Sustainability 2019, 11, x doi: FOR PEER REVIEW www.mdp i.comjournal sustainab ility [623018]

Sustainability 2019, 11, x; doi: FOR PEER REVIEW www.mdp i.com/journal/ sustainab ility
Type of the Paper (Article, Review, Communication, etc.) 1
Financing Constraints, Trade Mode Transition and 2
Global V alue Chain Upgrading of Chinese Firm s 3
Lin Chen1, Sumei Luo 2 and Tian Zhao 2,* 4
1 School of Economics , East China Normal University, Shanghai, 200062, China; [anonimizat] 5
2 School of Finance, Shanghai University of Finance and Economics, Shanghai, 200433, China 6
* Correspondence: [anonimizat]; Tel.: +86 -21-65908396 7
3 Department of Economics, the University of North Carolina at Chapel Hill, Chapel H ill, 27514, USA; 8
[anonimizat] 9
Received: date; Accepted: date; Published: date 10
Abstract: At present, China is facing a serious problem of "low -end locking" in the value chain while 11
deeply integrating into the global value chain. It can be said that how to comprehensively up grade 12
the position in the global value chain is the key to China's economic transformation and sustainable 13
development. From the perspective of financing constraints, this paper attempts to explore the 14
feasibility of upgrading China's global value chain. B ased on the theoretical framework, this paper 15
applies firm -level production data and also trade data, using Kee and Tang [1] method to measure 16
the domestic added value at the firm level, and comprehensively measure the financial constraints 17
faced by enterp rises by three methods. Empirical studies have found that the mitigation of financing 18
constraints can significantly increase the enterprises’ domestic value added, and this conclusion is 19
still valid after considering various robustness tests. Heterogeneity analysis indicates that the easing 20
of financing constraints can exert a more prominent effect on the value chain upgrading of Chinese 21
private enterprises. Finally, this paper analyzes the two mechanisms by which financing constraint 22
relief could promote g lobal value chain upgrading: First, the easing of financing constraints can 23
directly promote the transformation of enterprises' trade mode from processing trade to general 24
trade; Second, the improvement of corporate financing constraints can also make ente rprises climb 25
up the value chain, that is, to promote enterprises into a higher position in the value chain . 26
Keywords: financing constraints; value added at enterprise level; trade mode transition; value chain 27
upgrading 28
29
Introduction 30
Since the 199 0s, the division of labor in the global value chain has gradually emerged. Under this 31
trend, China is gradually integrated into and deeply embedded in the global value chain’s division 32
of labor with its cheap workforce. In the meantime, China is caught in a serious mismatch between 33
the size and profitability of trade. On the one hand, trade frictions caused by high trade surplus 34
escalate in practices; On the other hand, China faces low -end lock of global value chain caused by 35
processing trade. Especially in recent years, under the background of the continuing downturn in 36
foreign demand and the dissipation of domestic "demographic dividend", a major issue to achieve 37
China's sustainable economic growth is how to transform and upgrade the trade mode, enhance 38
China's position in the global value chain, so as to maintain its competitive advantage in global 39
trade. Based on the perspective of financing constraints, this paper attempts to explore the feasible 40
path to realize the transformation of Chinese enterprises' trade mode and the upgrading of global 41
value chain. 42

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With the gradual improvement of the global value chain division, a single enterprise is no longer 43
engaged in the entire production process of the value chain, but only involved in one or several 44
producti on links of product manufacturing. Therefore, the value of products exported also includes 45
the value of intermediate products imported from abroad, and the value created by a single 46
enterprise itself only accounts for a small part. Therefore, the measureme nt of a country's trade 47
interests and division of labor should be shifted from the perspective of product to the perspective 48
of value added . Based on the global value chain division of labor system, the domestic value added 49
in exports is a more accurate m easure of domestic production and exports [2]. In order to improve 50
the shortcomings of traditional statistical methods and measure China's real position in the global 51
value chain, value -added trade has been proposed and widely applied in recent years. 52
Hum mels et al.[3] creatively puts forwards the use of vertical specialization to measure the foreign 53
component of exports. Based on a country's input and output table, exports can be divided into 54
domestic added value and foreign added value. Thereafter, some articles calculate domestic added 55
value based on this method, such as Dean et al.[4]. However, Hummels et al. [3] assumes that 56
imports are used equally in the production of domestic and export products. In China, it is widely 57
acknowledged that processing t rade accounts for almost half of China's foreign trade exports, and 58
its export accounts for 67% of the world's total processing trade [5]. Imports under processing trade 59
are used exclusively for the production of exports, which contain large amounts of int ermediate 60
imports from abroad. Companies receive only modest processing fees, so the value added created 61
at home is limited. If the value -added method of processing trade is considered to calculate China's 62
trade surplus with the United States, China’s trad e surplus with the United States will be reduced 63
by 40% compared to traditional methods [2]. Koopman et al. [2] (KWW method for short) revised 64
the accounting method of Hummels et al. [3]. KWW method distinguished processing trade and 65
non-processing trade a nd recalculated them with the input -output table and found that the 66
previously calculated vertical specialization rate was underestimated. 67
Although the input -output table can reflect the division of labor between countries and industries, 68
it can only be us ed to calculate the domestic added value at the national and industry level without 69
considering the heterogeneity of enterprises. As there are enterprises with different sizes and 70
operating abilities in the same industry, the calculation results will be bi ased if the differences 71
between enterprises are not considered [1]. Upward et al. [6] made a pioneering study on the 72
calculation of domestic added value at the enterprise level. They calculated the domestic added 73
value of processing trade and general trade respectively using the database of China Customs 74
Import and Export Trade and the database of China Industrial Enterprises from 2000 to 2007, and 75
found that the domestic added value of processing trade was obviously low. Kee and Tang [1] also 76
used micro da ta to calculate the domestic added value rate at the enterprise level, and analyzed the 77
reasons for the increase of China's domestic added value in recent years. The study also showed 78
that the domestic added value rate of processing trade was much lower th an that of general trade. 79
Whether the value added at the industry level is calculated using the non -competitive input -output 80
table[7 -8], or the value -added calculated at the enterprise level [1,6], the results all show that the 81
value creation ability of pr ocessing trade is much lower than that of general trade. Similarly, for 82
every $1,000 of export products, according to China's GVC team, domestic value added in 83
processing trade is less than half of it in general trade. 84
It can be said that it is a feasible way to enhance the position of Chinese enterprises in the global 85
value chain by promoting the transformation of their trade mode from processing trade to general 86
trade. In addition, enterprises can bring more added value by upgrading from the simple 87
proces sing and assembly in the downstream to the production of intermediate products in the 88
upstream. However, the way in which enterprises choose to trade and how to integrate into the 89
global value chain is related to their own financing capacity. Manova and Yu [9] found in their 90
research that in China, enterprises with weaker financing ability are more inclined to integrate into 91
GVCS by processing trade, which indicates that the enterprises' financing ability will affect their 92

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choice of trade mode. It can be sh own that a good financial market environment can provide 93
effective protection for enterprises to raise funds and provide financial support for enterprises to 94
enter the international market. However, the lag of financial development in developing countries 95
is a phenomenon that often occurs during economic development [10]. According to the World 96
Bank Investment Environment Survey, about 80% of Chinese companies believe that financing 97
constraints are the main obstacle to their development. Financing constrain t sets a threshold for 98
capital required by enterprises to carry out high -end trade, and enterprises have to engage in 99
processing trade with low added value and low input cost [9]. In view of this, this paper attempts to 100
make contributions in the following two aspects: Firstly, different from the predecessors' calculation 101
of domestic value -added at the national or industry level, this paper uses Kee and Tang [1] methods 102
to calculate domestic value -added at the enterprise level by using micro -data of import a nd export 103
of Chinese industrial enterprises and customs. Second, this paper attempts to investigate the impact 104
of financing constraints on the status of Chinese enterprises in GVCS, and further analyzes through 105
what mechanism the mitigation of financing co nstraints could achieve the upgrade of Chinese 106
companies in the global value chain. 107
The rest of this paper is structured as follows: The second part makes a theoretical analysis on the 108
mechanism by which financing constraint could affect trade mode selecti on and value chain 109
upgrading. The third part calculates the domestic added value at the enterprise level and 110
introduces the data and variables. The fourth part uses the micro -enterprise data to conduct 111
empirical tests and gives the robustness test and anal ysis of the possible mechanism. The last part is 112
the conclusion of this paper. 113
Theoretical Analysis 114
According to the new new trade theory that considers heterogeneous firms, financing constraints 115
are the important factors affecting the export of enterpris es [11 -12]. Export enterprises face higher 116
entry costs than those in the domestic market. Long -term fixed costs, such as establishing 117
distribution networks abroad and paying for advertising and marketing, need to be paid in advance 118
before companies can ent er international markets. On the other hand, compared with sales in the 119
domestic market, transportation to other countries takes longer time and brings more capital 120
occupation, which requires higher liquidity within the enterprise. Therefore, whether the f inancing 121
of enterprises is constrained, whether the prepaid costs and variable costs in international trade can 122
be paid not only affects whether enterprises can export, but also affects the volume of trade 123
exported by enterprises. Moreover, scholars have e xpounded the relationship between financing 124
constraints and enterprise export from various perspectives. Manova and Yu [9] further found in 125
their research on China that the strength of enterprises' financing ability also affects their choice of 126
trade mode. Compared with general trade, processing trade needs less capital. Therefore, those 127
enterprises with weaker financing capacity tend to integrate into the global value chain by choosing 128
processing trade mode. Processing trade generates much lower firms’ pro fits and domestic value 129
added than general trade, which makes China locked in the lower end of the global value chain for 130
a long time. Therefore, it is believed that the financing constraints of enterprises should be eased in 131
the first place to upgrade Chi na's global value chain, so as to transform enterprises' low -value – 132
added trade mode such as processing trade. Based on the OECD [13] research on the upgrading 133
path of China's global value chain, it is believed that there are two mechanisms through which th e 134
mitigation of financial constraints could upgrade Chinese enterprises’ position in global value 135
chain: First, from the perspective of trade mode, the easing of financing constraints of enterprises 136
can promote the transformation of their trade mode from p rocessing trade to general trade, because 137
the latter trade mode can create more domestic added value but at the same time requiring more 138
capital investment. Second, from the perspective of the value chain position of enterprises, the 139
enhancement of financi ng ability can realize the transformation of Chinese enterprises from simple 140
assemblers to suppliers of parts and capital goods, that is, from processing and assembly at the 141
bottom of the value chain to intermediate manufacturers in the middle and upper po sition. 142

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First of all, we will demonstrate the first mechanism. General trade involves more domestic 143
production links and more domestic added value in the export products. While the raw materials, 144
materials and parts required for processing trade producti on are all from abroad, and only simple 145
assembly or welding and other processing procedures are carried out in China. In process trade, the 146
domestic process and raw materials required for product production are less, which caused process 147
trade to be a trad e mode with very low domestic value -added. In terms of capital demand, 148
enterprises need to pay in advance for product design, raw material purchase, import tariff, 149
product sales in general trade, so they need more adequate capital in advance compared with 150
processing trade. Processing trade is divided into two modes, that is processing with supplied 151
materials and processing with imported materials. Processing with supplied materials means that 152
the raw materials are provided by overseas enterprises. Domestic enterprises do not need to pay for 153
import, but only need to produce based on the requirements of overseas enterprises, and the 154
finished products are sold by overseas enterprises. In this way, domestic processing companies do 155
not need to pay for raw materia ls, nor do they have to bear the risk of sales of finished products. 156
Therefore, processing trade is considered as a trade mode with low cost, low risk and low capital 157
demand. In the processing with imported materials, domestic enterprises pay foreign curre ncies to 158
import raw materials, then assemble and process them before exporting them. In this way, although 159
domestic enterprises need to pay the import cost of raw materials in advance, due to the special 160
bonded and tax rebate policies of the Chinese govern ment on processing trade, the parts and raw 161
materials they import are duty -free, and the amount of capital they need to pay in advance is less 162
than that of general trade. It can be seen that enterprises engaged in general trade need more 163
money investment t han those engaged in processing trade. Those that are financially constrained 164
will be more inclined to engage in processing trade. Once the financing constraints of enterprises 165
are eased, they will switch from processing trade to general trade, because the general trade has a 166
higher profit margin, its domestic value -added rate is also significantly higher than that of 167
processing trade [2,8]. 168
The next part demonstrates the second mechanism through which financing constraints mitigation 169
could promote the upg rading of value chain. Ju and Yu [14] research shows that those enterprises 170
in the upstream of the value chain have stronger production capacity and profitability, and higher 171
domestic added value. At the same time, the capital intensity of enterprises is a lso higher. The 172
research of Chen [15] also shows that in China, most enterprises at the lower value chain position 173
are engaged in processing trade such as assembly. Therefore, if enterprises could rise to the 174
upstream of the value chain and produce more in termediate goods, it is possible to upgrade the 175
value chain. Compared with downstream assembly, if enterprises want to climb to the upstream 176
and engage in the production of intermediate products, they need to buy more machinery and 177
equipment to organize pr oduction, and accordingly the demand for capital increases. Therefore, the 178
easing of corporate finance could also prompt a shift from downstream assembly to upstream parts 179
producers. 180
Based on the research of Antras et al. [16], an index called upstream ind ex is constructed to measure 181
the specific position of enterprises in the value chain. This index represents the distance between 182
the production links engaged in by enterprises and the final consumption. Specifically, we construct 183
the index through the foll owing methods: Firstly, the upstream index of the industry is constructed. 184
According to Leontief's input -output table, in a closed economy, the total output of an industry is 185
equal to the consumption of the final product of the industry and the intermediat e products 186
produced by other industries, which can be written as follows: 187
𝑌𝑝=𝐹𝑝+∑𝑑𝑝𝑞𝐹𝑞𝑁
𝑞=1+∑∑𝑑𝑝𝑘𝑑𝑘𝑞𝐹𝑞𝑁
𝑘=1𝑁
𝑞=1+∑∑∑𝑑𝑝𝑙𝑑𝑙𝑘𝑑𝑘𝑞𝐹𝑞𝑁
𝑙=1𝑁
𝑘=1𝑁
𝑞=1+⋯ (1)

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188
In this formula, q=1 ，2，3…… N represents the national economy, Y represents the final output of 189
the industry, F represents the final product of the industry. d_pq represents the consumption of 190
intermediate products in p industry by producing a unit of q indu stry products. 191
On this basis, Antràs et al. [16] proposed a method for calculating the average distance (upstream) 192
between output and final consumption of an industry in the value chain. They multiplied the 193
consumption of each stage in (1) by the distance between it and the final consumption, and 194
summed up the output consumption of this stage as the weight: 195
𝑈𝑝=1×𝐹𝑝
𝑌𝑝+2×∑𝑑𝑝𝑞𝐹𝑞𝑁
𝑞=1
𝑌𝑝+3×∑∑𝑑𝑝𝑘𝑑𝑘𝑞𝐹𝑞𝑁
𝑘=1𝑁
𝑞=1
𝑌𝑝+4
×∑∑∑𝑑𝑝𝑙𝑑𝑙𝑘𝑑𝑘𝑞𝐹𝑞𝑁
𝑙=1𝑁
𝑘=1𝑁
𝑞=1
𝑌𝑝⋯ (2)
In this formula,U_prepresents the average distance between the industry and final consumption; 196
obviously U_p≥1, and only if the industry's output is all final consumption, U_p=1. If U_pis larger, 197
the output of the industry is mainly intermediate goods, far from final consumption; if U_p is 198
smaller, the industry's output is closer to the final consumer. 199
Considering imports and exports in open economies, and taking inventory into account, d_ijis 200
updated as (3); By substituting (3) into (2), the upstream degree of domestic indus try under open 201
economy can be obtained. 202
Based on the calculating the upstream degree of the industry, Chor et al. [17], Ju and Yu et al. [14] 203
measured the upstream degree index of enterprises' integration into GVC through export to reflect 204
the embedding po sition of an enterprise in GVC. Specifically, the upstream index of each enterprise 205
is obtained by mapping the upstream degree of the industry to a single enterprise with the export 206
of each enterprise's sub -industry as the weight to measure the embedded po sition of the 207
enterprise's global value chain. Since the industries in WIOD input -output table [18] are classified 208
according to ISIC4.0 standard, while the import and export products in Chinese customs database 209
are classified according to HS8 standard code , this paper matched the two schemes according to the 210
HS Combined to ISIC rev3 schemes provided by WITS and the ISIC rev.3 – ISIC rev3.1, ISIC rev3.1 211
– ISIC rev.4 schemes provided by the United Nations Statistics Division. After the industry 212
matching is completed, the upstream degree of the enterprise can be expressed as the weighted 213
average upstream degree of the export products of different industries 214
𝑒𝑥𝑠𝑡𝑟𝑒𝑎𝑚 𝑖𝑡=1
𝑒𝑥𝑝𝑜𝑟𝑡 𝑖𝑡×∑𝑈𝑗𝑡×𝑒𝑥𝑝𝑜𝑟𝑡 𝑖𝑗𝑡𝑁
𝑗=1 (4)
In the formula, 〖exstream 〗_itrepresents the upstream index of enterprise i embedded in global 215
value chain through exports in year t, 〖export〗_it represents the total exports of enterprise i in 216
year t, 〖export〗_ijt represents the import volume of enterpris e i in the jth industry in year t. U_jt 217
represents the upstream index of the jth industry in t year. Through this indicator, we’d like to 218
verify whether the mitigation of financing constraints promotes enterprises to the upstream 219
position of the value chai n. 220 𝑑𝑝𝑞̂=𝑑𝑝𝑞𝑌𝑝
𝑌𝑝−𝑋𝑝+𝑀𝑝 (3)

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221
3. Econometric Models, Variables and Data Description 222
3.1 Model Specifications 223
In order to verify the above hypothesis, based on the research of Upward et al. [6], we use the 224
following econometric models to estimate the impact of financing constraints on firm's position in 225
global value chain: 226
〖 DVAR〗_ijkt=β_0+β_1 〖fin〗_ijkt+β_2 X_ijkt+ γ_j+δ_k+τ_t+ε_ijkt (5) 227
In the formula, i represents enterprises; j represents the industry in which the enterprise is located; 228
k represents the area where the enterprise is located; t represents the year; domestic value added 229
rate of exports 〖DVAR〗_ijkt is adopted to represent the position of enterprises in global value 230
chain; 〖Fin〗_ijkt is used to represent firm ’s financing constraint; β_1 pres ents the impact of 231
financing constraints on the domestic value -added rate of enterprises. X_ijkt is a control variable, 232
including enterprise productivity, size, age, capital intensity and industry -level variables such as 233
industry competitiveness and utiliz ation rate of foreign capital. γ_j, δ_k, τ_t represent the fixed 234
effects of industry, region and time, respectively, ε_ijkt is a random error term. 235
3.2 Variable Construction 236
3.2.1 Domestic Value -Added Exports (DVAR) 237
According to Kee and Tang’s research, domestic value added ratio (DVAR) at the enterprise level 238
is used to measure the status of global value chain. 239
Firstly, an accounting equation is introduced. 240
〖 PY〗_i=π_i+〖wL〗_i+〖rK〗_i+P^D M_i^D+P^I M_i^I (6) 241
In Formula (6), output value of enterprises ( 〖PY〗_i) is composed of enterprise profit ( π_i), wages 242
(〖wL〗_i), capital expenditure ( 〖rK〗_i), domestic purchase materials (P^D M_i^D) and 243
imported materials from abroad (P^I M_i^I). Domestic materials purchased may contain foreign 244
ingredients, which are marked as δ_i^F; Foreign imported materials may also contain domestic 245
ingredients, which are marked as δ_i^D; At the same time, the national ingredients contained in 246
domestic materials are marked as q_i ^D; The whole country's foreign ingredients contained in 247
foreign materials are marked as q_i^F; Therefore, domestic purchases of materials P^D M_i^D, 248
imported materials from abroadP^I M_i^I can be written as follows: 249
P^D M_i^D=δ_i^F+q_i^D 250
P^I M_i^I=δ_i^D+q _i^F 251
Similar to the method used to measure a country's gross domestic product, we interpret the value 252
added of a firm as the total domestic value contained in the firm's total output. The domestic added 253
value of the enterprise includes the profit of the en terprise(π_i), wages ( 〖wL〗_i), capital 254
expenditure( 〖rK〗_i), direct or indirect domestic materials; As shown in Formula (7): 255
〖DVA〗_i=π_i+〖wL〗_i+〖rK〗_i+q_i^D+ δ_i^D (7) 256
In China, it can be said that processing trade accounts for a consid erable proportion of exports. In 257
view of the great differences between the production modes of processing trade and general trade, 258

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the domestic value -added rate (DVAR) is calculated for processing trade enterprises and general 259
trade enterprises respectivel y. 260
Because the processing trade enterprises export all the products they produce and consume all the 261
imported products, their total export value is equal to the gross product. That is, 〖EXP〗_i= 262
〖PY〗_i; All imported foreign materials are also used in the pro duction of export products, 263
namely: 〖IMP〗_i=P^I M_i^I; This means that for processing trade enterprises, formula (8) can be 264
derived from (6), as follows: 265
〖EXP〗_i=π_i+〖wL〗_i+〖rK〗_i+P^D M_i^D+P^I M_i^I = π_i+〖wL〗_i+〖rK〗 266
_i+δ_i^F+q_i^D+ δ_i^D+q_i^F (8) 267
According to Formula (7), Formula (8) can be transformed into: 268
〖EXP〗_i=〖DVA〗_i+〖IMP〗_i-δ_i^D+δ_i^F 269
Then 270
〖 DVA〗_i=〖EXP〗_i-〖IMP〗_i+δ_i^D -δ_i^F (9) 271
In this case, based on studies of Koopman et al. [2], δ_i^D trend to 0. This means that the domestic 272
added value of the export of processing trade enterprises is the total amount of exports minus the 273
total amount of imports, and then the foreign components δ_i^F contained in domestic materials 274
are adjusted. 275
Based on the obtain ed domestic added value 〖DVA〗_i, we further calculate the rate of domestic 276
added value 〖DVAR〗_i of processing trade enterprises: 277
〖DVAR〗_i=〖DVA〗_i/〖EXP〗_i =(〖EXP〗_i-〖IMP〗_i+δ_i^D -δ_i^F )/〖EXP〗_i =1 – 278
〖IMP〗_i/〖EXP〗_i – (δ_i^F)/〖EXP〗_i (10 ) 279
However, there is no data (δ_i^F)/ 〖EXP〗_i on the proportion of foreign components in domestic 280
materials at the enterprise level. By referring to the calculation of Kee and Tang [1], this paper 281
matches the industry codes of the first two columns in China industrial enterprise database, and 282
finally obtains the proportion of foreign components in domestic materials of each industry((δ_i^F)/ 283
〖EXP〗_i ). The data is then applied to the corresponding enterprises in the industry. 284
For general trading enterprises, one part of their output is for export and the other part is for 285
domestic sales. In the imported materials, the general trade enterprises will also use part of the 286
materials to produce domestic products. Therefore, unlike the processing trade enterprises, the 287
output and imported materials of the general trade enterprises will leak out to the domestic market. 288
In order to calculate the general trade enterprises 〖DVAR〗_i, according to the assumption of Kee 289
and Tang [1], the proportion of imports consumed in a firm's exports is proportional to its exports 290
divided by total output; This means that the value 〖DVAR〗_i of products manufactured by 291
enterprises is the same whether for export or domestic sales. According to hypothetical conditions, 292
the domestic added va lue of general trading enterprises 〖DVA〗_i^o is: 293
〖 DVA〗_i^o=〖EXP〗_i-（〖 IMP〗_i+δ_i^F）*（〖 EXP〗_i/〖PY〗_i ） 294
Based on the domestic added value obtained 〖DVA〗_i^o, we further calculate the domestic 295
value -added rate of general trading enterprises 〖DVAR〗_i^O： 296

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〖DVAR〗_i^O=(〖DVA〗_i^o)/〖EXP〗_i =1 – (〖IMP〗_i+δ_i^F)/〖PY〗_i 297
(11) 298
Although the domestic value -added rates of processing trade enterprises and general trade 299
enterprises are calculated respectively, there are still some enterprises en gaged in both processing 300
trade and general trade, which we call mixed trade enterprises. According to the export share of 301
processing trade and the export share of general trade, this paper attempts to weighted average the 302
rate of domestic added value of pr ocessing trade and general trade to calculate the rate of domestic 303
added value of mixed trade enterprises. 304
3.2.2 Financing Constraints 305
There has been a debate in academic about how to accurately measure the financing constraints 306
faced by enterprises. This paper uses three methods to measure the financing constraints faced by 307
enterprises. They are: Comprehensive index scoring method, SA index, single financial index 308
interest expenditure. The construction of these three indicators is explained separately as f ollows: 309
Firstly, on the comprehensive index scoring method: Through referring to studies of Bellone et al. 310
[19]; This paper comprehensively measures the financing ability of enterprises from the 311
perspectives of external financing ability, commercial credit and profitability, and finally selects 7 312
sub-indexes, which are: ①Enterprise size (logarithmic value of total assets scale); ②The ratio of net 313
tangible assets (the proportion of total fixed assets to total assets); ③The liquidation ratio (owner's 314
equity d ivided by total liabilities); ④ Liquidity (current assets divided by current liabilities) ；⑤ 315
Commercial credit (accounts receivable divided by total assets); ⑥Net interest rate of assets (after 316
interest and tax income divided by total assets); ⑦ Net interes t rate on sales (income after interest 317
and tax divided by sales revenue).On the basis of the 7 sub -indexes of each enterprise, we score 318
according to the ranking of each enterprise index in all enterprises. The higher the value is, the 319
stronger the financin g ability of the enterprise is, and the higher the score is correspondingly. 320
Specific methods are as follows: the values of seven sub -indicators are sorted according to the order 321
from small to large, and divided into five intervals (0% -20%], (20% -40%], (40 %-60%], (60% -80%], 322
(80% -100%]. They are assigned 1 -5 points respectively. After calculating the scores of seven sub – 323
indicators, we sum them up. Finally, we standardize the total scores to (0,1) interval and mark them 324
as fin 1. The larger the value, the str onger the financing capacity of enterprises and the smaller the 325
financing constraints they face, the more beneficial it will be to promote the upgrading of the global 326
value chain of enterprises. Therefore, we expect the return of this index, the regression coefficient, 327
to be positive. 328
Secondly, according to the research of Hadlock and Pierce [20], SA index excludes all financial 329
indicators that influence each other, and only uses exogenous enterprise size and age variables to 330
estimate the calculation formula of SA index according to the ordered probit model. The higher the 331
value of SA index, the greater the financing constraint on enterprises is. In order to be consistent 332
with other indicators of financing constraints in the regression results, this paper takes the negative 333
number fin2 o f SA index in the regression analysis. The larger the value of fin2 is, the smaller the 334
financing constraints will be, which will be conducive to the improvement of enterprises' GVC 335
status, and the expected coefficient is also positive. 336
Finally, the intere st expense ratio of a single financial indicator (denoted as fin3) is adopted to 337
measure financing constraints. Feenstra et al. [21] studied the financing constraints on enterprises 338
caused by banks' inability to issue optimal loans due to asymmetric inform ation. Since there is no 339
loan data obtained from banks in the financial statements, they use interest expense as an indicator 340
to measure financing constraints. In China, only a small number of enterprises are listed and 341
financed through the securities mark et. The majority of enterprises obtain external financing 342
through bank loans. Moreover, due to financial repression, there is "ownership discrimination" in 343

Sustainabili ty 2019, 11, x FOR PEER REVIEW 9 of 18
the loans available to enterprises. The higher the interest expense is, the more bank loans the 344
enterprise actually obtains and the less financing constraints it is subject to. This paper divides 345
interest expenditure by fixed assets to measure the financing constraints of enterprises. The larger 346
the fin 3 value, the smaller the financing constraints face d by enterprises, the higher the global 347
value chain will be, and the coefficient is also expected to be positive. 348
3.2.3 Other Control Variables 349
In addition to the core variable financial constraint, based on Upward et al. [6], we have also 350
controlled other enterprise characteristic variables and some industry variables, including: total 351
factor productivity (TFP). We used the LP method to calculate enterprise productivity from 2005 to 352
2007. Because the data of Chinese industrial enterprises in 2008 and 2009 have no information on 353
intermediate input and industrial added value, the total factor productivity in these two years is 354
estimated by Solow residual method with fixed effect. The size of the enterprise is measured by the 355
logarithm number of the workers em ployed by the enterprise. Enterprise age (age) refers to the 356
length of time the enterprise has been established. Capital intensity, or intensity, measures the 357
number of fixed assets per capita. FDI inflow (FDI), which is calculated by the annual FDI 358
inflow /GDP of each province, may also have an impact on domestic added value. After FDI flows 359
into China, in order to protect technology and seize the market, foreign -funded enterprises organize 360
production by cooperation with leading companies in manufacturing k ey components, which will 361
enhance the domestic added value of exports. On the other hand, many enterprises in coastal areas 362
are actually engaged in processing trade by using cheap Chinese labor force. This in turn will 363
reduce the domestic value added of pr oducts, so the role of FDI is not clear. Industry concentration 364
(hhi), which is calculated by the sum of the squares of the percentage of enterprise sales in the total 365
sales of the industry, represents the industry concentration and also affects the added value that 366
enterprises may obtain. 367
3.3 Data Sources and Data Processing 368
The data used in this paper are micro firm -level production data compiled by China’s National 369
Bureau of Statistics(NBS) and import and export trade data collected by China’s General 370
Administration of Customs. The time span is 2005 -2009. Data of China's industrial enterprises are 371
collected from all state -owned enterprises and non -state -owned enterprises with "above scale" 372
(main business income of more than 5 million yuan). This set of da ta contains basic information and 373
financial information of enterprises. In order to improve data quality, referring to Cai and Liu [22] 374
and Feenstra et al. [21] research methods, we excluded those samples that are absent of important 375
variables such as tot al assets, gross industrial output value, net fixed assets and samples of 376
employees under 10. At the same time, the following samples that do not conform to general 377
accounting standards are excluded: current assets exceed total assets, fixed assets exceed total 378
assets, and current depreciation exceeds accumulated depreciation. We also eliminated companies 379
with invalid founding dates, unknown regions and industries. 380
Although the Chinese industrial enterprise database provides a wealth of information at the 381
enterprise level, the trade statistics are very rough. It is necessary to match the Chinese customs 382
import and export trade data with the Chinese industrial enterprise database. China Customs 383
Import and Export Trade Database mainly provides import and expor t data at the product level. 384
Since the data are based on monthly data, we first sum up the monthly data into annual data. 385
Although both databases contain the same variable, enterprise code, the coding systems are 386
different and cannot be directly matched to enterprise code. By using the methods of Upward et 387
al.[6] and Yu [23] for reference, we matched the database of Chinese industrial enterprises with the 388
database of customs import and export enterprises in three steps: enterprise name, enterprise 389
postcode plus telephone number, enterprise postcode plus contact person. The complete database 390

Sustainabili ty 2019, 11, x FOR PEER REVIEW 10 of 18
includes the basic financial status of the enterprise, import and export volume, trade mode and 391
other indicators. 392
393
4. Regression Results and Analysis 394
This section takes t he domestic value -added rate of enterprises' exports as the explained variable to 395
investigate the impact of financing constraints on enterprises' GVC status. In addition, we also test 396
the robustness of eliminating outliers, considering financial crisis and distinguishing ownership 397
types, and further examine the mechanism how financing constraints relief will impact on the 398
upgrading of enterprises' global value chain. 399
Benchmark regression results 400
Table 1 is the result of benchmark regression, (1) – (3) col umn control time fixed effect, (4) – (6) 401
column control time, industry and province fixed effect. The regression results (1) show that the 402
fin1 coefficient is positive and significant at the 1% level, indicating that the easing of financing 403
constraints sig nificantly improves the domestic value added rate of enterprises, that is, the 404
enhancement of financing ability can promote the upgrading of enterprises' global value chain. 405
Regression (2) shows that the coefficient of the comprehensive index SA (fin2) of financing 406
constraints is also positive and significant at the level of 1%. According to the construction method 407
of SA index, the higher the value is, the stronger the financing constraint is. Because we take the 408
opposite number of SA index, it means that t he larger the opposite number of SA index (fin2), the 409
smaller the financing constraints on behalf of enterprises. From the regression results, we can see 410
that with the increase of fin2, the domestic value -added rate of enterprises is increasing, that is, t he 411
relief of financing constraints can promote the enterprise value chain. The results of regression (3) 412
show that the coefficient of interest expense ratio (fin3) is also positive and significant at the level of 413
1%, indicating that with the increase of in terest expense of enterprises, namely the enhancement of 414
financing capacity, the domestic added value ratio of enterprises significantly increases. Regression 415
results (4) to (6) control the fixed effect of time and fixed effect of industry and region, and get 416
similar results. 417
Table 1． Benchmark regression results 418
(1) (2) (3) (4) (5) (6)
dvar dvar dvar dvar dvar dvar
fin1 0.1281*** 0.0928***
(0.0048) (0.0047)
fin2 0.6972*** 0.3851***
(0.0183) (0.0178)
fin3 0.0516*** 0.0395***
(0.0024) (0.0024)
tfp 0.0081*** 0.0010** 0.0062*** 0.0050*** 0.0003 0.0038***
(0.0005) (0.0004) (0.0005) (0.0004) (0.0004) (0.0005)
size -0.0128*** -0.0109*** -0.0119*** -0.0079*** -0.0067*** -0.0073***
(0.0004) (0.0003) (0.0004) (0.0004) (0.0003) (0.0003)
age 0.0074*** 0.0015 0.0073*** 0.0047*** 0.0009 0.0049***
(0.0013) (0.0013) (0.0013) (0.0012) (0.0012) (0.0012)
intensity 0.0069*** 0.0028*** 0.0056*** 0.0084*** 0.0050*** 0.0075***
(0.0003) (0.0003) (0.0003) (0.0004) (0.0003) (0.0003)

Sustainabili ty 2019, 11, x FOR PEER REVIEW 11 of 18
fdi -0.738*** -0.719*** -0.741*** 0.0949*** 0.0926*** 0.0971***
(0.0146) (0.0147) (0.0146) (0.0261) (0.0262) (0.0261)
hhi 0.2063*** 0.1716*** 0.1813*** 0.9427 0.8201 0.9039
(0.0582) (0.0579) (0.0582) (0.6315) (0.6306) (0.6318)
consant 0.8238*** 0.9715*** 0.8958*** 0.8822*** 0.9843*** 0.9309***
(0.0083) (0.0065) (0.0072) (0.0376) (0.0372) (0.0374)
Year dummy
Ind dummy
Pro dummy Yes
No
No Yes
No
No Yes
No
No Yes
Yes
Yes Yes
Yes
Yes Yes
Yes
Yes
𝑅2 0.0372 0.0438 0.0354 0.1255 0.1271 0.1248
N 128876 127928 128879 128876 127928 128879
Note: *, ** and *** are significant at 10%, 5% and 1% levels, respectively. The brackets are the robust 419
standard error of coefficients. 420
From the perspective of other control variables, total factor product ivity and enterprise capital 421
intensity have a significant positive impact on domestic value -added rate, indicating that 422
enterprises with high total factor productivity and high capital intensity have higher domestic 423
value -added rate. At the same time, the impact of enterprise age is positive, but the impact of 424
enterprise size is negative. In addition, the level of FDI entry and the degree of industry monopoly 425
have uncertain impacts on the global value chain of enterprises. 426
427
4.2 Robustness Test 428
4.2.1 Exclude outliers 429
In view of the possible influence of extreme values on the estimated results, the values of the first 430
5% and the last 5% of samples were removed for further regression analysis. Columns (1) – (3) of 431
table 2 show the results after ou tliers are removed. The results are roughly the same as those in 432
table 1. The three index coefficients to measure the financing capacity of enterprises are still positive 433
and significant at the level of 1%. 434
4.2.2 Impact of financial crisis 435
The subprime mor tgage crisis broke out in 2008 had a huge impact on China's exports, and China's 436
participation in the global value chain dropped sharply. Considering the possible interference of 437
such shocks on the regression results, this paper excludes the sample size of 2008 and 2009, and 438
only regression of pre -crisis samples is made. The regression results are listed in Table 2 (4) – (6). It 439
can be found that the mitigation of financing constraints still significantly promotes the increase of 440
domestic value -added rate o f enterprises, and this effect is established at the level of 1% 441
significance. 442
Table 2． Robustness test 1 443
444
(1) (2) (3) (4) (5) (6)
dvar dvar dvar dvar dvar dvar
fin1 0.0710*** 0.0910***

Sustainabili ty 2019, 11, x FOR PEER REVIEW 12 of 18
(0.0037) (0.0060)
fin2 0.2734*** 0.460***
(0.0137) (0.0256)
fin3 0.0319*** 0.0373***
(0.0018) (0.0032)
tfp 0.0048*** 0.0012*** 0.0040*** 0.0062*** 0.0009* 0.0047***
(0.0004) (0.0003) (0.0004) (0.0007) (0.0005) (0.0006)
size -0.0068*** -0.0059*** -0.0064*** -0.0127*** -0.0105*** -0.0118***
(0.0003) (0.0003) (0.0003) (0.0005) (0.0004) (0.0005)
age 0.0034*** 0.0005 0.0037*** 0.0023 -0.0018 0.0021
(0.0009) (0.0010) (0.0009) (0.0017) (0.0016) (0.0017)
intensity 0.0063*** 0.0037*** 0.0058*** 0.0114*** 0.0084*** 0.0103***
(0.0003) (0.0002) (0.0003) (0.0004) (0.0004) (0.0004)
fdi 0.0203 0.0199 0.0219 0.0183 0.0166 0.0202
(0.0200) (0.0200) (0.0200) (0.0363) (0.0362) (0.0363)
hhi 0.7861* 0.6912 0.7595 0.6959 0.4062 0.6091
(0.4617) (0.4611) (0.4619) (1.0271) (1.0263) (1.0272)
constant 0.887*** 0.965*** 0.922*** 0.967*** 1.078*** 1.019***
(0.0276) (0.0273) (0.0274) (0.0606) (0.0603) (0.0605)
Yea dummy
Ind dummy
Pro dummy Yes
Yes
Yes Yes
Yes
Yes Yes
Yes
Yes Yes
Yes
Yes Yes
Yes
Yes Yes
Yes
Yes
𝑅2 0.1137 0.1149 0.1132 0.1426 0.1436 0.1415
N 116240 115357 116243 76254 76256 76256
Note: *, ** and *** are significant at 10%, 5% and 1% levels, respectively. The brackets are the robust 445
standard error of coeffic ients. 446
447
4.2.3 Endogenous problems 448
There may be endogenous problems between financing constraints and domestic value added . On 449
the one hand, the easing of financing constraints will promote the position of enterprise value 450
chain. On the other hand, if ent erprises have upgraded the value chain, from processing and 451
assembly to the upstream of value chain, creating more domestic value -added and profits, the 452
enterprises’ internal cash flow will increase and financing constraints could be mitigated. At the 453
same time, enterprises with upgraded value chains are more likely to signal high -quality 454
enterprises to banks, and they may be more likely to obtain financial support from banks, thus 455
alleviating financing constraints. In order to exclude this endogenous probl em, we used the first – 456
order lagged variable of financing constraint as the instrumental variable for regression analysis, 457
and the regression results are shown in (1) – (3) in table 3. It can be seen that easing of corporate 458
financing constraints measured b y three different variables can significantly upgrade the global 459
value chain after considering endogenous problems. In addition, Kleibergen -Paap rk LM statistic p 460
value is less than 0.01, strongly rejecting the original hypothesis of non -identifiability, i ndicating 461
that tool variables and interpretation variables are related. The Kleibergen -Paap Wald rk F test 462
shows that there is no weak tool variable. These tests show that our tool variables are reasonable 463
and effective, and the estimated results are relia ble. 464

Sustainabili ty 2019, 11, x FOR PEER REVIEW 13 of 18
465
466
467
Table 3． Robustness test 2 468
(1) (2) (3)
dvar dvar dvar
fin1 0.0614***
(0.0115)
fin2 0.930***
(0.0845)
fin3 0.0240***
(0.0056)
tfp 0.0042*** 0.0004 0.0032***
(0.0009) (0.0007) (0.0008)
size -0.0086*** -0.0085*** -0.0082***
(0.0006) (0.0006) (0.0006)
age 0.0051*** 0.0014 0.0050***
(0.0017) (0.0017) (0.0017)
intensity 0.0064*** 0.0044*** 0.0056***
(0.0007) (0.0005) (0.0007)
fdi 0.0658 0.0514 0.0679
(0.0458) (0.0464) (0.0458)
hhi 1.5452** 1.2580* 1.5347**
(0.7139) (0.7371) (0.7022)
constant 0.8826*** 0.9655*** 0.9170***
(0.0578) (0.0621) (0.0563)
Kleibergn -Pa
LM
Kleibergen -Paap
Wald F 9780
[0.000]

16000
[0.000] 197.07
[0.000]

31.823
[0.000] 9453
[0.000]

18000
[0.000]
Yea dummy
Ind dummy
Pro dummy Yes
Yes
Yes Yes
Yes
Yes Yes
Yes
Yes
𝑅2 0.115 0.110 0.115
N 53736 52902 53738
Note: *, ** and *** are significant at 10%, 5% and 1% levels, respectively. The brackets are the robust 469
standard error of coefficients. 470
4.3 Expansion Analysis: Heterogeneity of Enterprise Ownership 471
In C hina, enterprises of different ownership types have different production modes, and the rate of 472
domestic added value of their exports may be significantly different. Next, the group regression is 473
carried out for state -owned enterprises(SOEs), private -owned enterprises(POEs) and foreign -owned 474

Sustainabili ty 2019, 11, x FOR PEER REVIEW 14 of 18
enterprises(FOEs) respectively. According to Chinese law, we define enterprises with over 25% of 475
paid -in capital from Hong Kong, Macao and Taiwan as foreign -funded enterprises, and enterprises 476
with over 50% of state -own ed capital as state -owned enterprises. The regression results are shown 477
in Table 3. Among them, (1) – (3) are the regression results for state -owned enterprises, (4) – (6) and 478
(7) – (8) are the regression results for private enterprises and foreign enterpr ises, respectively. It can 479
be seen that financing constraints have no impact on the upgrading of the value chain of state – 480
owned enterprises, but the mitigation of financing constraints can significantly promote the 481
upgrading of the value chain of private e nterprises and foreign -funded enterprises in China. 482
Generally speaking, the impact on the upgrading of private enterprises is stronger. The possible 483
reason is that Chinese banks have ownership discrimination in credit rationing, and they tend to 484
give prior ity to loans for state -owned enterprises. Therefore, China's state -owned enterprises 485
generally do not have financing difficulties, and the financing problem may not be a constraint to 486
their value chain upgrading. On the contrary, many private enterprises a re faced with the problem 487
of difficult and expensive financing, which becomes an important factor restricting their upgrading 488
of the value chain. In other words, the easing of financing constraints will significantly promote 489
their rise to upstream of the v alue chain. The coefficient of the impact of financing on the value 490
chain upgrading of foreign -funded enterprises is also significantly positive. 491
Table 4． Heterogeneity: Differentiation of Enterprise Ownership Types 492
state -owned enterprises private -owned enterprises foreign -owned enterprises
(1) (2) (3) (4) (5) (6) (7) (8) (9)
dvar dvar dvar dvar dvar dvar dvar dvar dvar
fin1 0.0358 0.0897*** 0.0762***
(0.0219) (0.0054) (0.0084)
fin2 -0.1249 0.1795*** 0.5899***
(0.1118) (0.0175) (0.0412)
fin3 0.0109 0.0367*** 0.0330***
(0.0114) (0.0026) (0.0044)
tfp -0.0027 -0.0042** -0.0034 0.0041*** -0.0001 0.0029*** 0.0048*** 0.0003 0.0037***
(0.0021) (0.0019) (0.0021) (0.0005) (0.0005) (0.0005) (0.0008) (0.0007) (0.0008)
size -0.0038** -0.0032** -0.0034** -0.0054*** -0.0050*** -0.0049*** -0.0099*** -0.0083*** -0.0092***
(0.0015) (0.0015) (0.0015) (0.0004) (0.0004) (0.0004) (0.0006) (0.0006) (0.0006)
age 0.0102*** 0.0101*** 0.0101*** 0.0071*** 0.0041*** 0.0072*** -0.0238*** -0.0266*** -0.0237***
(0.0039) (0.0039) (0.0039) (0.0013) (0.0013) (0.0013) (0.0026) (0.0026) (0.0026)
intensity 0.0048*** 0.0034** 0.0041** 0.0067*** 0.0035*** 0.0059*** 0.0073*** 0.0048*** 0.0066***
(0.0017) (0.0014) (0.0017) (0.0004) (0.0004) (0.0004) (0.0006) (0.0005) (0.0006)
fdi -0.1957 -0.2140 -0.1948 -0.0907*** -0.0991*** -0.0861*** 0.0910* 0.0972** 0.0924*
(0.1402) (0.1403) (0.1402) (0.0315) (0.0314) (0.0315) (0.0487) (0.0487) (0.0488)
hhi 0.1911 0.1588 0.1836 0.6917 0.6144 0.6854 1.1914 1.0957 1.0557
(1.1647) (1.1637) (1.1650) (0.7155) (0.7119) (0.7159) (2.6734) (2.6704) (2.6740)
constant 0.9878*** 1.0210*** 1.0097*** 0.8885*** 0.9866*** 0.9366*** 0.9798*** 1.0580*** 1.0254***
(0.0718) (0.0692) (0.0699) (0.0384) (0.0379) (0.0382) (0.2208) (0.2204) (0.2207)
Yeadum
Ind dum
Pro dum Yes
Yes
Yes Yes
Yes
Yes Yes
Yes
Yes Yes
Yes
Yes Yes
Yes
Yes Yes
Yes
Yes Yes
Yes
Yes Yes
Yes
Yes Yes
Yes
Yes
R2 0.120 0.119 0.119 0.131 0.131 0.130 0.113 0.115 0.112

Sustainabili ty 2019, 11, x FOR PEER REVIEW 15 of 18
N 3388 3377 3388 69290 68364 69292 56907 56885 56908
Note: *, ** and *** are significant at 10%, 5% and 1% levels, respectively. The brackets are the robust 493
standard error of coefficients. 494
495
5. the Possible Mechanism 496
Although the empirical analysis shows that the mitigation of financing constraints significantly 497
improves the domestic value -added rate of enterprises, there is no in -depth analysis of the 498
mechanism, which we will discuss next. This paper argues that there are two ways to ease financing 499
constraints and increase the domestic value added rate. First, with the ease of financing constraints, 500
enterprises will be able to shift from processing trade to general trade, which has a higher demand 501
for capital. Secondly , with the enhancement of financing ability, enterprises can shift from the 502
processing and assembly of final products to the production of intermediate products, which also 503
requires more investment. In order to test the above two mechanisms, this paper con structs the 504
following mediating effect model for testing: 505
〖share〗_ijkt=β_0+β_1 〖fin〗_it+β_2 X_ijkt+ γ_ijkt+δ_ijkt+τ_ijkt+ε_ijkt (12) 506
〖 exstream 〗_ijkt=β_0+β_1 〖fin〗_it+β_2 X_ijkt+ γ_ijkt+δ_ijkt+τ_ijkt+ε_ijkt 507
(13) 508
〖DVAR〗_ijkt=β_0+β_1 〖fin〗_it+β_2 〖〖 share〗_ijkt+β_3 〖〖 exstream 〗_ijkt+β〗_4 X〗 509
_ijkt 510
+γ_ijkt+δ_ijkt+τ_ijkt+ε_ijkt (14) 511
In the formula, 〖share〗_ijkt represents the proportion of general trade exports in total exports; 512
〖exstream 〗_ijktrepresents Upstream Degree of Enterprise in Value Chain; According to the 513
method of theoretical analysis, the regression results of econometric equations (12) and (13) are 514
shown in column (1) – (6) of table5. It can be seen that the easing of financi ng constraints, on the one 515
hand, can increase the proportion of enterprises engaged in general trade, on the other hand, 516
significantly promote enterprises to climb up in the value chain. This shows that with the ease of 517
financing constraints, enterprises w ill engage in more general trade, thereby enhance their domestic 518
value added and upgrade the global value chain. According to Ju and Yu[14], the upstream climb of 519
the value chain will also increase the profits and added value of enterprises. Table 6 (1) (3 ) and (5) 520
columns added two intermediary variables at the same time, then the domestic value -added rate 521
DVAR was regressed, (2) (4) (6) was the result of regression without intermediary variables. 522
Through comparison, it can be seen that the coefficient val ue of financing constraints decreases 523
after the addition of intermediary variables, indicating that the above two channels are the main 524
channels to alleviate financing constraints and promote enterprise value chain upgrading. 525
526
Table 5. Mechanism of Financi ng Constraint Relief Affecting the Upgrading of Value Chain 527
(1) (2) (3) (4) (5) (6)
share share share exstream exstream exstream
fin1 0.1005*** 1.0095***
(0.0129) (0.0417)
fin2 1.0668*** 1.0486***

Sustainabili ty 2019, 11, x FOR PEER REVIEW 16 of 18
(0.0483) (0.1574)
fin3 0.0453*** 0.3766***
(0.0064) (0.0208)
tfp 0.0290*** 0.0234*** 0.0279*** 0.0064 0.0560*** 0.0238***
(0.0013) (0.0011) (0.0013) (0.0042) (0.0037) (0.0040)
size -0.0378*** -0.0376*** -0.0372*** -0.0200*** -0.0371*** -0.0283***
(0.0009) (0.0009) (0.0010) (0.0031) (0.0030) (0.0031)
age 0.0111*** 0.0158*** 0.0108*** 0.0970*** 0.0612*** 0.0943***
(0.0033) (0.0033) (0.0033) (0.0108) (0.0107) (0.0108)
intensity 0.0199*** 0.0224*** 0.0206*** 0.1268*** 0.0858*** 0.1136***
(0.0010) (0.0008) (0.0009) (0.0032) (0.0027) (0.0031)
fdi 0.3734*** 0.3546*** 0.3755*** -0.6412*** -0.5992*** -0.6117***
(0.0709) (0.0709) (0.0709) (0.2298) (0.2308) (0.2299)
hhi -0.8749 -1.0262 -0.9129 -8.9578 -10.2054* -9.4722*
(1.7138) (1.7073) (1.7138) (5.5554) (5.5609) (5.5608)
constant 0.8519*** 0.9758*** 0.9017*** 1.9461*** 3.0056*** 2.5383***
(0.1019) (0.1007) (0.1014) (0.3304) (0.3281) (0.3289)
Year dummy
Ind dummy
Pro dummy Yes
Yes
Yes Yes
Yes
Yes Yes
Yes
Yes Yes
Yes
Yes Yes
Yes
Yes Yes
Yes
Yes
0.2130 0.2172 0.2130 0.1778 0.1749 0.1762
N 128876 127928 128879 128876 127928 128879
Note: The data in parentheses are t values of coefficients, and *,** and *** are significant at the levels 528
of 10%, 5% and 1% respectively. 529
530
Table 6. Mechanism of Fin ancing Constraint Relief Affecting the Upgrading of Value Chain 531
(1) (2) (3) (4) (5) (6)
dvar dvar dvar dvar dvar dvar
fin1 0.0631*** 0.0928***
(0.0037) (0.0047)
fin2 0.1311*** 0.3851***
(0.0140) (0.0178)
fin3 0.0266*** 0.0395***
(0.0019) (0.0024)
share 0.2309*** 0.2310*** 0.2310***
(0.0008) (0.0008) (0.0008)
exstream 0.0065*** 0.0068*** 0.0067***
(0.0002) (0.0003) (0.0002)
tfp 0.0016*** 0.0050*** 0.0048*** 0.0003 0.0025*** 0.0038***
(0.0004) (0.0004) (0.0003) (0.0004) (0.0004) (0.0005)
size 0.0007** -0.0079*** 0.0018*** -0.0067*** 0.0011*** -0.0073***

Sustainabili ty 2019, 11, x FOR PEER REVIEW 17 of 18
(0.0003) (0.0004) (0.0003) (0.0003) (0.0002) (0.0003)
age 0.0067*** 0.0047*** 0.0042*** 0.0009 0.0067*** 0.0049***
(0.0009) (0.0012) (0.0010) (0.0012) (0.0010) (0.0012)
intensity 0.0121*** 0.0084*** 0.0096*** 0.0050*** 0.0115*** 0.0075***
(0.0003) (0.0004) (0.0002) (0.0003) (0.0003) (0.0003)
fdi 0.0128 0.0949*** 0.0147 0.0926*** 0.0144 0.0971***
(0.0204) (0.0261) (0.0205) (0.0262) (0.0204) (0.0261)
hhi 1.2027** 0.9427 1.1260** 0.8201 1.1771** 0.9039
(0.4938) (0.6315) (0.4936) (0.6306) (0.4940) (0.6318)
constant 0.6730*** 0.8822*** 0.7386*** 0.9843*** 0.7059*** 0.9309***
(0.0294) (0.0376) (0.0291) (0.0372) (0.0292) (0.0374)
Year dummy
Ind dummy
Pro dummy Yes
Yes
Yes Yes
Yes
Yes Yes
Yes
Yes Yes
Yes
Yes Yes
Yes
Yes Yes
Yes
Yes
0.4653 0.1255 0.4651 0.1271 0.4650 0.1248
N 128876 128876 127928 127928 128879 128879
Note: The data in parentheses are t values of coefficients, and *,** and *** are significant at the levels 532
of 10%, 5% and 1% respectively. 533
534
6. Conclusion 535
This paper aims to examine the r elationship between financing constraints and value chain 536
upgrading and its mechanism. Different from previous literatures, which calculated the domestic 537
added value at the national or industrial level, this paper adopted the import and export trade 538
databa se of Chinese industrial enterprises and China customs, fully considered the heterogeneity of 539
enterprises, distinguished processing trade enterprises from general trade enterprises, and 540
calculated the domestic added value rate based on the enterprise level . On the index construction of 541
financing constraints, this paper uses three methods: comprehensive index scoring method, SA 542
index and single financial index to comprehensively measure the financing constraints faced by 543
enterprises. Based on benchmark regre ssion, the robustness test is carried out from three aspects: 544
excluding outliers, considering financial crisis and endogenous problems. The study finds that the 545
mitigation of financing constraints can significantly promote the status of Chinese enterprises in the 546
global value chain. Given that Chinese Banks have "ownership" discrimination in credit, that is, 547
banks tend to give priority to loans to state -owned enterprises, and private enterprises often have 548
financing difficulties and high financing costs, we divide all samples into state -owned enterprises, 549
private enterprises and foreign -funded enterprises for regression. The results show that financing 550
constraints have no effect on state -owned enterprises, but financing difficulties have become an 551
important factor restricting the upgrading of private enterprises’ positions in value chain. In other 552
words, the mitigation of financing constraints will significantly promote Chinese private enterprises 553
to climb up in the value chain. Then, what is the mechanism of financing constraint affecting the 554
value chain upgrading of Chinese enterprises? This paper examines this through the intermediary 555
effect. The study finds that the increase of financing capacity of enterprises can shift the enterprises 556
from processing tra de to general trade which demands more capital. On the other hand, It also help 557
enterprises shift from simple processing and assembly to intermediate product production, which 558
requires more capital to purchase the corresponding machinery and equipment for production. It 559
can be said that the transformation from processing trade to general trade and from processing and 560

Sustainabili ty 2019, 11, x FOR PEER REVIEW 18 of 18
assembly to intermediate goods production both improve the profit margin and domestic added 561
value rate of enterprises, thus promoting China's position in the global value chain. 562
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