4. Baseline Results and Robustness Checks
4.4. Robustness Checks
We further check the robustness of our baseline results above in this subsection. We shall show that a more rigorous proxy for endogenous firm-level financial credits that captures relatively exogenous variation in these variables does not change our main findings. We follow the idea of Manova et al. (2015) to proxy firm-level internal and external finance with sectoral counterparts. We expand it to province-sector-level proxies to generate more reasonable variation in finance, which can then be employed to identify the augmented treatment effect from firms’ financial credits.
Tables 11-12tabulate the panel data difference-in-differences estimates for ex-port value, comparable to Tables 4-5. It is revealed that proxy firm-level finance using province-sector level counterparts marginally change our baseline results.
The statistical significance keeps unchanged and economic magnitude is just s-lightly changed. We still have the conclusion that on average the encouraging effect of finance on firms’ export value increases when a firm switches its export-ing mode, and the results are robust to includexport-ing more controls like age and size, and instrument the potentially endogenous treatment with the product of firm’s initial productivity and provincial capital supply shock. Tables13-14confirm the robustness of our baseline results in Tables6-7for firms’ productivity, i.e. TFPR.
They exhibit marginally changed estimates, and the main conclusion we drew pre-viously still holds, that the encouraging effect of finance on productivity is higher when the firms engage into the switch from indirect to direct exporting. Moreover, it still shows an incomplete pass-through from gains in export value to gains in productivity.
In Tables15-16, we present the panel data difference-in-difference-in-differences estimates using province-sector-level proxies for firm-level financial credits. The results are highly comparable to those in Tables 8-9, with only a sensible change in magnitudes. It reinforces our baseline finding that the increased encouraging effect of finance on firm’s export value and productivity is higher when the firm switches from indirect into direct exporter and if the firm is observed in the WTO accession period. The results are robust to how we separate pre- and post-WTO accession periods. It also underscores a declining difference-in-difference-in-differences estimate when we choose a later cutoff year to separate pre- and post-WTO accession periods, which essentially reflects the phase-in feature of China’s deregulation on directing exporting rights.
5. Conclusions
This paper examines the time-varying feature of the impacts of finance on firm exporting behaviors when a firm switches from indirect to direct exporting mode in the context of China’s WTO accession. To fulfill WTO accession commitments, China gradually lifted the restriction in direct exporting rights over the period 2001-2004. It is noticeable that the regulation on exporting modes primarily in-hibited PDEs from exporting directly while more SOEs were exempted, as their registered capital requirements were quite different. Direct exporters feature more favorable future outcomes, e.g. productivity and demand stock growth (Bai et al., 2017). Using panel survey data, we show that financial credits improve the firm-level exports and productivity more for firms that switch from indirect to direct exporting. Knowing that PDE firms were typically credit constrained, we
conjec-ture that the impact of financial credits on firm exports when the firm switches from indirect to direct exporting mode would be larger after China’s WTO ac-cession. This is because that many more PDEs were granted the opportunity to engage in direct exporting and the direct exporting typically incurs massive ad-ditional fixed/variable costs cost as well as subsequent investment in upgrading technology.
Using a panel data difference-in-difference-in-differences approach combined with instrumental variable methods to control for potential endogeneity issues associated with the switch in exporting modes, we find strong evidence to sub-stantiate our time-varying hypothesis. The difference-in-difference-in-differences estimation produces a further increase in the role of finance in promoting firms’
export value in the post-WTO accession period. The main results remain un-changed after controlling for possible endogeneities.
Though we are focusing on the time-varying impact of finance on firm perfor-mance, our work has strong implications in two dimensions. First, we show the heterogeneous impact of financial credits on different firms. We demonstrate that finance could make a pivotal contribution to firm-level exports and productivity growth when firms have a higher efficiency in finance usage. Second, our study im-plies additional welfare gains of trade liberalization. Our empirical findings suggest that when distortions exist, trade liberalization is an effective way to eliminate the distortion. Further, the elimination of distortions makes financial markets play a more pronounced role in improving firm-level exports, which results in additional welfare gains as export share further increases.
Reference
[1] Ackerberg, D. A., K. Caves and G. Frazer 2015. “Identification Properties of Recent Pro-duction Function Estimators.” Econometrica,83, 2411-51.
[2] Ahn, J., A. K. Khandelwal, and S. J. Wei 2011. “The Role of Intermediaries in Facilitating Trade.”Journal of International Economics,84, 73-85.
[3] Amiti, M. and D. Weinstein 2011. “Exports and Financial Shocks.” Quarterly Journal of Economics,126 (4), 1841-77.
[4] Arkolakis, C. 2011. “Market Penetration Costs and the New Consumers Margin in Interna-tional Trade.” Journal of Political Economy,118 (6): 1151-1199.
[5] Arkolakis, C., A. Costinot, and A. Rodriguez-Clare 2012. “New Trade Models, Same Old Gains?” American Economic Review,102 (1), 94-130.
[6] Alvarez, R. and R.A. L´opez 2005. “Exporting and Performance: Evidence from Chilean Plants.” Canadian Journal of Economics,38 (4), 1384-1400.
[7] Aw, B.Y., S. Chung, and M. Roberts 2000. “Productivity and Turnover in the Export Market: Micro Evidence from Taiwan and South Korea.” World Bank Economic Review, 14 (1): 1-65.
[8] Aw, B.Y., Y. Xu, and M. Roberts. 2011. “R&D Investment, Exporting, and Productivity Dynamics.” American Economic Review,101 (4), 1312-1344.
[9] Bai, X., K. Krishna, and H. Ma 2017. “How You Export Matters: Export Mode, Learning and Productivity in China.” Journal of International Economics,104, 122-137.
[10] Baum, C.F., M. E. Schaffer, and S. Stillman 2007. “Enhanced Routines for Instrumental Variables / Generalized Method of Moments Estimation and Testing.” Stata Journal, 7, 465-506.
[11] Berman, N. and J. H´ericourt 2010. “Financial Factors and the Margins of Trade: Evidence from Cross-country Firm-level Data.” Journal of Development Economics,93, 206-17.
[12] Bernard, A. B., E. J. Blanchard, I. Van Beveren, and H. Y. Vandenbussche 2012. “Carry-along Trade.” NBER Working Paper, No. 18246.
[13] Bernard, A. B., I. Van Beveren, and H. Y. Vandenbussche 2010. “Multi-product Exporters, Carry-along Trade and the Margins of Trade.” National Bank of Belgium Working Paper, No. 203.
[14] Brandt, L., J. Van Biesebroeck, and Y. Zhang 2012. “Creative Accounting or Creative Destruction? Firm-level Productivity Growth in Chinese Manufacturing.” Journal of De-velopment Economics,97, 339-51.
[15] Brandt, L., J. Van Biesebroeck, L. Wang, and Y. Zhang 2017. “WTO Accession and Per-formance of Chinese Manufacturing Firms.” American Economic Review, forthcoming.
[16] Campa, J. M., and J. M., Shaver 2002. “Exporting and Capital Investment: On the Strategic Behavior of Exporters.” Iese Research Papers.
[17] Channey, T. 2016. “Liquidity Constraint Exporters.” Journal of Economic Dynamics and Control,72, 141-154.
[18] Chen, M. and A. Guariglia 2013. “Internal Financial Constraints and Firm Productivity in China: Do Liquidity and Export Behavior Make a Difference?” Journal of Comparative Economics,41 (4), 1123-40.
[19] Cragg, J. G. and S.G. Donald 1993. “Testing Identifiability and Specification in Instrumental Variables Models.” Econometric Theory,9 (2), 222-40.
[20] Dai, M., M. Maitra, and M. Yu 2016. “Unexceptional Exporter Performance in China? The Role of Processing Trade.” Journal of Development Economics,121, 177-89.
[21] Dai, M., and M. Yu 2013. “Firm R&D, Absorptive Capacity and Learning by Exporting:
Firm-Level Evidence from China.” The World Economy,36 (9), 1131-1145.
[22] De Loecker, J. 2007. “Do Exporters Generate Higher Productivity? Evidence from Slove-nia.” Journal of International Economics,73, 69-98.
[23] De Loecker, J. 2013. “Detecting Learning by Exporting.” American Economic Journal:
Microeconomics,5(5), 1-21.
[24] Deloof, M. 2003. “Does Working Capital Management Affect Profitability of Belgian Firm-s?” Journal of Business Finance &Accounting,30, 573-588.
[25] Ding, S., A. Guariglia and J. Knight 2013. “Investment and Financing Constraints in China:
Does Working Capital Management Make a Difference?” Journal of Bandking &Finance, 37(5), 1490-1507.
[26] Egan, M., and A. Mody 1992. “Buyer-Seller Links in Export Development.” World Devel-opment,20(3), 321-334.
[27] Eljelly, A. 2004. “Liquidity-Profitability Tradeoff. An Emprical Investigation in an Emerging Market.” International Journal of Commerce and Management,14(2), 48-61.
[28] Feenstra, R. C., Z. Li, and M. Yu 2014. “Exports and Credit Constraints Under Incomplete Information: Theory and Evidence from China.” Review of Economics and Statistics, 96 (4), 729-44.
[29] Guariglia, A., X. Liu, and L. Song 2011. “Internal Finance and Growth: Microeconometric Evidence on Chinese Firms.” Journal of Development Economics, 96, 79-94.
[30] Girma, S., D. Greenaway, and R. Kneller 2004. “Does Exporting Increase Productivity? A Microeconomic Analysis of Matched Firms.” Review of International Economics, 12 (5), 855-66.
[31] Greenaway, D., A. Guariglia, and R. Kneller 2007. “Financial Factors and Exporting Deci-sions.” Journal of International Economics,73, 377-95.
[32] Garcia-Marin A. and N. Voigtl¨ander 2017. “Exporting and Plant-Level Efficiency Gains:
It’s the Measure” Working Paper.
[33] Hsieh, C. T. and P. J. Klenow 2009. “Misallocation and Manufacturing TFP in China and India.” Quarterly Journal of Economics,124 (4), 1403-48.
[34] Imbens, G. W. and J. D. Angrist 1994. “Identification and Estimation of Local Average Treatment Effects.” Econometrica,62 (2), 467-475.
[35] Imbens, G. W. and J. Wooldridge 2007. “What’s New in Econometrics: Difference-in-Differences Estimation.” NBER Summer Institute Econometric Lectures.
[36] Jarreau, J. and S. Poncet 2014. “Credit Constraints, Firm Ownership and the Structure of Exports in China.” International Economics,139 (4), 152-73.
[37] Khandelwal, A., P. Schott, and S.J. Wei 2013. “Trade Liberalization and Embeded Institu-tional Reform: Evidence from Chinese Exporters.” American Economic Review, 103 (6), 2169-95.
[38] Kraay, A. 1999. “Exports and Economic Performance: Evidence from a Panel of Chinese Enterprises.” mimeo, World Bank.
[39] Lileeva, A. and D. Trefler 2010. “Improved Access to Foreign Markets Raises Plant-level ProductivityFor Some Plants.” Quarterly Journal of Economics,125 (3), 1051-1099.
[40] Levinsohn, J. and A. Petrin 2003. “Estimating Production Functions Using Inputs to Con-trol for Unobservables.” Review of Economic Studies,70, 317-41.
[41] Manova, K., S. J. Wei, and Z. Zhang 2015. “Firm Exports and Multinational Activity under Credit Constraints.” Review of Economics and Statistics, 97 (3), 574-88.
[42] Manova, K. and Z. Yu 2016. “How Firms Export: Processing vs. Ordinary Trade with Financial Frictions.” Journal of International Economics,100, 120-137.
[43] Manova, K. and Z. Zhang 2012. “Export Prices across Firms and Destinations.” Quarterly Journal of Economics,127 (1), 379-436.
[44] Melitz, M., and S. J. Redding 2015. “New Trade Models, New Welfare Implications. ” American Economic Review, 105(3), 1105-1146.
[45] Meyer, B. D. 1995. “Natural and Quasi-experiments in Economics.” Journal of Business and Economic Statistics,13 (2), 151-61.
[46] Minetti, R. and S. C. Zhu 2011. “Credit Constraints and Firm Export: Microeconomic Evidence from Italy.” Journal of International Economics,83, 109-25.
[47] Olley, S. and A. Pakes 1996. “The Dynamics of Productivity in the Telecommunications Equipment Industry.” Econometrica,64, 1263-97.
[48] Petersen, M. and R. G. Rajan 1997. “Trade Credit: Theories and Evidence” The Review of Financial Studies,10(3), 661-691.
[49] Shin, H. and L. Soenen 1998. “Efficiency of Working Capital Management and Corporate Profitability” Finacial Practice and Education,(8), 37-45.
[50] Staiger, D. and J. H. Stock 1997. “Instrumental Variables Regression with Weak Instru-ments.” Econometrica,65 (3), 557-86.
[51] Stock, J. and M. Yogo 2005. “Testing for Weak Instruments in Linear IV Regression.” In:
Andrews DWK Identification and Inference for Econometric Models, New York: Cambridge University Press.
[52] Van Biesebroeck, J. 2006. “Exporting Raises Productivity in Sub-Saharan African Manu-facturing Firms.” Journal of International Economics,67 (2), 373-91.
[53] Wooldridge, J. W. 2009. “On Estimating Firm-level Production Functions Using Proxy Variables to Control for Unobservables.” Economics Letters, 104, 112-4.
1.75 1.8 1.85 1.9 1.95 2
.2 .25 .3 .35 .4 .45 .5
2001 2002 2003 2004 2005 2006
Share of Private Domestic Enterprises (Left) Productivity of New Switchers (Right)
Figure 1: Share and average productivity of private domestic firms in the pool of direct exporters, 2001-2006.
Notes. The red dash line confines the period when China lifted its regulation on direct exporting rights, that is, 2004.
Part I: Main Story of Our Work
⇓
Explained Variable: Exporting Values
↓
Treatment Effect
Treatment Group: Indirect Exporters Who Switch to Direct Ones Control Group: Indirect Exporters Who Remain as Indirect Ones
↓
Goal 1: Exploring How Finance Affects the Treatment Effect
↓
Methodology: Panel-data DID Method Is Applied
↓
Goal 2: Exploring How the Effect of Finance above Is Affected by the WTO Accession Pre-WTO Accession: Many PDEs Are Not Allowed To Export Directly
Post-WTO Accession: More PDEs Are Allowed To Export Directly
↓
Methodology: Panel-data DDD Method Is Applied w
w
Part II: Main Results of Our Work
⇓
Contribution 1: We Identify an Augmented Treatment Effect on the Treated Where Finance Promotes More Exports for Firms When They Switch from Indirect to Direct Exporting
↓
Contribution 2: We Reveal That the Encouraging Effect of Finance for the Treated Group is Greater after China’s WTO Accession When More PDE Firms Are Engaged in Direct Exporting
Figure 2: The Main Idea and Contributions of Our Work.
1.021.051.081.111.14
Liquidity Ratio
2001 2002 2003 2004 2005 2006 Non−switchers
Switchers
44.24.44.64.8
Receivable Turnover Ratio
2001 2002 2003 2004 2005 2006
2.533.544.5
Inventory Turnover Ratio
2001 2002 2003 2004 2005 2006
180200220240260
Operation Cycle (Days)
2001 2002 2003 2004 2005 2006
Figure 3: Four measures of firms’ efficiency in utilizing finance, 2001-2006.
Notes. Non-switchers are indirect exporters in both previous and current periods. Switchers are firms switching from indirect exporters in the previous period to direct exporters in the current period. Since the construction of receivable turnover ratio and inventory turnover ratio requires lagged variables, the four financial variables start from 2001.
Table 1: Basic Statistical Summary of the ASIP Data set
Year Number of Number of TFPR TFPR of Value Value Added of Employment Capital Intermediate
Firms Exporters Exporters Added Exporters Value Stock Input
2000 146,898 36,759 1.46 1.62 14,105 28,573 354 25,247 39,597
2001 153,958 39,997 1.55 1.71 14,833 28,992 296 24,348 41,570
2002 165,491 44,886 1.64 1.77 16,600 31,738 287 24,274 45,893
2003 180,696 50,534 1.73 1.83 19,410 37,006 276 24,294 55,254
2004 258,390 76,482 1.79 1.88 17,235 31,645 224 20,400 49,465
2005 250,467 74,250 1.85 1.91 21,492 38,993 240 24,123 59,697
2006 278,014 78,052 1.9 1.95 24,101 45,515 229 25,227 65,822
Notes. As in Hsieh and Klenow (2009), TFPR is dimensionless; value added is measured in thousand yuan; labor is measured in persons; capital and intermediate inputs are measured in thousand yuan.
Table 2: Basic Statistical Summary of the Customs Data set
Year Number of Number of Export Total Average Number of
Observations Firms Value Destinations Destinations Products
2000 1,882,359 62,746 29,6791.4 213 6.9 30
2001 2,121,515 68,487 286,292.2 222 7.3 30.9
2002 2,613,005 78,612 270,810.7 222 7.5 33.2
2003 3,243,538 95,686 276,459.1 220 7.8 33.9
2004 4,029,789 120,590 297,836.6 220 8.3 33.4
2005 5,103,048 144,030 298,019.1 221 8.3 35.4
2006 6,187,856 171,144 301,018.7 220 8.1 36.2
Notes. Export value is measured in thousand yuan.
Table 3: Three Types of Firms in the Matched Dataset
Year Exporting Number of Mean Custom Export Average
Mode Firms TFPR Value Destinations
2000 Direct 15,639 1.63 55,120.52 6.46
Indirect 21,120 1.47 26,580.81
Nonexporters 106,994 1.37
2001 Direct 17,957 1.71 55,482.69 7.00
Indirect 22,040 1.53 26,678.49
Nonexporters 110,188 1.48
2002 Direct 21,157 1.77 60,235.41 7.66
Indirect 23,729 1.65 29,911.51
Nonexporters 115,891 1.57
2003 Direct 25,392 1.85 68,748.30 8.27
Indirect 25,142 1.74 37,509.51
Nonexporters 124,233 1.66
2004 Direct 41,392 1.88 64,746.70 8.09
Indirect 37,431 1.81 37,237.03
Nonexporters 174,321 1.73
2005 Direct 38,683 1.93 78,127.19 9.21
Indirect 35,567 1.85 47,413.39
Nonexporters 166,285 1.78
2006 Direct 41,944 1.97 90,630.63 9.81
Indirect 36,109 1.91 61,387.64
Nonexporters 188,714 1.84
Notes. As in Hsieh and Klenow (2009), TFPR is dimensionless; custom export value is measured in thousand yuan.
Table 4: Difference-in-Differences Estimation for Export Value with Internal Finance
Dependent Variable (horizontal) Export Value Export Value Export Value Export Value
(1) (2) (3) (4)
dExportingmode × 0.1096*** 0.1097***
internalfinance [0.0376] [0.0376]
dExportingmode IV × 0.4261** 0.4332**
internalfinance [0.2109] [0.2168]
Year Fixed Effect YES YES YES YES
Age NO YES NO YES
Size NO YES NO YES
R Squared 0.14 0.14 0.14 0.14
Number of Observations 25,728 25,721 25,576 25,569
Table 5: Difference-in-Differences Estimation for Export Value with External Finance
Dependent Variable (horizontal) Export Value Export Value Export Value Export Value
(1) (2) (3) (4)
dExportingmode × 0.1728*** 0.1723***
externalfinance [0.0291] [0.0291]
dExportingmode IV × 0.2892** 0.2931**
externalfinance [0.1358] [0.1439]
Year Fixed Effect YES YES YES YES
Age NO YES NO YES
Size NO YES NO YES
R Squared 0.15 0.15 0.15 0.15
Number of Observations 25,602 25,594 25,476 25,468
Notes. Robust standard errors in bracket; export value is measured in thousand yuan; year-fixed effect is a linear combination of year dummies for 2001-2006; size is measured by firms’ capital stock; dExport-ingmode IV is constructed as the product of firm-level initial productivity and province-level aggregate capital supply shock; *, *** indicate significance at the 10% and 1% confidence level, respectively.
Table 6: Difference-in-Differences Estimation for TFPR with Internal Finance
Dependent Variable (→) TFPR TFPR TFPR TFPR
(1) (2) (3) (4)
dExportingmode × 0.0144*** 0.0142***
internalfinance [0.0047] [0.0047]
dExportingmode IV × 0.0778*** 0.0783***
internalfinance [0.0084] [0.0084]
Year Fixed Effect YES YES YES YES
Age NO YES NO YES
Size NO YES NO YES
R Squared 0.03 0.03 0.02 0.03
Number of Observations 37,630 37,618 37,438 37,426
Table 7: Difference-in-Differences Estimation for TFPR with External Finance
Dependent Variable (→) TFPR TFPR TFPR TFPR
(1) (2) (3) (4)
dExportingmode × 0.0064* 0.0064*
externalfinance [0.0037] [0.0037]
dExportingmode IV × 0.0655*** 0.0659***
externalfinance [0.0072] [0.0072]
Year Fixed Effect YES YES YES YES
Age NO YES NO YES
Size NO YES NO YES
R Squared 0.03 0.03 0.02 0.02
Number of Observations 37,460 37,447 37,274 37,261
Notes. Robust standard errors in bracket; TFPR is dimensionless; year-fixed effect is a linear combination of year dummies for 2001-2006; size is measured by firms’ capital stock;dExportingmode IV is constructed as the product of firm-level initial productivity and province-level aggregate capital supply shock; *, ***
indicate significance at the 10% and 1% confidence level, respectively.
Table 8: Difference-in-Difference-in-Differences Estimation for Export Value with Internal Finance
Dependent Variable (→) Export Value Export Value Export Value Export Value Export Value Export Value
(2002) (2002) (2003) (2003) (2004) (2004)
dExportingmode IV ×
internalfinance × 3.8972*** 3.8880*** 0.9837*** 0.9725*** 0.6182** 0.6157**
dWTO [0.7758] [0.7761] [0.3412] [0.3414] [0.2733] [0.2731]
Year Fixed Effect YES YES YES YES YES YES
Age NO YES NO YES NO YES
Size NO YES NO YES NO YES
R Squared 0.18 0.18 0.16 0.16 0.18 0.18
Number of Observations 25,593 25,586 25,593 25,586 25,593 25,586
Notes. Robust standard errors in bracket; export value is measured in thousand yuan; 2002, 2003, 2004 in parenthesis denote the critical years that we use to define dW T O; year-fixed effect is a linear combination of year dummies for 2001-2006; size is measured by firms’ capital stock;
dExportingmode IV is constructed as the product of firm-level initial productivity and province-level aggregate capital supply shock; **, ***
indicate significance at the 5%, 1% confidence level, respectively.
50
Table 9: Difference-in-Difference-in-Differences Estimation for Export Value with External Finance
Dependent Variable (→) Export Value Export Value Export Value Export Value Export Value Export Value
(2002) (2002) (2003) (2003) (2004) (2004)
dExportingmode IV×
externalfinance× 8.6627*** 8.5982*** 2.7693** 2.7205** 2.6241* 2.6053*
dWTO [3.2160] [3.1850] [1.1851] [1.1721] [1.4625] [1.4552]
Year Fixed Effect YES YES YES YES YES YES
Age NO YES NO YES NO YES
Size NO YES NO YES NO YES
R Squared 0.22 0.21 0.23 0.22 0.24 0.24
Number of Observations 25,576 25,568 25,576 25,568 25,576 25,568
Notes. Robust standard errors in bracket; export value is measured in thousand yuan; 2002, 2003, 2004 in parenthesis denote the critical years that we use to define dW T O; year fixed effect is a linear combination of year dummies for 2001-2006; size is measured by firms’ capital stock;
dExportingmode IV is constructed as the product of firm-level initial productivity and province-level aggregate capital supply shock; *, **, ***
indicate significance at the 10%, 5%, 1% confidence level, respectively.
51
Table 10: Difference-in-Differences Estimation for Export Value with Internal Finance
Dependent Variable Liquidity Receivable Turnover Inventory Turnover Operation Cycle
(horizontal) (1) (2) (3) (4)
dExportingmode -0.0218** 0.0026 0.0534*** -0.0322***
[0.0107] [0.0130] [0.0127] [0.0094]
Export Share -0.0002 -0.0006*** -0.0008*** 0.0007***
[0.0002] [0.0002] [0.0002] [0.0001]
Constant 0.1516*** 1.6130*** 1.2711*** 5.3167***
[0.0149] [0.0181] [0.0177] [0.0132]
R Squared 0.16 0.09 1.14 0.08
Number of Observations 9,830 9,853 9,853 9,853
Notes. Robust standard errors in bracket; export share is the share of exports in firms’ total sales, included to control for the level of involvement in international markets after the entry into direct exporting; age, size and year fixed effect have been controlled for all regressions; **, *** indicate significance at the 5%
and 1% confidence level, respectively.
Table 11: Difference-in-Differences Estimation for Export Value with Internal Finance Proxy
Dependent Variable (horizontal) Export Value Export Value Export Value Export Value
(1) (2) (3) (4)
dExportingmode × 0.1005*** 0.1006***
internalfinance [0.0123] [0.0124]
dExportingmode IV × 0.4019* 0.4084*
internalfinance [0.2163] [0.2165]
Year Fixed Effect YES YES YES YES
Age NO YES NO YES
Size NO YES NO YES
R Squared 0.15 0.15 0.15 0.15
Number of Observations 25,728 25,721 25,593 25,586
Table 12: Difference-in-Differences Estimation for Export Value with External Finance Proxy
Dependent Variable (horizontal) Export Value Export Value Export Value Export Value
(1) (2) (3) (4)
dExportingmode × 0.1213*** 0.1212***
externalfinance [0.0087] [0.0087]
dExportingmode IV × 0.2934* 0.2967*
externalfinance [0.1766] [0.1797]
Year Fixed Effect YES YES YES YES
Age NO YES NO YES
Size NO YES NO YES
R Squared 0.15 0.16 0.15 0.15
Number of Observations 25,602 25,594 25,470 25,462
Notes. Robust standard errors in bracket; export value is measured in thousand yuan; internal and external finance are proxied by province-sector-level aggregate of firm-level counterparts; year fixed effect is a linear combination of year dummies for 2001-2006; size is measured by firms’ capital stock; dExportingmode IV is constructed as the product of firm-level initial productivity and province-level aggregate capital supply shock; *, *** indicates significance at the 10% and 1% confidence level, respectively.
Table 13: Difference-in-Differences Estimation for TFPR with Internal Finance Proxy
Dependent Variable (→) TFPR TFPR TFPR TFPR
(1) (2) (3) (4)
dExportingmode × 0.0232*** 0.0237***
internalfinance [0.0073] [0.0073]
dExportingmode IV × 0.0757*** 0.0762***
internalfinance [0.0082] [0.0082]
Year Fixed Effect YES YES YES YES
Age NO YES NO YES
Size NO YES NO YES
R Squared 0.03 0.03 0.03 0.03
Number of Observations 37,630 37,618 37,438 37,426
Table 14: Difference-in-Differences Estimation for TFPR with External Finance Proxy
Dependent Variable (→) TFPR TFPR TFPR TFPR
(1) (2) (3) (4)
dExportingmode × 0.0137** 0.0140**
externalfinance [0.0060] [0.0061]
dExportingmode IV × 0.0629*** 0.0633***
externalfinance [0.0069] [0.0068]
Year Fixed Effect YES YES YES YES
Age NO YES NO YES
Size NO YES NO YES
R Squared 0.03 0.03 0.03 0.02
Number of Observations 37,460 37,447 37,274 37,261
Notes. Robust standard errors in bracket; TFPR is dimensionless; internal and external finance are proxied by province-sector-level aggregate of firm-level counterparts; year fixed effect is a linear combination of year dummies for 2001-2006; size is measured by firms’ capital stock; dExportingmode IV is constructed as the product of firm-level initial productivity and province-level aggregate capital supply shock; **, ***
indicates significance at the 5% and 1% confidence level, respectively.
Table 15: Difference-in-Difference-in-Differences Estimation for Export Value with Internal Finance Proxy
Table 15: Difference-in-Difference-in-Differences Estimation for Export Value with Internal Finance Proxy