision
			1%	5%
I. Cobb–Douglas
H0: βll = βlk = βkk = βtt = βlt = βkt = 0	−5144.43	83.46	16.81	12.59	Reject Ho
II. No stochastic inefficiency
H0: γ = 0	−5112.5	432.527	10.51	7.045	Reject Ho

Note: The hypothesis (I) assumes that SMEs follow a Cobb–Douglas production function. Thus, the hypothesis is tested by using the likelihood-ratio test statistic (λ) that is defined as . The value of the λ statistic in the first row greatly exceeds critical value. This indicates that the Translog function is the appropriate choice for our data. The value of the test hypothesis is reported automatically as ‘LR test of the one-sided error’ in Frontier 4.1 and is used to test the hypothesis (II). The result shows that using OLS or average production function estimation will underestimate the actual frontier because of the existence of technical inefficiency.

^aCritical values for these tests are taken from Table 1 of Kodde and Palm (1986).

Table

Variables	Definitions	Obs	Mean	SD
Exporter	1 if firm has export activities; 0 otherwise	4992	0.052	0.223
Levin & Petrin TFP	TFP predicted from Levinsohn–Petrin methodology	4992	18.71	91.23
Stochastic frontier TFPc	TFP change calculated from stochastic frontier methodology	3328	0.156	0.118
Tec	Technical efficiency change predicted from SEPF	3328	−0.025	0.009
TPc	Technical change predicted from SFPF	3328	0.160	0.053
Sec	SE change predicted from SEPF	3328	0.021	0.09
LP	Labour productivity calculated by value added per total employees	4992	12.78	55.79
Firm size	Total employment	4992	15.73	27.7
Capital intensity	The ratio of capital over total employment	4992	59.68	131.94
Firm age	The number of years since established	4992	14.0	10.7
Trade link	1 if firms have a long-term relationship with foreign partners, 0 otherwise	3328	0.03	0.171
Average real wage	Ratio of total wage to total employees	4992	3.89	5.07
Innovation	1 if firms introduced new products, had major improvements in existing products, or introduced new production processes or technology, 0 otherwise	4992	0.54	0.498
Credit constraint	1 if firms applied for a loan but failed to obtain the loan, 0 otherwise	4992	0.078	0.26
Government support	1 if a firm receives investment incentives or loans, a human resource training programme, national key trade programme, quality and technology improvement programme, or other type of government assistance, 0 otherwise	3328	0.282	0.45
Private ownership	1 if firms have private or limited liability ownerships, 0 otherwise	4992	0.233	0.423
Partnership ownership	1 if firms have partnership or cooperative ownerships, 0 otherwise	4992	0.03	0.171
Join-stock ownership	1 if firms have joint-stock ownerships, 0 otherwise	4992	0.015	0.124
Urban dummy	1 if firm located in Hanoi, Haiphong or Ho Chi Minh, 0 otherwise	4992	0.384	0.486
Ethnicity of owners	1 if owners belong to minority ethnic group, 0 otherwise	3328	0.071	0.256

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