does trade credit boost firm performance?

Does Trade Credit Boost Firm Performance?

Dongya Lia and Yi Luba University of Hong Kong

b National University of Singapore

This Version: May 2011

Abstract

A counterexample to the �ndings in the �nance and development liter-ature is that �rms have achieved good performance in many develop-ing economies where the �nancial sector is far from established. Onewidely suggested mechanism in the literature is that �rms in theseeconomies rely on a large amount of informal �nancing, i.e., tradecredit. Using a survey of �rms in China conducted by the WorldBank in early 2003, this paper examines the impact of trade credit on�rm performance. Ordinary least squares estimations show that tradecredit is signi�cantly and positively correlated with �rm performance.However, after we use the instrumental variable approach to tacklepotential endogeneity issues, trade credit no longer has any impacton �rm performance. The results are robust to a series of robustnesschecks. Our study suggests that trade credit plays a limited role inboosting �rm performance.

Keywords: Trade Credit, Firm Performance, Informal Financing

JEL Codes: G3, L2, D2, O1

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Does Trade Credit Boost Firm Performance?

1 Introduction

It is widely acknowledged that �nancial institutions play an important rolein promoting �rm growth and �rm performance (e.g., Demirgüç-Kunt andMaksimovic, 1996, 1998; Beck, Demirgüç-Kunt and Maksimovic, 2004; Dyckand Zingales, 2004).1 However, in many developing economies where the �-nancial sector is far from established, �rms have achieved good performancein the past a few decades, especially private �rms, which are often discrimi-nated against in access to bank loans. China provides an illustrative example(e.g., Allen, Qian, and Qian, 2005; Ayyagari, Demirgüç-Kunt, and Maksi-movic, 2010). Firms in developing economies have also been found to obtaina large amount of informal �nancing such as trade credit (e.g., McMillanand Woodru¤, 1999; Cull, Xu, and Zhu, 2007), which leads to the implicitsuggestion that �rms may achieve good performance through trade credit(e.g., Ge and Qiu, 2007). The question is: does trade credit really boost �rmperformance?It is surprisingly that studies on this important issue are limited, although

there is a large literature investigating various determinants of trade credit(e.g., Ferris, 1981; Mian and Smith, 1992; Biais and Gollier, 1997; Petersenand Rajan, 1997; McMillan andWoodru¤, 1999; Ng, Smith, and Smith, 1999;and Cuñat, 2007). To the best of our knowledge, Fisman and Love (2003) isthe only one that has studied the impacts of trade credit on industry growthand �nd that industries with a higher degree of dependence on trade creditexhibit higher growth rates in countries with weaker �nancial institutions.Using a survey of �rms in China conducted by the World Bank in early 2003,this paper explores the impact of trade credit at the �rm level.China o¤ers us a good setting to study the impact of trade credit. On

the one hand, the country lacks well-developed �nancial institutions, andfast-growing �rms in China rely on informal �nancing channels instead offormal �nancial institutions (Allen, Qian, and Qian, 2005). On the otherhand, China is a large country with substantial variations in the develop-ment of �nancial institutions across regions, and �rms di¤er in �nancing

1There is a large body of literature on the impact of �nancial markets on economicperformance and economic growth at the country level (e.g., Goldsmith, 1969; King andLevine, 1993; Levine and Zervos, 1998; Levine, 1998, 1999; Levine, Loayza, and Beck,2000; La Porta, Lopez-de-Silanes, and Shleifer, 2002), and at the industry level (e.g.,Rajan and Zingales, 1998; Wurgler, 2000; Cetorelli and Gambera, 2001; Claeseens andLaeven, 2005). Levine (2005) provides an excellent review of this literature.

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patterns across regions (Ayyagari, Demirgüç-Kunt, and Maksimovic, 2010).This great reliance on informal �nancing and the variations in �rm �nancingpatterns allow us to identify the impact of trade credit on �rm performance.To gauge �rm performance, we employ two variables, labor productivity

(measured as the logarithm of output per worker) and return on assets (ROA)(measured as the return on �xed assets calculated at book value). Our keyexplanatory variable, trade credit, is measured as the average percentage ofthe two major inputs that a �rm bought without making an immediate cashpayment.Ordinary-least-squares (OLS) estimations show that trade credit is pos-

itively and signi�cantly correlated with both labor productivity and ROA.However, these estimates may be biased due to the omitted variables biasand the reverse causality issue, and thus may be unable to capture the causalimpact of trade credit on �rm performance. To address these potential endo-geneity problems, we employ the instrumental variable (IV) approach. Morespeci�cally, we use the average number of days it could take a �rm to obtainreplacements if the main suppliers of its two major inputs failed to deliverand the number of the �rm�s two main inputs that are supplied by rela-tives of the �rm owner as our two instruments for trade credit. McMillanand Woodru¤ (1999) and Cuñat (2007) show that suppliers are more likelyto o¤er trade credit to their customers when they both belong to the samenetworks, such as family and are locked into the transaction relationship.This is because customers�any misbehavior leads to the spread of bad wordsamong members of the networks and the termination of further deliveries oftailor-made inputs, subsequently causing severe damage to them.The �rst-stage results of the IV regressions show that our instrumen-

tal variables are positively and signi�cantly correlated with the endogenousvariable, which con�rm the above argument. Surprisingly, the second-stageresults of the IV estimations show that trade credit exerts no signi�cantimpact on �rm performance.The validity of the IV estimation hinges upon two conditions, the rele-

vance condition and the exclusion restriction. The relevance condition statesthat the instrumental variable should be signi�cantly correlated with theendogenous variable (namely, the condition of the relevant instrument) andthat the correlation should not be weak (namely, the condition of the stronginstrument). The signi�cant correlation between our instrument variablesand the endogenous variable found in the �rst-stage of the IV estimations,as well as the Anderson canonical correlation LR statistic and the Cragg-Donald Wald statistic, con�rm that our instrumental variables are relevant.In addition, the Shea partial R-square shows values around 0.01, suggestingthat the correlation between our instrumental variables and the endogenous

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variable is not small. However, despite statistical signi�cance at the 1% level,the F-test of excluded instruments is around 5.8, which is below the criticalvalue 10 of the �safety zone�for a strong instrument suggested by Straigerand Stock (1997). We then report three additional econometric tests, theAnderson-Rubin Wald test, the Stock-Wright LM S statistic, and the Finlay-Magnusson Wald test, which provide reliable statistical inferences in a weakinstrument setting (Anderson and Robun, 1949; Stock and Wright, 2000;Baum, Scha¤er, and Stillman, 2003; Finlay and Magnusson, 2009). Noneof these tests shows any statistical signi�cance, which implies that our re-sults are robust to the presence of weak instruments. For further robustnesschecks of the relevance condition, we conduct three tests suggested by An-grist and Pischke (2009): limited information maximum likelihood (LIML)estimations, reduced-form regressions of our outcome variables on instrumen-tal variables, and just-identi�ed IV estimations. Our �ndings are robust toall of these exercises, implying that the relevance condition is satis�ed in ourIV estimations.The exclusion restriction requires that the instrumental variable does not

a¤ect outcome variables through any channel other than trade credit. TheHansen J statistic, which is a standard test of the satisfaction of the exclusionrestriction in the overidenti�cation scenario, cannot reject the null hypoth-esis that at least one of our instrumental variables is exogenous. We thusprovide two additional tests of the exclusion restriction. First, reduced-formregressions �nd that our instrumental variables have no signi�cant impacton �rm performance, which implies that the exclusion restriction is satis�ed.Second, we control explicitly for other potential channels through which theinstrumental variable could a¤ect �rm performance. Speci�cally, we con-sider �ve channel variables: the quality, speci�city and delivery of inputs;the terms of trade credit; and the importance of the �rm to its main sup-pliers. Our �ndings are robust to the inclusion of these additional channelvariables, which suggests that our IV estimations are valid.For further robustness checks, we exclude outlying observations and focus

on a subsample of �nancially constrained �rms. Our main �nding that tradecredit has no signi�cant impact on �rm performance remains robust to theseexercises.To better understand why trade credit has no impact on �rm performance,

we investigate several possible explanations. It could be that �rms withaccess to trade credit also enjoy alternative sources of �nancing such as bankloans; that �rms may have access to credit from the buyer side in addition tothat from the supplier side; that trade credit goes to �rms with strong growthpotential; that trade credit as an informal �nancing channel is by naturelimited in amount (or size) and thus cannot meet some �rms��nancing needs;

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and that trade credit may have a long-run rather than a short-run impact on�rm performance. Our empirical results suggest that the size and the timingargument could partially explain our �ndings.The remainder of the paper is organized as follows. The data and variables

are described in Section 2. Section 3 presents the empirical results. The paperconcludes with Section 4.

2 Data and Variables

Our data comes from a survey of �rms on the investment climate in Chinaconducted jointly by the World Bank and the Enterprise Survey Organiza-tion of China in early 2003.2 For balanced representation, the Survey covers18 cities from �ve regions in China: Northeast: Benxi, Changchun, Dalian,and Haerbin; Coastal: Hangzhou, Jiangmen, Shenzhen, and Wenzhou; Cen-tral: Changsha, Nanchang, Wuhan, and Zhengzhou; Southwest: Chongqing,Guiyang, Kunming, and Nanning; and Northwest: Lanzhou and Xi�an. Ineach city, 100 or 150 �rms are randomly sampled from nine manufacturingindustries (garment and leather products, electronic equipment, electronicparts making, household electronics, auto and auto parts, food processing,chemical products and medicine, biotech products and Chinese medicine, andmetallurgical products) and �ve service industries (transportation services,information technology, accounting and non-banking �nancial services, ad-vertisement and marketing, and business services). The total number of �rmssurveyed is 2,400. However, since only manufacturing �rms were required toanswer the survey question regarding the use of trade credit, we are restrictedto a �nal sample with 1,566 observations.The Survey comprises two parts. One is a general questionnaire directed

at senior management seeking information about the �rm, innovation, prod-uct certi�cation, marketing, relations with suppliers and customers, accessto markets and technology, relations with government, labor, infrastructure,international trade, �nance and taxation, and the GM and board of directors.The other questionnaire is directed at accountants and personnel managers,and covers ownership, various �nancial measures, and labor and training.The Survey is basically a cross-sectional dataset, with most of the variablesmeasured in 2002; however, some of the �nancial variables, such as output,employment, and �xed assets, contain information from the past three years.We employ two variables to measure �rm performance: Labor Produc-

tivity (measured as the logarithm of output per worker in 2002) and ROA

2The data has been used recently by Cull and Xu (2005), Ayyagari, Demirgüç-Kunt,and Maksimovic (2010), and Lu, Png, and Tao (2011).

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(measured as the return on �xed assets calculated at book value in 2002).Summary statistics are provided in Table 1a.Our key explanatory variable, Trade Credit, is measured as the average

percentage of the two major inputs that a �rm bought without making animmediate cash payment in 2002. The responses vary substantially across�rms, with a mean value of 0.357 and a standard deviation of 0.373. Thesenumbers are comparable with those found in the literature, thus suggestingthat the measure of trade credit in our analysis is reliable. In the case ofChina, Cull, Xu, and Zhu (2009) �nd that the ratio of trade credit over totalsales ranges from 21.5% to 27.2% in the dataset of industrial �rms collectedby the National Bureau of Statistics of China for the period of 1998-2003,whereas Ge and Qiu (2007) document a mean value of 27% trade credit overtotal sales from the enterprise survey conducted by the Chinese Academy ofSocial Sciences (CASS) in 2000. Further, in their study of inter�rm relation-ships in Vietnam, McMillan and Woodru¤ (1999) report an average of 30%of bills going unpaid after suppliers�delivery of goods. Finally, our measureof trade credit is positively and statistically signi�cantly correlated with themeasure of trade credit intensity at the industry level reported in Fismanand Love (2003).3 Tables 1b-1c further decompose �rms�use of trade creditby �rms into cities and industries, respectively. It is found that �rms incoastal areas (i.e., Shenzhen, Hangzhou, and Jiangmen) and in electronicsmanufacturing industries (i.e., household electronics, electronic equipment,and electronic parts making) are the most likely to use trade credit, whereasthose in inland areas (i.e., Zhengzhou, Lanzhou, and Changsha) are the leastlikely.To deal with the omitted variables bias, we control for other factors that

may a¤ect �rm performance. Variables related to �rm characteristics includePercentage of Private Ownership, Firm Size (measured as the logarithm ofemployment in 2001), Firm Age (measured as the logarithm of years of es-tablishment by the end of 2002), Bank Loan (a dummy variable indicatingwhether the �rm has outstanding bank loans in 2002), and Government Rep-resentative on the Board (a dummy variable indicating whether there is agovernment representative on the board in 2002). Variables related to GMcharacteristics include his/her human capital, i.e., Education (years of school-ing by the end of 2002), Years of Being GM (years of being GM by the endof 2002), and Deputy GM Before (a dummy variable indicating whether theGM was the �rm�s deputy GM before he/she became the GM); and his/herpolitical capital, Government Cadre (a dummy variable indicating whetherthe GM was a government o¢ cial before he/she became the GM), Party

3The coe¢ cient was 1.905 with a t-statistic of 2.28.

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Membership (a dummy variable indicating whether the GM is a member ofthe Chinese Communist Party in 2002), and Government Appointment (adummy variable indicating whether the GM was appointed by the govern-ment). Next, two variables are used to control for city di¤erences: Logarithmof GDP per capita and Logarithm of Population, which measure the wealthand size of the cities, respectively. Finally, industry dummy is included toaccount for industry di¤erences.To further address the potential endogeneity problems associated with

trade credit, we employ the IV estimation. More speci�cally, the instrumen-tal variables we use are Delay (measured as the average number of days itcould take a �rm to obtain replacements if the main suppliers of its twomajor inputs failed to deliver in 2002) and Relationship (a category variablethat took values 0, 0:5, and 1 if none, one, and both of a �rm�s two maininputs are supplied by relatives of the �rm owner in 2002, respectively). Theidenti�cation strategy using these two instrumental variables is discussed inSection 3.3.The correlations among the key variables are reported in Table 2.

3 Empirical Analysis

3.1 Framework for Empirical Analysis

Three explanations are given in the literature for why trade credit may af-fect �rm performance. First, trade credit is an important form of �nancing(Emery, 1987). It is especially so in developing economies, where �nancialinstitutions are less developed and private �rms are discriminated against inaccess to formal �nancing such as bank loans (e.g., Allen, Qian, and Qian,2005; Ayyagari, Demirgüç-Kunt, and Maksimovic, 2010). Such credit allows�nancially constrained �rms to exploit pro�table growth opportunities or in-vest in e¢ ciency-improvement technologies, which, in turn, leads to better�rm performance (Demirgüç-Kunt and Maksimovic, 1998). Second, Fisman(2001) argues that �rms with supplier trade credit are less prone to inputshortages and less likely to incur interruptions in production, as a resultof which they can manage their inventories and utilize their production ca-pacities more e¢ ciently. Third, building upon Smith (1987)�s ideas, Long,Malitz, and Ravid (1993) argue that trade credit can play the role of a prod-uct quality guarantee, thereby enhancing product marketability. Given theimperfections in the input market, �rms cannot fully evaluate the quality ofinputs, and trade credit allows them to test that quality before paying.Although these explanations are theoretically appealing, however, there

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are reasons to suspect the positive, causal impact of trade credit on �rmperformance. First, suppliers do not randomly grant trade credit to theirclients. For example, private �rms are found to obtain more trade credit thanstate-owned enterprises (e.g., Ge and Qiu, 2007), and the former are moree¢ cient than the latter in China. Hence, omitting the percentage of privateownership in the regression leads to a spurious, positive correlation betweentrade credit and �rm performance. Second, the positive correlation betweentrade credit and �rm performance possibly indicates that trade credit simplygoes to better-performing �rms rather than boosting �rm performance, or thereverse causality issue. Indeed, Petersen and Rajan (1997) �nd that morepro�table �rms are more likely to receive supplier trade credit. Third, tradecredit is small in scale by nature, and hence may be capable of satisfyingthe �nancial needs of small �rms but not those of large �rms. Fourth, tradecredit may have a delayed e¤ect on �rm performance, that is, it may nota¤ect contemporary �rm performance but in�uence �rm performance in thelong-run.4

In our empirical analysis, we investigate whether trade credit boosts �rmperformance by conducting a regression of �rm performance on the extentof trade credit. We �rst conduct a series of OLS estimations, gradually con-trolling for �rm characteristics, GM characteristics, city characteristics, andindustry characteristics. A positive estimated coe¢ cient of trade credit im-plies that trade credit may be correlated with �rm performance. However,as previously noted, we face the potential omitted variable bias and the re-verse causality issue. To obtain a reliable conclusion, we carry out the IVestimation. Based on IV estimation results, we can make the following in-ferences: if trade credit remains statistically signi�cant, then it does indeedboost �rm performance; however, if trade credit loses its statistical signi�-cance, then it exerts no positive impact on �rm performance and the positivecoe¢ cients found in the OLS estimations are due to the omitted variablesbias or re�ective of the reverse causality.Meanwhile, if trade credit does not have a positive and statistically signif-

icant estimated coe¢ cient in the IV estimation, then this suggests that tradecredit on average does not have a positive impact. As argued above, tradecredit may a¤ect small �rms whose �nancial needs tend to be small or maya¤ect �rms�future performance instead of their contemporary performance.In order to investigate these possibilities, we divide the whole sample intoseveral sub-samples and repeat the analysis: small �rms vis-à-vis large �rmsand �rms with di¤erent durations of trade credit.

4We thank an anonymous referee for pointing out this possibility.

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3.2 OLS Estimates

To investigate the impacts of trade credit on �rm performance, we estimatethe following equation:

yeic = �+ � � Trade Crediteic +X 0eic + "eic (1)

where yeic is the outcome variables for �rm e in industry i and city c (i.e. , La-bor Productivity and ROA), Trade Crediteic is the key explanatory variable,X 0eic is a vector of control variables (i.e., �rm characteristics, GM character-

istics, city characteristics, and industry dummy), and "eic is the error term.Robust standard errors are clustered at the industry-city level, allowing foran arbitrary correlation within the same city and same industry.Regression results are reported in Table 3. Columns 1-2 show that trade

credit is positively and signi�cantly correlated with �rm performance.5 Quan-titatively, a one standard deviation increase in Trade Credit boosts laborproductivity and ROA by a 0.22 standard deviation and a 0.04 standarddeviation, respectively.

3.3 IV Estimates

As we argue in Section 3.1, the OLS estimates may be signi�cantly biaseddue to the omitted variables bias and the reverse causality issue. To addressthese endogeneity issues and detect the causal impact of trade credit on �rmperformance, we adopt the IV approach. More speci�cally, we re-estimateequation (1) using the two-stage-least-squares (TSLS) regression with the�rst-stage regression as follows:

Trade Crediteic = �1 + �1 �Reic +X 0eic 1 + "eic1 (2)

where Reic are instrumental variables.The �rst instrumental variable we use is Delay, measured as the aver-

age number of days it could take a �rm to obtain replacements if the mainsuppliers of its two major inputs failed to deliver in 2002. McMillan andWoodru¤ (1999) argue that when clients are locked into a relationship withtheir suppliers, suppliers can threaten not to deliver further inputs if creditis not paid back. Hence, suppliers are more willing to provide credit to theirclients when it is di¢ cult for their clients to �nd replacements in the market.Cuñat (2007) build a model showing that suppliers increase the amount of

5In the analysis, we gradually add control variables, and the results are similar witha subset of these controls. To save space, we henceforce report only the results with fullcontrols. Our results with the subset of controls are available upon request.

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trade credit to their clients when the transaction involves tailor-made inputs,learning by doing, or other sunk costs that generate a transaction surplus.The theoretical prediction is then tested and con�rmed by a panel of U.K.�rms.The second instrumental variable we use is Relationship, which is the

number of a �rm�s two main inputs that are supplied by relatives of the �rmowner in 2002. As shown by McMillan and Woodru¤ (1999), suppliers aremore likely to o¤er trade credit to their customers who belong to the samenetworks, such as families, friends, and business associations. This is becausecustomers�any misbehavior leads to the spread of bad word among membersof the same networks, causing severe damage to them.Regression results are presented in Table 4. The �rst-stage results of

the TSLS reported in Panel B show that instrument variables (i.e., Delayand Relationship) have a positive and signi�cant impact on Trade Credit,which is consistent with the foregoing argument. Interestingly, the second-stage results of the TSLS reported in Panel A show that trade credit, afterbeing instrumented, has no signi�cant impact on either labor productivityor pro�tability.

3.3.1 Relevance Condition

The validity of the IV estimation hinges upon two conditions, the relevancecondition and the exclusion restriction.The relevance condition requires that the instrumental variable be signif-

icantly correlated with the endogenous variable (namely, the condition of therelevant instrument) and that this correlation not be weak (namely, the con-dition of the strong instrument). For a check on the condition of the relevantinstrument, we report the Anderson canonical correlation LR statistic andthe Cragg-DonaldWald statistic in Panel C. In addition to the signi�cant cor-relation between instrumental variables and the endogenous variable foundin Panel B, these two tests further con�rm that our instrumental variablesare relevant.For a check on the condition of the strong instrument, we �rst report

two statistical tests, the Shea partial R-square and the F-test of excludedinstruments. The former, with values around 0.01, suggests that the cor-relation between our instrumental variables and the endogenous variable isnot small. However, despite a statistical signi�cance at the 1% level, the F-test of excluded instruments is around 5.8, which is below the critical value10 of the �safety zone�for the strong instrument suggested by Straiger andStock (1997). This may raise concerns over the possibility of a weak instru-ment in our analysis. We then report three additional econometric tests,

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the Anderson-Rubin Wald test, the Stock-Wright LM S statistic, and theFinlay-Magnusson Wald test, which o¤er reliable statistical inferences in aweak instrument setting (Anderson and Robun, 1949; Stock and Wright,2000; Baum, Scha¤er, and Stillman, 2003; Finlay and Magnusson, 2009). Itis clear that none of the three tests shows any statistical signi�cance, thusimplying that our results are robust to the presence of weak instruments.For further robustness checks on the relevance condition, we conduct three

tests as suggested Angrist and Pischke (2009). First, we use the LIML es-timation, as it is approximately median-unbiased in the overidenti�cationestimation and performs better than the TSLS estimation when instrumen-tal variables are potentially weak (e.g., Davidson and MacKinnon, 1993;Mariano, 2001; Stock, Yogo, and Wright, 2002; Hahn and Hausman, 2003;Flores-Lagunes, 2007). Panel D of Table 4 reports the LIML estimationresults, in which only estimated coe¢ cients for Trade Credit are reportedto save space. It is clear that the LIML estimates are very similar to theTSLS estimates, which suggests that our instrumental variables satisfy therelevance condition.6 Second, we conduct reduced-form regressions of ouroutcome variables (i.e., Labor Productivity and ROA) on our instrumentalvariables. As noted by Angrist and Krueger (2001) and Chernozhukov andHansen (2008), if no correlation is found between our outcome variables andinstrumental variables in these reduced-form regressions, then the endoge-nous variable probably has no impact on the outcome variables. As shownin Table 5, neither Relationship nor Delay has any statistically signi�cantestimated coe¢ cients, thus suggesting that the �ndings in Table 4 are ro-bust. Finally, instead of using two instruments together, we conduct thejust-identi�ed TSLS estimation, i.e., we use one instrument each time. Thisis because the just-identi�ed TSLS estimation is median-unbiased, and thebias in the TSLS estimation increases with the number of instrumental vari-ables. Regression results are reported in Table 6. Clearly, none of theseregressions �nds trade credit to have any signi�cant impact on �rm perfor-mance, which suggests that the �ndings reported in Table 4 are not biaseddue to the weak instrument problem.

3.3.2 Exclusion Restriction

The exclusion restriction of the instrumental variable estimation requiresthat our instrumental variables do not a¤ect the outcome variables throughchannels other than the endogenous variable (i.e., Trade Credit). In PanelC of Table 4, we report the Hansen J statistic, which is a standard test of

6Henceforth, we also report the LIML estimation results when they are applicable.

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the satisfaction of the exclusion restriction in the overidenti�cation scenario.The Hansen J statistic cannot reject the null hypothesis that at least one ofour instrumental variables is exogenous.We then provide two additional robustness checks on the exclusion re-

striction. The �rst robustness check is the reduced-form regressions of ouroutcome variables on our instrumental variables instead of the endogenousvariable. The rationale behind this strategy is as follows. Note that tradecredit has no signi�cant impact on �rm performance; thus, if our instrumen-tal variables are found to have any signi�cant impact on �rm performancein the reduced-form regressions, then it could suggest that there are otherchannels through which instrument variables a¤ect �rm performance. Asshown in Table 5, our instrumental variables have no signi�cant impact on�rm performance in these reduced-form regressions, thus implying that theexclusion restriction is satis�ed.The second robustness check on the exclusion restriction involves ex-

plicit controls on the potential channels other than the endogenous variablethrough which our instrumental variables may a¤ect our outcome variables.First, our instrumental variables may a¤ect �rm performance through the

quality of inputs. In the Survey, there is a question regarding the percentageof inputs with lower than expected quality, and a variable called Qualityis constructed accordingly. As a robustness check, we include Quality asan additional control variable in the TSLS estimation and the results arereported in Columns 1 and 6 of Table 7. Clearly, our main results regardingthe insigni�cant impact of Trade Credit on �rm performance remain robustto this control.Second, our instrumental variables may a¤ect �rm performance through

the speci�city of inputs. In the Survey, there is a question asking whetherthe inputs are made to the �rm�s unique speci�cations, and a variable calledSpeci�city is constructed accordingly. As a robustness check, we includeSpeci�city as an additional control variable in the TSLS estimation and theresults are reported in Columns 2 and 7 of Table 7. Clearly, our main re-sults regarding the insigni�cant impact of Trade Credit on �rm performanceremain robust to this control.Third, our instrumental variables may a¤ect �rm performance through

the delivery of inputs. In the Survey, there is a question regarding the per-centage of sales lost in the previous year due to delivery delays from suppliers,and a variable called Delivery is constructed accordingly. As a robustnesscheck, we include Delivery as an additional control variable in the TSLS esti-mation and the results are reported in Columns 3 and 8 of Table 7. Clearly,our main results regarding the insigni�cant impact of Trade Credit on �rmperformance remain robust to this control.

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Fourth, our instrumental variables may a¤ect �rm performance throughthe terms of trade credit, e.g., bene�ts accruing to the supplier in exchangefor more generous credit terms. In the Survey, there is a question regardingthe average number of days that the �rm is allowed to pay back trade credit,which can be used to proxy for the trade credit terms. A variable called CreditTerm is constructed accordingly, and is included as an additional controlvariable in the TSLS estimation. Regression results reported in Columns 4and 9 of Table 7 show that the impact of Trade Credit on �rm performanceremains insigni�cant.Finally, our instrumental variables may a¤ect �rm performance through

the price of the inputs, because a close relationship between the �rm and itssuppliers may increase/decrease the �rm�s bargaining power, thus resultingin lower/higher input price. Although there is no question in the Surveydirectly measuring the prices of inputs, there is a question allowing us toproxy for the �rm�s bargaining power vis-à-vis its suppliers, that is, a questionasking the �rm whether it is the most important client of its main supplier,with an a¢ rmative response re�ecting a higher degree of bargaining power.A variable called Most Important Client is constructed accordingly, and isincluded as an additional control variable in the TSLS estimation. Regressionresults reported in Columns 5 and 10 of Table 7 show that the impact of TradeCredit on �rm performance remains insigni�cant.Overall, regression results reported in Tables 4-7 imply that our IV es-

timation is valid, and that trade credit has no signi�cant impact on �rmperformance. These �ndings are contrary to those obtained from the OLSestimations, suggesting that OLS estimates are indeed biased due to theendogeneity problems and should be interpreted with caution.

3.4 Robustness Checks

3.4.1 Outliers

One may be concerned that our main �ndings are driven by outliers, giventhe large variations in outcome variables. To address this concern, we followHadi (1992, 1994) in identifying outliers in our multivariate data and excludethem in the analysis. The results reported in Table 8 show that trade credithas no signi�cant impact on �rm performance, which implies that the concernfor outliers is not relevant in our case.

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3.4.2 Financially Constrained Firms

The second robustness check is to determine whether trade credit has anyimpact on �rms with �nancial constraints. To de�ne whether a �rm is �nan-cially constrained or not, we employ the following procedures. First, in theSurvey, there is a question asking �rms why they do not have bank loans.Two answers are possible: either the �rm never applied for a bank loan or itdid so but its application was rejected. We thus de�ne a �rm as �nanciallycontrained if it chooses the second answer. Next, for �rms choosing the �rstanswer, there is a further question asking why they did not apply for a bankloan, with six possible answers: (i) there is no need for a bank loan; (ii) theapplication procedure is too cumbersome; (iii) collateral requirements are toostringent; (iv) the interest rate is too high; (v) there is corruption in the al-location of bank loans; and (vi) there is no expectation of approval. Clearly,answers (ii)-(vi) imply that the �rm had the intention to apply for a bankloan but was deterred from doing so due to the high costs, whereas answer(i) suggests that the �rm may be capitally abundant. Thus, we include �rmschoosing answers (ii)-(vi) in the sample of �nancially constrained �rms. Fi-nally, we further include �rms with bank loans in the sample of �nanciallyconstrained �rms.7 Regression results reported in Table 9 are consistent withour earlier �ndings.

3.5 Discussion

In this subsection, we explore several possible explanations for why tradecredit has no signi�cant impact on �rm performance.The �rst possible explanation is that �rms may have access to other

sources of �nancing and thus are not �nancially constrained. For example,banks may grant loans to �rms with a good reputation, thus resolving theirneeds for external �nancing. However, all of our regressions already include avariable related to access to banks loans. Moreover, in one of the robustnesschecks, we focus on a subsample of �rms that are �nancially constrained butstill �nd trade credit to exhibit no signi�cant impact on �rm performance.These results suggest that access to other sources of �nancing may not bethe explanation.Second, in addition to trade credit from the supplier side, �rms may have

access to credit from the buyer side. In the Survey, there is a question regard-ing the percentage of cash payments by clients, and a variable called BuyerCredit is constructed accordingly. To investigate whether access to buyer

7The results are similar if �rms with bank loans are excluded from the sample of�nancially constrainted �rms.

14

credit could explain our results, we include Buyer Credit as an additionalcontrol variable in the TSLS estimation and the results are reported in Table10. Clearly, our results regarding the insigni�cant impact of Trade Credit on�rm performance remain robust to this control, thus implying that access tobuyer credit explanation is not relevant.Third, it could be possible that suppliers o¤er credit to �rms with great

growth potential or good performance, which would lead to the estimationbias in the OLS estimations and explain why the TSLS estimations fail to�nd a positive impact of trade credit on �rm performance. To investigate thispossibility, we employ average labor productivity in the past three years asa proxy for a �rm�s growth potential, and include it as an additional controlvariable in the OLS estimation to see whether it causes the disappearanceof trade credit�s statistical signi�cance.8 Regression results are reported inTable 11. It is clear that the inclusion of the proxy for the �rm�s growthpotential does not change the signi�cance of the estimated coe¢ cients oftrade credit, thus suggesting that the argument that trade credit goes to�rms with growth potential is unlikely to explain our results.Fourth, trade credit as an informal �nancing channel is by nature limited

in amount (or size). It may be able to satisfy the �nancial needs of startupand relatively small �rms, but is probably ill-equipped to scale up and meetthe �nancing needs of large and fast-growing �rms. To investigate this possi-bility, we divide the full sample into two subsamples, one with large �rms andthe other with small �rms, and check whether trade credit has positive andsigni�cant estimated coe¢ cients in the latter but not in the former.9 TheOLS regression results are reported in Table 12. As shown in Columns 1-2,although both signi�cant at the 1% level, trade credit has a larger impacton labor productivity in the sample of small �rms than that of large �rms.Moreover, trade credit has a positive and statistically signi�cant impact onpro�tability in the sample of small �rms but not in that of large �rms. Theseresults suggest that the scale of trade credit may partially explain why tradecredit appears to have no overall impact on �rm performance.Lastly, trade credit may not a¤ect contemporary �rm performance. In-

stead, �rm performance may be correlated with past access to trade credit.Given that we have a cross-sectional data, however, we are not able to fullytestify this hypothesis. As a partial test, we divide the full sample into twosub-samples according to the terms of a �rm�s trade credit. More speci�cally,in the Survey, there is a question regarding the number of days in which the

8We also experiment with the average growth rate in terms of both employment andsales in the past two years as proxies for growth potential, and the results are similar.

9A �rm is de�ned as large or small by whether its size is above or below the samplemean.

15

�rm is required to pay back trade credit (so as to avoid any penalties) fortheir two major inputs. Based on the replies to this question, we divide �rmsinto those that are required to pay back trade credit within 90 days and thosethat are required to pay back beyond 90 days. If trade credit has a long-runrather than contemporary e¤ect, then trade credit would not be expected tohave an impact on �rm performance for �rms with shorter trade credit terms(i.e., less than 90 days) and it should continue to have a signi�cant impactfor �rms with longer trade credit terms (i.e., more than 90 days). Indeed,as reported in Table 13, it is found that trade credit has no impact on �rmpro�tability for �rms with trade credit expires within 90 days but continuesto exert a positive and statistically signi�cant impact on �rm pro�tabilityfor �rms a¤orded trade credit for a period longer than 90 days.In summary, our exercises shed some lights on why trade credit does not

a¤ect �rm performance overall. Possibly, it is because trade credit constitutesonly a small chunk of money and hence is capable only of meeting the �nancialneeds of small �rms. Another possibility is that the impact of trade creditis a long-run e¤ect, that is, trade credit may a¤ect �rm performance in thelong-run rather than in the short-run.

4 Conclusion

Contrary to the �ndings in the �nance and development literature, �rms inmany developing economies have achieved good performance in the past fewdecades despite the weak �nancial sectors. One widely suggested mechanismby which this good performance takes place is that these �rms may haveaccess to alternative �nancing, such as trade credit. However, the directevidence in support of this explanation is limited. To �ll the gap, this paper,uses a survey of �rms in China conducted by the World Bank in early 2003to empirically investigate the impact of trade credit on �rm performance.The OLS estimates show that trade credit is positively and signi�cantly

correlated with �rm performance. However, when we employ the IV ap-proach to address the concern over the endogeneity issue associated withtrade credit, the TSLS regressions �nd trade credit to have no signi�cantimpact on �rm performance. Our �ndings are robust to a set of checks onthe validity of the IV estimation, the exclusion of outlying observations, anda subsample of �nancially constrained �rms. We then investigate severalpossible explanations for our results and �nd that the ine¤ectiveness of tradecredit in supporting �rm performance could be partially due to the small sizeand dynamic e¤ect of trade credit.

16

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19

Table 1a: Summary Statistics

Variable Obs Mean S.D. Min Max Labor Productivity 1557 4.322 1.562 -3.989 11.893ROA 1544 0.106 2.727 -49.000 83.640Trade Credit 1368 0.357 0.373 0.000 1.000Delay 1500 10.477 19.899 0.000 210.000Relationship 1442 0.021 0.123 0.000 1.000Percentage of Private Ownership 1566 0.813 0.376 0.000 1.000Firm Size 1563 5.040 1.453 0.000 9.899Firm Age 1566 2.494 0.777 1.099 3.970Bank Loan 1540 0.273 0.446 0.000 1.000Government Representative in the Board 1566 0.156 0.363 0.000 1.000Education 1553 14.361 2.503 0.000 18.000Years of Being GM 1548 6.240 4.580 1.000 33.000Depute GM Before 1566 0.277 0.448 0.000 1.000Government Cadre 1566 0.035 0.184 0.000 1.000Party Membership 1566 0.658 0.475 0.000 1.000Government Appointment 1566 0.239 0.427 0.000 1.000Quality 1541 0.033 0.085 0.000 1.000Specificity 1444 0.068 0.221 0.000 1.000Delivery 1524 0.021 0.050 0.000 0.500Credit Term 1011 21.707 43.427 0.000 720.000Most Important Client 1421 0.409 0.453 0.000 1.000

Table 1b: Trade Credit across Cities

City Number Trade Credit Shenzhen 61 0.582Hangzhou 64 0.527Jiangmen 60 0.474Chongqing 96 0.471Nanchang 88 0.409Xian 87 0.404Guiyang 67 0.390Changchun 87 0.388Nanning 53 0.379Wenzhou 47 0.370Dalian 58 0.325Wuhan 94 0.321Kunming 89 0.283Haerbin 93 0.266Changsha 90 0.264Lanzhou 74 0.247Benxi 61 0.233Zhengzhou 99 0.215

Table 1c: Trade Credit across Industries

Industry Number Trade Credit Biotech Products and Chinese Medicine 27 0.474 Household Electronics 55 0.420 Electronic Equipment 163 0.398 Electronic Parts Making 251 0.389 Food Processing 60 0.388 Auto and Auto Parts 318 0.385 Chemical Products and Medicine 54 0.331 Garment and Leather Products 310 0.304 Metallurgical Products 130 0.243

Table 2: Correlation

Labor Productivity ROA Trade Credit Delay RelationshipLabor Productivity 1.0000 ROA 0.2022 1.0000 Trade Credit 0.2231 0.0679 1.0000 Delay 0.0992 0.0120 0.0912 1.0000 Relationship 0.0330 0.0119 0.0820 0.0183 1.0000

Table 3: OLS Estimates

1 2 Dependent Variable Labor Productivity ROA Trade Credit 0.472*** 0.203** [0.102] [0.096] Firm Characteristics Percentage of Private Ownership 0.111*** 0.051 [0.032] [0.056] Firm Size -0.430*** -0.053 [0.064] [0.034] Firm Age 0.178 0.437 [0.121] [0.299] Bank Loan 0.451*** 0.101 [0.071] [0.103] Government Representative in the Board 0.388*** 0.293** [0.099] [0.145] GM Characteristics Human Capital Education 0.063*** 0.033 [0.019] [0.022] Years of Being GM 0.006 -0.001 [0.009] [0.007] Deputy GM Before 0.005 -0.168 [0.075] [0.147] Political Capital Government Cadre 0.227 0.013 [0.182] [0.086] Party Membership -0.152* -0.203** [0.080] [0.096] Government Appointment -0.252*** 0.163 [0.084] [0.191] City Characteristics Logarithm of GDP per Capita 0.591*** -0.129 [0.090] [0.095] Logarithm of Population 0.357*** -0.078 [0.089] [0.103] Industry Characteristics Industry Dummy Yes Yes Number of Observations 1,326 1,313 R-squared 0.3336 0.0299 F-test 20.54 2.74 p-value for F-test 0.0000 0.0004

Robust standard errors, clustered at industry-city level, are presented in the bracket. *, **, *** represent significance at 10%, 5%, 1% level respectively.

Table 4: Main Results 1 2

Panel A, Second Stage of TSLS Dependent Variable Labor Productivity ROA Trade Credit 0.943 0.129 [1.218] [0.782] Firm Characteristics Percentage of Private Ownership 0.104** 0.054 [0.051] [0.047] Firm Size -0.399*** -0.051 [0.067] [0.034] Firm Age 0.177 0.449 [0.128] [0.308] Bank Loan 0.421*** 0.1000 [0.077] [0.106] Government Representative in the Board 0.353** 0.310* [0.148] [0.169] GM Characteristics Human Capital Education 0.053** 0.033 [0.024] [0.021] Years of Being GM 0.008 -0.001 [0.009] [0.007] Deputy GM Before 0.020 -0.170 [0.088] [0.130] Political Capital Government Cadre 0.129 -0.003 [0.192] [0.081] Party Membership -0.138 -0.225** [0.117] [0.107] Government Appointment -0.283*** 0.169 [0.093] [0.184] City Characteristics Logarithm of GDP per Capita 0.577*** -0.123 [0.110] [0.108] Logarithm of Population 0.365*** -0.075 [0.092] [0.108] Industry Characteristics Industry Dummy Yes Yes

Panel B, First Stage of TSLS: Dependent Variable is Trade Credit Delay 0.001** 0.001** [0.001] [0.001] Relationship 0.224** 0.221** [0.093] [0.092] Firm Characteristics Percentage of Private Ownership -0.018 -0.012 [0.025] [0.025] Firm Size 0.026*** 0.026*** [0.009] [0.009] Firm Age -0.014 -0.014

[0.016] [0.016] Bank Loan 0.020 0.017 [0.022] [0.022] Government Representative in the Board 0.088** 0.086** [0.035] [0.035] GM Characteristics Human Capital Education 0.009** 0.010** [0.004] [0.004] Years of Being GM -0.001 -0.001 [0.002] [0.002] Deputy GM Before -0.042* -0.046** [0.022] [0.022] Political Capital Government Cadre -0.026 -0.037 [0.065] [0.064] Party Membership -0.059** -0.055** [0.024] [0.023] Government Appointment 0.025 0.030 [0.028] [0.029] City Characteristics Logarithm of GDP per Capita 0.049* 0.049* [0.027] [0.027] Logarithm of Population 0.010 0.011 [0.027] [0.027] Industry Characteristics Industry Dummy Yes Yes

Panel C, Various First-Stage Statistic Tests Relevance Test Anderson Canonical Correlations LR Statistic [9.23]*** [9.03]*** Cragg-Donald Wald Statistic [11.47]*** [11.23]*** Weak Instrument Test Shea Partial 0.0095 0.0093 F Test of Excluded Instrument [5.87]*** [5.68]*** Anderson-Rubin Wald test [0.36] [0.24] Stock-Wright LM S statistic [0.71] [0.25] Finlay-Magnusson Wald test [0.60] [0.03] Overidentification Test Hansen J statistic 0.274 0.041

Panel D, Second Stage of LIML Trade Credit 0.958 0.129 [1.256] [0.783] Number of Observations 1,265 1,252

Note: Robust standard errors, clustered at industry-city level, are presented in the round bracket. *, **, *** represent significance at 10%, 5%, 1% level respectively. In Panel D, the limited information maximum likelihood (LIML) regressions include the same control variables as those in the corresponding two-stage-least-squares (TSLS) regressions but results of these control variables are not reported to save space (available upon request).

Table 5: Mean Results, Counter Check I

1 2 Dependent Variable Labor Productivity ROA Delay 0.002 0.000 [0.002] [0.002] Relationship 0.088 0.052 [0.355] [0.121] Included Control Variables Firm Characteristics Yes Yes GM Characteristics Yes Yes City Characteristics Yes Yes Industry Characteristics Yes Yes Number of Observations 1,265 1,252 R-squared 0.3125 0.0286 F-test 17.01 2.52 p-value for F-test 0.0000 0.0010

Note: Robust standard errors, clustered at industry-city level, are presented in the round bracket. *, **, *** represent significance at 10%, 5%, 1% level respectively.

Table 6: Mean Results, Counter Check II

1 2 3 4 Panel A, Second Stage of TSLS

Dependent Variable Labor Productivity ROA Trade Credit 1.566 0.448 0.055 0.198

[1.719] [1.438] [1.230] [0.519] Panel B, First Stage of TSLS: Dependent Variable is Trade Credit

Delay 0.001** 0.001** [0.001) [0.001) Relationship 0.236** 0.233** [0.092] [0.092]

Panel C, Various First-Stage Statistics Tests Relevance Test Anderson Canonical Correlations LR Statistic [5.11]** [5.81]** [4.95]** [5.74]** Cragg-Donald Wald Statistic [5.86]** [6.64]*** [5.68]*** [6.56]** Weak Instrument Test Shea Partial 0.0048 0.0057 0.0047 0.0057 F Test of Excluded Instrument [6.22]*** [6.49]** [6.01]*** [6.38]*** Anderson-Rubin Wald test [0.78] [0.09] [0.00] [0.15] Stock-Wright LM S statistic [0.77] [0.09] [0.00] [0.15] Finlay-Magnusson Wald test [0.83] [0.10] [0.00] [0.15] Included Control Variables Firm Characteristics Yes Yes Yes Yes GM Characteristics Yes Yes Yes Yes City Characteristics Yes Yes Yes Yes Industry Characteristics Yes Yes Yes Yes Number of Observations 1,291 1,296 1,278 1,283


Table 7: Main Results, Counter Check III

1 2 3 4 5 6 7 8 9 10 Panel A, Second Stage of TSLS

Dependent Variable Labor Productivity ROA Trade Credit 0.979 0.962 0.727 0.835 0.850 0.101 0.200 0.084 0.342 0.135 [1.235] [1.198] [1.354] [1.940] [1.239] [0.780] [1.162] [0.893] [1.467] [0.813] Quality -0.737 0.312 [0.620] [0.338] Specificity 0.072 -0.141 [0.163] [0.505] Delivery 0.460 0.838 [0.912] [0.697] Credit Term -0.001 -0.001 [0.008] [0.005] Most Important Client 0.298*** -0.043 [0.102] [0.083]

Panel B, First Stage of TSLS: Dependent Variable is Trade Credit Delay 0.001** 0.001** 0.001** 0.001* 0.001** 0.001** 0.001** 0.001** 0.001* 0.001** [0.001] [0.001] [0.001] [0.001] [0.001] [0.001] [0.001] [0.001] [0.001] [0.001] Relationship 0.229** 0.228** 0.203** 0.176* 0.215** 0.226** 0.223** 0.201** 0.173* 0.212** [0.094] [0.092] [0.099] [0.099] [0.092] [0.093] [0.093] [0.099] [0.099] [0.092]

Panel C, Various First-Stage Statistics Tests Relevance Test Anderson Canonical Correlations LR Statistic [8.76]** [9.02]** [8.15]** [5.24]* [8.74]** [8.58]** [8.61]** [7.99]** [5.05]* [8.51]** Cragg-Donald Wald Statistic [10.75]*** [11.51]*** [9.90]*** [6.04]** [10.94]*** [10.52]*** [10.93]*** [9.72]*** [5.78]* [10.64]*** Weak Instrument Test Shea Partial 0.0089 0.0094 0.0083 0.0075 0.0093 0.0087 0.0090 0.0082 0.0073 0.0091 F Test of Excluded Instrument [5.49]*** [6.20]*** [4.81]** [3.17]* [5.69]*** [5.31]*** [5.84]*** [4.67]** [3.03]* [5.45]*** Anderson-Rubin Wald test [0.88] [0.81] [0.65] [0.27] [0.72] [0.37] [0.21] [0.25] [0.38] [0.26] Stock-Wright LM S statistic [0.85] [0.79] [0.64] [0.28] [0.70] [0.38] [0.21] [0.25] [0.39] [0.27]

Finlay-Magnusson Wald test [0.63] [0.64] [0.29] [0.19] [0.47] [0.02] [0.03] [0.01] [0.05] [0.03] Overidentification Test Hansen J statistic 0.444 0.320 0.438 0.104 0.365 0.093 0.013 0.083 0.060 0.050

Panel D, Second Stage of LIML Trade Credit 1.007 0.980 0.745 0.843 0.868 0.101 0.200 0.084 0.342 0.135 [1.303] [1.244] [1.441] [1.983] [1.294] [0.781] [1.163] [0.894] [1.470] [0.814] Included Control Variables Firm Characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes GM Characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes City Characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Industry Characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Number of Observations 1,263 1,252 1,249 870 1,245 1,250 1,240 1,236 859 1,232

Note: Robust standard errors, clustered at industry-city level, are presented in the round bracket. *, **, *** represent significance at 10%, 5%, 1% level respectively. The first Stage of TSLS contains same controls as the second stage but results of these control variables are not reported to save space (available upon request). In Panel D, the limited information maximum likelihood (LIML) regressions include the same control variables as those in the corresponding two-stage-least-squares (TSLS) regressions but results of these control variables are not reported to save space (available upon request)

Table 8: Robustness Check I, Outliers

1 2 Panel A, Second Stage of TSLS

Dependent Variable Labor Productivity ROA Trade Credit 0.684 0.156 [1.185] [0.189]

Panel B, First Stage of TSLS: Dependent Variable is Trade Credit Delay 0.001** 0.001** [0.001] [0.001] Relationship 0.242** 0.240** [0.099] [0.098]

Panel C, Various First-Stage Statistics Tests Relevance Test Anderson Canonical Correlations LR Statistic [9.78]*** [9.55]*** Cragg-Donald Wald Statistic [12.14]*** [11.86]*** Weak Instrument Test Shea Partial 0.0111 0.0109 F Test of Excluded Instrument [6.38]*** [6.16]*** Anderson-Rubin Wald test [0.41] [0.12] Stock-Wright LM S statistic [0.41] [0.12] Finlay-Magnusson Wald test [0.33] [0.03] Overidentification Test Hansen J statistic 0.135 0.004

Panel D, Second Stage of LIML Trade Credit 0.686 0.146 [1.202] [0.796] Included Control Variables Firm Characteristics Yes Yes GM Characteristics Yes Yes City Characteristics Yes Yes Industry Characteristics Yes Yes Number of Observations 1,170 1,111

Note: Robust standard errors, clustered at industry-city level, are presented in the round bracket. *, **, *** represent significance at 10%, 5%, 1% level respectively. The first Stage of TSLS contains same controls as the second stage but results of these control variables are not reported to save space (available upon request). In Panel D, the limited information maximum likelihood (LIML) regressions include the same control variables as those in the corresponding two-stage-least-squares (TSLS) regressions but results of these control variables are not reported to save space (available upon request).

Table 9: Robustness Check II, Sub-sample of Financially-constrained Firms


Dependent Variable Labor Productivity ROA Trade Credit -0.108 0.425 (0.710) (0.549)

Panel B, First Stage of TSLS: Dependent Variable is Trade Credit Delay 0.001 0.001 (0.001) (0.001) Relationship 0.372*** 0.370*** (0.129) (0.128)

Panel C, Various First-Stage Statistics Tests Relevance Test Anderson Canonical Correlations LR Statistic [6.86]** [6.82]*** Cragg-Donald Wald Statistic [12.06]*** [12.09]*** Weak Instrument Test Shea Partial 0.0140 0.0141 F Test of Excluded Instrument [5.74]** [5.69]** Anderson-Rubin Wald test [0.68] [0.57] Stock-Wright LM S statistic [0.64] [0.59] Finlay-Magnusson Wald test [0.02] [0.60] Overidentification Test Hansen J statistic 0.619 0.199

Panel D, Second Stage of LIML Trade Credit -0.132 0.426 (0.736) (0.550) Included Control Variables Firm Characteristics Yes Yes GM Characteristics Yes Yes City Characteristics Yes Yes Industry Characteristics Yes Yes Number of Observations 665 660

Note: Robust standard errors, clustered at industry- city level, are presented in the round bracket. *, **, *** represent significance at 10%, 5%, 1% level respectively. The first Stage of TSLS contains same controls as the second stage but results of these control variables are not reported to save space (available upon request). In Panel D, the limited information maximum likelihood (LIML) regressions include the same control variables as those in the corresponding two-stage-least-squares (TSLS) regressions but results of these control variables are not reported to save space (available upon request).

Table 10: Explanation I, Buyer Credit Effect


Dependent Variable Labor Productivity ROA Trade Credit 0.911 0.064 [1.212] [0.721] Buyer Credit -0.086 -0.030 [0.156] [0.127]

Panel B, First Stage of TSLS: Dependent Variable is Trade Credit Delay 0.001** 0.001** [0.001] [0.001] Relationship 0.219** 0.217** [0.093] [0.092]

Panel C, Various First-Stage Statistics Tests Relevance Test Anderson Canonical Correlations LR Statistic [9.56]*** [10.82]*** Cragg-Donald Wald Statistic [11.82]*** [13.64]*** Weak Instrument Test Shea Partial 0.0098 0.0097 F Test of Excluded Instrument [5.92]*** [5.74]** Anderson-Rubin Wald test [0.64] [0.20] Stock-Wright LM S statistic [0.63] [0.20] Finlay-Magnusson Wald test [0.56] [0.01] Overidentification Test Hansen J statistic 0.198 0.097

Panel D, Second Stage of LIML Trade Credit 0.921 0.064 [1.239] [0.722] Included Control Variables Firm Characteristics Yes Yes GM Characteristics Yes Yes City Characteristics Yes Yes Industry Characteristics Yes Yes Number of Observations 1,254 1,241

Note: Robust standard errors, clustered at industry-city level, are presented in the round bracket. *, **, *** represent significance at 10%, 5%, 1% level respectively. The first Stage of TSLS contains same controls as the second stage but results of these control variables are not reported to save space (available upon request). In Panel D, the limited information maximum likelihood (LIML) regressions include the same control variables as those in the corresponding two-stage-least-squares (TSLS) regressions but results of these control variables are not reported to save space (available upon request).

Table 11: Explanation II, Growth Potential Effect 1 2 Dependent Variable Labor Productivity ROA Trade Credit 0.120** 0.140* [0.051] [0.082] Average Labor Productivity in the past Three years 0.858*** 0.182*** [0.023] [0.057] Included Control Variables Firm Characteristics Yes Yes GM Characteristics Yes Yes City Characteristics Yes Yes Industry Characteristics Yes Yes Number of Observations 1,281 1,269 R-squared 0.8083 0.0466 F-test 297.61 3.67 p-value for F-test 0.0000 0.0000


Table 12: Explanation IV, Size Effect

1 2 3 4 Sample Large Firm Small Firm Large Firm Small Firm Dependent Variable Labor Productivity ROA Trade Credit 0.429*** 0.508*** -0.024 0.441** [0.118] [0.144] [0.073] [0.177] Included Control Variables Firm Characteristics Yes Yes Yes Yes GM Characteristics Yes Yes Yes Yes City Characteristics Yes Yes Yes Yes Industry Characteristics Yes Yes Yes Yes Number of Observations 662 664 656 657 R-squared 0.4004 0.2714 0.0535 0.0547 F-test 16.58 15.60 3.24 1.28 p-value for F-test 0.0000 0.0000 0.0000 0.0547


Table 13: Explanation III, Time Effect

1 2 3 4 Sample Expiration>90 Expiration <=90 Expiration >90 Expiration <=90 Dependent Variable Labor Productivity ROA Trade Credit 0.645*** 0.551*** 0.278* 0.014 [0.221] [0.165] [0.171] [0.135] Included Control Variables Firm Characteristics Yes Yes Yes Yes GM Characteristics Yes Yes Yes Yes City Characteristics Yes Yes Yes Yes Industry Characteristics Yes Yes Yes Yes Number of Observations 684 642 674 639 R-squared 0.3147 0.3742 0.0440 0.0437 F-test 26.50 20.53 1.32 2.85 p-value for F-test 0.0000 0.0000 0.0440 0.0437


Appendix: Survey Questions Regarding Key Variables

Variable Survey Question

Trade Credit Question F8: % of inputs (from this supplier) you buy on credit

Delay Question F9: If your main supplier of your 2 major inputs failed to deliver, how long would it take you to obtain replacement supplies?

Relationship Question F10: Do friends and relatives of the owners of your plant own any of the suppliers of your plant’s most important production materials?

Bank Loan Question L2: Do you have a loan from a bank or financial institution?

Government Representative in the Board

Question M7: Is the government (including state shareholding company) represented in the Board?

Education Question M1: What is the highest level of education completed by the General Manager?

Years of Being GM Question M3: How many years has the General Manager held this position?

Depute GM Before Question M3: Before becoming General manager in this firm, what was his/her position?

Government Cadre Question M3: Before becoming General manager in this firm, what was his/her position?

Party Membership Question M4: What’s the position of the General Manager in the party?

Government Appointment Question M6: How was the General Manager appointed?

Quality Question F11: What percentage of supplies you purchase are lower than expected quality?

Specificity Question F7: Is this input made to your unique specification?

Delivery Question F16: What percentage of sales in the last year were lost due to delivery delays from suppliers?

Credit Term Question L14: Regarding the repayment of trade credit (average over all your trade creditors for all inputs), average number of days before supplier will impose penalties

Most Important Client Question F8: Is your firm the most important customer of this supplier?

does trade credit boost firm performance?

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