Exports, Productivity, and Credit Constraints: A
Firm-Level Empirical Investigation of China
Zhiyuan Liy

Miaojie Yuz

August 25, 2009

Abstract
Recent Melitz-type (2003) intra-industry heterogonous trade models argue that a…rm’s productivity has signi…cant e¤ects on the …rm’s exports. This paper examines
how a …rm’s credit constraints as well as its productivity a¤ect its export decisions.
We imbed the …rm’s credit constraints into a Melitz-type general-equilibrium model byendogenizing the probability of the success of …rm-speci…c projects. We show that, allelse equal, it is easier for …rms to enter the export market if (1) the probability of thesuccess of their project is higher and consequently they have easier access to external…nance from …nancial intermediaries; or (2) they have alternative sources, other thanfrom …nancial intermediaries, to obtain funds. We test these theoretical hypotheses
using …rm-level data from Chinese manufacturing industries and …nd strong evidence
supporting the predictions of the model.
JEL: F1, F3, D9, G2
Keywords: Credit Constraints, Heterogeneous Firms, Productivity, Trade

We thank Robert Feenstra, Joaquim Silvestre, Deborah Swenson, Katheryn Russ, Tianshu Chu, Kenneth Clements, Charles Engel, Pinelopi Goldberg, Haiwei Tang, David Weinstein, Rod Tyers, and Yanrui
Wu for their very helpful comments. This paper is also bene…ted from seminar participants at the University of Western Australia, 2009 Annual conference of RCIE at the University of Washington, and the 11th
NBER-CCER annual conference. Miaojie Yu thanks Jun Zhang and Bo Shen for their excellent research
assistance. However, all errors are ours.
yDepartment of Economics, University of California, Davis, 95616 CA. Email: zhyli@ucdavis.eduzChina Center for Economic Research (CCER), Peking University, Beijing 100871, China. Phone:
86-10-6275-3109, Fax: 86-10-6275-1474, Email: mjyu@ccer.pku.edu.cn.

1Introduction

A widely accepted belief about a …rm’s heterogeneous export behavior is that a …rm withhigher productivity would generate greater revenue and thus be able to shoulder the …xed
costs of market entry (Melitz, 2003). This interpretation abstracts from …nancial frictions
that may arise from the …rm’s access to liquidity or to external …nance. In the presenceof …nancial frictions, however, …rms face di¤erent borrowing constraints, which could inturn a¤ect their capabilities to …nance the upfront entry costs. In addition, inward foreign
direct investment (FDI) may provide an alternative source of low-cost funds and alleviatecredit constraints faced by …rms. These possibilities raise the following questions: How
do …rms’credit constraints a¤ect their exports? Do credit constraints vary across …rms’
types?
This paper addresses the issue by providing a general-equilibrium Melitz-type (2003)
model to analyze the impact of a …rm’s heterogeneous external …nance capability on its
exports. There are two main innovations in our theoretical model. First, …rms’ export
projects have di¤erent probabilities of success. More importantly, the heterogeneous probabilities of success induce di¤erent capabilities of …rms to obtain external …nance from…nancial intermediaries. Secondly, di¤erent types of …rms have di¤erent abilities to obtainlow-cost funds from sources other than …nancial intermediaries. In particular, subsidiaries
of multinational corporations are able to receive …nancial support from their parent …rms
to bypass domestic credit constraints.
Inspired by the theoretical predictions, we estimate the e¤ect of a …rm’s credit constraints on its exports, using a very rich panel dataset of Chinese manufacturing …rms
over the period of 2000-2007. We obtain robust empirical evidence that …rms with fewer
credit constraints exports more, while controlling for the endogeneity of credit constraints.
Overall, one percentage increase in a …rm’s interest expenditures, which is used as a proxy
for the …rm’s capability to borrow, increases its exports by percentage, ceteris paribus. In

1addition, foreign-invested-enterprises (FIEs) in China export more and are less sensitive
to the availability of external …nance indicating that the presence of foreign capital does
alleviate the credit constraints faced by …rms. Finally, various sensitivity checks strongly
support our empirical …ndings.
This work adds to a small but growing literature on trade and …nance, including work
done by, among others, Qiu (1999), Chaney (2005), Manova (2008), Muûls (2008), andBuch et al. (2008). Inspired by Melitz (2003), Chaney (2005) …rst introduced liquidity
constraints into the heterogeneous …rms model and predicted that …rms with higher liquidity endowment would face fewer …nancial constraints and it would consequently be easierfor such …rms to enter the export market. As a pioneering work in the …eld, Chaney (2005)
theoretically focused only on the impact of a …rm’s internal funds on its exports. Then,
Manova (2008) took a step forward to consider …nancial contracts and asset tangibility
in a framework of …rm heterogeneity. She found that, using sector-level panel data ofbilateral exports, those industries in more …nancially developed countries are more likely
to export bilaterally and to ship greater volumes.
In addition, Muûls (2008) combined both liquidity endowments and external …nancial
contracts into a general equilibrium model and showed that the credit rating, the Coface
score from a credit insuring company, has signi…cant e¤ects on a …rm’s exports. However,Muûls’s theoretical model assumes equal cost of external …nance following Manova (2008).
That is, the repayment required for an identical principal is the same across all …rms. Last
but not least, Buch et al. (2008) investigated the impact of credit constraints on exports
and outward FDI, including …rm heterogeneity in borrowing costs in a partial equilibrium
framework. In retrospect, all these works have enriched our understanding of the impactof a …rm’s credit constraints on its export behavior.
Most of the previous studies assume that, other than productivity, …rms are di¤erent
only in terms of the principal amount they need to borrow. Once this amount is given,
there are no di¤erences in repayments. This stems from the assumption made by these2

studies that the default rates across …rms are the same. Our work introduces two more
sources of heterogeneous credit constraints. Project-speci…c risks lead to di¤erent borrowing capabilities and capital from foreign parent …rms causes …rms to be less dependent
on external …nance. These additional sources of credit constraints lead to a better …t toreality. Equally importantly, though some of the previous studies worked on …rm heterogeneity theoretically, they estimated their models only at the country and sector level, inpart due to data restrictions. In the present paper, we are able to test our theoreticalpredictions using disaggregated …rm-level data from China.
These …ndings are of special interest because China’s exports experienced unprecedented growth over the past decade whereas Chinese …rms faced severe credit constraints.
Over the period of 2000-2007, the annual growth rate of China’s exports was 25%. However, according to the World Business Environment Survey during 1999-2000 and theInvestment Climate Assessment surveys in 1999 and 2002, China was among the group ofcountries that had the worst …nancing obstacles (Claessens and Tzioumis, 2006). Our results suggest that easier access to external …nance through …nancial intermediaries and/or
FDI would make the engine of China’s exports more powerful. Figure 1 illustrates thispoint. During 2000-2007, FDI is positively associated with exports across Chinese cities asshown in Figure 1A whereas interest expenditures are positively associated with exports
across industries as shown in Figure 1B.
[Insert Figure 1 Here]The remainder of the paper is organized as follows. Section 2 describes mechanisms
by which a …rm’s credit constraints a¤ect its exports. Section 3 constructs a model introducing a …rm’s heterogeneous access to external …nance and the impact of external…nance impact on the …rm’s exports. Section 4 presents the econometric speci…cation anddata. Section 5 discusses the estimation results and provides robustness checks. Section 6
concludes the paper.
3

2

Exploring the Impact of Credit Constraints on exports

The role of a …rm’s productivity in its exports has been discussed in the literature. The
seminal work by Melitz (2003) highlights that the most e¢ cient …rms export and earn
extra pro…ts abroad. Less e¢ cient …rms can only serve the domestic market since theentry to the foreign market would generate losses due to …xed entry costs. In contrast,
the least e¢ cient …rms die and exit from the market.
Inspired by pioneering works by Chaney (2005) and Manova (2008), in this paper
we take a step forward to identify that a …rm’s credit constraints are one of the mostimportant channels through which a …rm’s productivity a¤ects its exports.
Firms have heterogeneous capabilities in obtaining external …nance. Firms with higher
productivity would be more capable of obtaining loans. The intuition is straightforward.
First, the more e¢ cient …rms are expected to have higher probability of success in export
projects. Thus for the same size loan and the same amount of repayment, …nancial intermediaries are more likely to extend loans to …rms with higher productivity because it
is more likely that the intermediaries will be repaid. With projects with low probabilityof success, investors expect higher risks and thus are reluctant to extend loans to theseprojects. Incapable of obtaining external …nance, less e¢ cient …rms would face more severe credit constraints and might be prevented from entering the export market, or they
have to export less if they also face credit constraints in …nancing variable costs as well.
In addition to borrowing from …nancial intermediaries, …rms are able to …nance internally with their domestic pro…ts. Aside from this, one interesting observation is thatthe …rm’s type also plays a role to its access to alternative sources of …nance. One good
example is that foreign-invested-enterprises (FIEs) in China, compared with non-FIEs,
may have easier access to funds from their parent …rms.1 As a result, FIEs are expected1

Héricourt and Poncet (2009) shows that inward foreign direct investment in China plays an importantrole in alleviating domestic …rms’credit constraints. Similar evidence is provided by Harrison and McMillan(2003) using data from the Ivory Coast.

4

to export more given all other constants. Indeed, the share of exports by FIEs in China
increased from 48% in 2000 to 57% in 2007. The average annual growth rate (i.e., 29.6%)
is higher than that of China’s total exports (i.e., 25%) during 2000-2007.
In summary, all else equal, it is easier for …rms to enter the export market if they
have higher productivity, easier access to external …nance from …nancial intermediaries,
or easier access to alternative sources of funds.
In this paper, we …rst theoretically show that …rms that have higher probabilities ofsuccess probabilities, especially FIEs, are less …nancially constrained and thus less likely tobe prevented from exporting. The intuition is that, when …rms need to borrow from …nancial intermediaries, the low probability of success leads to high repayment requirements by
the intermediaries to cover investment risks. This in turn increases the export threshold.
Therefore, …rms that otherwise are productive enough to export could be prevented fromexporting due to higher requirements for repayment. FIEs that have access to alternative
funds from their parent …rms need to borrow less and are less sensitive to external …nance.
In this sense, FIEs are less credit constrained.
We evaluate the importance of access to external …nance to …rms’export behaviors by
testing two hypotheses: (1) Firms for which it is easier to borrow from …nancial intermediaries export more; and (2) FIEs export more and are less sensitive to the availability of
external …nance from …nancial intermediaries. By choosing …rms’interest expenditures as
a proxy for …rms’external …nancing capabilities, we are able to explore the nexus amonginterest expenditures, access to foreign capital, and exports. Equally importantly, we also
pin down the e¤ect of foreign capital access on exports through the channel of credit constraints. All the estimation results have the expected sign, are statistically signi…cant andare robust to di¤erent speci…cations.
Finally, we address the endogeneity issue of the e¤ect of the …rm’s exports on its
interest expenditures. The main idea is that, with more exports, …rms need more upfront…xed costs. We choose the scale-weighted money supply in the previous period as an
5instrument of interest expenditures. Various statistical tests con…rm that the instrument
is well justi…ed. After controlling for endogeneity problems, interest expenditures areshown to have an even greater impact on …rms’exports.
Motivated by these observations, in the next section we develop a general equilibrium
model aimed at capturing the impact of a …rm’s credit constraints on its exports.

3

The Model

3.1

Domestic Demand and Production

The economy consists of two countries, Home and Foreign (henceforth foreign counterpartsof the variables are denoted with an asterisk ). Labor is the only input factor for production. The population is of size L at home. There are two sectors. One sector produces asingle homogeneous good that is freely traded. Production in this sector is characterizedby constant returns to scale with q0 = wl0 where l0 is the labor used to produce quantity q0of the good. Thus labor productivity in this sector determines the wage level, w at home.
We assume that both countries produce in this sector and that wages are thus …xed by
the productivity in this sector. The second sector produces a continuum of di¤erentiatedgoods as in Melitz (2003). Each …rm supplies one of these goods and is a monopolist ofits variety.
As in Melitz (2003), consumers are endowed with one unit of labor and the preference over the di¤erentiated good displays constant elasticity of substitution. The utility
function of the representative consumer is
U = q01

0
@

Z

q(!)

!2

where ! denotes each variety and

1

1

d! A

1

;

is the set of varieties available to the consumer. The

utility has a Cobb-Douglas form with share 1

on the numeraire sector. The constantelasticity of substitution across each variety in the non-numeraire sector is denoted by

6

(> 1). The aggregate price index at home is thus
0
1 1
1
Z
1
;
P =@
p(!)
d! A
!2

where p (!) is the price of each variety. Accordingly, the demand for each variety isq(!) = wLp(!)
P1

r(!) = wLp(!)
P

and the revenue of each …rm is1;

where wL is the total expenditure on the di¤erentiated good at home.
Firms face a …xed cost of entering the domestic market (Cd ), which can be fully …nanced
by using their internal liquidity. The cost function of serving the domestic market is
cd (qd ) = qd

w
+ wCd ;
xwhere x is the productivity of the …rm. As is common for monopolistic competition, each…rm sets the domestic price, pd , with a constant mark-up

1 over the unit costw:
1x

pd (x) =Therefore, …rms generate pro…ts at home:d (x) =rd (x)

wCd =

w
1 xP

wL

1

wCd ;

where rd (x) is the …rms’domestic revenues. In order to survive in the domestic market,…rms must have high enough productivity so that they can make positive pro…ts. The
cut-o¤ productivity level, xd , is determined by the zero pro…ts condition,d (xd ) = 0;

or
xd =

w
1P
7

Cd
L

1
1

:

(1)

3.2

The Decision to Export

When a …rm wants to export, the upfront …xed cost, wCE , can be …nanced internallyusing a fraction, d, of its domestic pro…tsd.

Firms could also use a certain amount ofzero-interest, zero-collateral funding from the parent …rm if the …rm is a FIE. Finally, they
could raise funds externally from …nancial intermediaries.
For simplicity, we assume that FIEs’parent …rms provide them a fraction of the …xed
costs of the exports without interest,
The parameter2
i w CE , where w CE is the …xed cost of exports.

i , 8i = F; N F , is the fraction and F; N F

respectively. We assume that

F

>

NF

are the FIEs and non-FIEs,= 0 given the fact that non-FIEs do not havealternative source of funds. If the project is successful, …rms pay back this speci…c loan.
Otherwise, the loss will be covered by the parent …rms.
When …rms borrow funds from …nancial intermediaries, they face di¤erent costs. First,
the export project is subject to …rm-speci…c risk and the probability of success,[0; 1], is public knowledge.

(x) 2(x) is assumed to be an increasing function of a …rm’s

productivity x.3 Firms must o¤er investors a repayment, GE (x), such that investors
can be breakeven. The minimum level of the repayment …rms can o¤er depends on the
project’s probability of success.
The external funds lent by investors require pledgeable collateral in case that the
project fails. The collateral that a …rm can provide is a fraction of the domestic …xed cost,
twCd , where wCd is the domestic …xed cost and t is the fraction of the …xed cost that ispledgeable. The …rm will never default on the repayment if the project is successful. If
the project fails, investors could get back only the collateral.
Financial contracting proceeds as follows. In the beginning of each period every …rm
makes a take-it-or-leave-it o¤er to a potential investor. This contract speci…es the amountthe …rm needs to borrow, the repayment GE in case the project is successful, and the2

For example, setting up a distribution network in foreign country requires foreign labor.
The assumption that the probability of success is a function of productivity is only for the sake oftheoretical simplicity. In reality, the probability of success of a project might be related to other variables.
3

8

collateral in case of failure. Revenues are then realized and the investor receives payments
at the end of the period.
The …rms maximize their expected export pro…ts, subject to four constraints:
E ( E (x)) =

(1
s.t.

qE (x) wx(x) pE (x)qE (x)kE )w CEGE (x)(x))twCd(x)GE (x) + (1(x))twCd = kE w CE

qE (x) w(1xd d (x) + i w CE
= 1 kE
w CE
w L pE (x)qE (x) =P 1
pE (x)qE (x)

where

(1

is the iceberg transportation cost, 1

kE )w CE(2)
GE (x)

kE is the …rm-speci…c fraction of the …xed

cost that could be …nanced internally or through loans from parent …rms of FIEs.
The …rst constraint is the investor break even equation, stating that investors are
perfectly competitive so they receive zero pro…ts. The second constraint indicates that,
if the project is successful, the …rm must have enough net revenue to pay the repayment
GE (x) to the investors. Notice that if expected exports pro…ts, E ( E (x)), are greaterthan or equal to zero, this constraint is not binding. The third constraint speci…es the
portion of funds that need to be externally …nanced. Firms could use a fraction of domestic
pro…ts and loans from parent …rms, if any, to cover part of …xed costs of the exports .
Aside from these, the leftover, kE w CE , must be …nanced externally. The last constraint
is the derived demand function.
This maximization problem thus provides the sources of credit constraints. First,
in order to export and make a pro…t, …rms must have the capability to generate large
enough revenue so that they can o¤er a large enough repayment to investors. With arepayment that would allow investors to gain more than the break-even amount, investors
would choose to extend …nancing to the …rms. If the …rm’s productivity is too low, it
9cannot generate large enough pro…ts and hence cannot o¤er repayments that will allow
the investors to break even, and thus they will not get any …nancing. Secondly, for thesame level of repayment, investors are more likely to extend …nancing to …rms that havelarger probabilities of success because it is more likely that they will be repaid. In sum,
the easier for the …rm to get external …nancing, the more likely that the …rm enters theexport market.
The investor’s break-even equation determines the repayment that investors demand:
GE (x) = twCd +1(kE w CE(x)

twCd ) :(3)

We substitute (3) into …rm’s expected pro…ts
E ( E (x)) = (x) pE (x)qE (x)qE (x) w
x

w CE

(1

(x))kE w CE :This equation indicates that as long as …rms need to borrow from investors, i.e., kE > 0,they must have higher expected pro…ts to survive in the export market. The extra cost due
to credit constraints depends on the amount they borrowed (kE w CE ) and the probabilityof success of the project ( (x)).
The expected pro…ts maximization problem is thus equivalent to maximizingpE (x)qE (x)qE (x) w
x

1
(x)

w CE

1 ((1

i ) w CEd d (x)) :(4)

By de…ning the operating export pro…ts as
E (x)pE (x)qE (x)qE (x) w
xw CE ;(5)

the maximization problem is in turn equivalent to maximizing the operating exports pro…ts
since productivity, domestic pro…ts, and the fraction of …xed costs covered by parent …rms
are predetermined from the perspective of the …rms when they make export decisions.4
4

An implicit assumption is that a …rm’s productivity leads to exports. That is, good …rms export,
which is a common assumption for all Melitz-type(2003) models.

10Consequently, the results of Melitz (2003) such as the optimal price pE (x), the quantity qE (x), the operating exports pro…ts

E (x), and revenue rE (x), hold for the active

exporters:5w
1 x

pE (x) =w
1 x

qE (x) =E (x) =rE (x) =rE (x)w L
P 1

w CE =

w L

w CE

1

w
1 xP

w L

1

w
1 xP

Firms might or might not be restricted by credit constraints. If …rms have high enoughproductivity so that they can generate high enough domestic pro…ts, or if loans from parent…rms are high enough, so that there is no need for externally raise funds, …rms are notsubject to credit constraints.
Without credit constraints, the threshold productivity level of the exporter, xE , isdetermined by

E (xE ) = 0, or

xE

1

=

1

1
1P

1
CE
L ( w)1:(6)

Firms might face credit constraints. Firms with productivity level x such that(1i ) w CEd d (x) > 0

need to borrow money externally in order to enter the exports market. Within these …rms,
only a subset could successfully enter the export market by making a positive expectedexport pro…ts. Note that if d ! 0, all …rms that want to export must raise funds externally.
The capability of …rms to secure external loans determines whether or not the …rm canexport. In order to make positive expected pro…ts, …rms must have a productivity level x
5Notice that exporting …rms sell in the foreign market and thus the foreign price level (P ), rather thanthe domestic price level, is present in the solutions.

11such that
1
(x)

E (x)

1 [(1

i ) w CEd d (x)]0

to survive the export market. Consequently, the cut-o¤ productivity of exporting …rms
that are subject to credit constraints is determined by
1(xCE )

E (xCE )1 [(1i ) w CEd d (xCE )] = 0:

(7)Compared to the cuto¤s without credit constraints in Equ.(6), credit constraints virtually"increase" the …xed costs of exports for marginal …rms, and hence the di¢ culty of entering
the market increases. Solving this equation, the cuto¤ productivity for …rms subject tocredit constraints is:6
1
1

xCE =

i+

+

1
(xCE )

1
w L

w
P

1

(xCE ) i ) w CE+
(xCE )

(1

w
1 dwL
P

1

1
(xCE )

1
1

1 dwCd

1
1

:

(8)

Firms with productivity below xCE cannot export due to credit constraints, although someof them are su¢ ciently productive to do so pro…tably without credit constraints.

3.3

The Equilibrium

To examine the equilibrium, we assume that the price level only depends on domestic…rms’prices and that foreign …rms do not face any credit constraints by following Chaney(2005) and Muûls (2008). This assumption indicates that

P

0
@

Z

1 1
1

1

pd (x)x>xd

LdF (x)A

where L is the home population and F (x) is the cumulated density function of the …rm’s
productivity at home. Given that the cut-o¤ productivity level has an implicit form, we
6

See the appendix for details.

12

de…ne the function h( ) by:1h( ) : x

0

=@

Z

x

x>x

1

1

dF (x)A C () x = h(C)with h0 > 0. Assuming that the distribution of productivity is the same in the foreigncountry as at home, F (x) = F (x), the function h( ) will be the same for the foreigncountry.
It turns out that the cuto¤s in equations (1), (6), and (8) are solved as:7
xd = h(Cd );
xE =

CE
Cd

xCE =

1

(9)
1
1

w
h(Cd );
w

(10)

(1

w CE
+ (1
i + (xCE ) i ) w

w
w(xCE )Cd h1(Cd ) + (1(xCE )) dCd(xCE )) dCd h1

where Equ.(11) indicates that xCE is an implicit decreasing function of

(Cd )

! 1

1

; (11)

i , which is denoted

as xCE ( i ).
From solutions (10) and (11), it is easy to show that xCE = xE provided that
Instead, if= 0, then xCE ( i ) is the solution of equation (1= 1.

i ) w CE = d d (x), de…ned

as xN EF , where N EF stands for "no external …nance". This is because xN EF is the cuto¤
productivity level with which …rms’domestic pro…ts and parent …rms’loans are su¢ cient
to …nance their …xed costs to export. That is, …rms with productivity above this level do
not need any external …nance. It is worth noting that when d ! 0 as in Manova(2008),
xN EF ! 1. In this case, all …rms need external loans in order to export. The capabilityof obtaining loans determines the capability of …rms to export.
When

2 (0; 1), xCE ( i ) is in between of xE and xN EF . From this fact, we can identify

the su¢ cient condition such that there are some potential exporting …rms prevented fromexporting as stated in the following proposition.
7

See the appendix.

13

Proposition 1 If x is continuously distributed from [0; 1], and if
(1

1

w Cd
C
+ d
i)
dwCd CE1h(Cd )h(Cd )>w
w

then there exists a non-empty set of …rms which are prevented from exporting even though
they are pro…table enough to do so if without credit constraints.
Proof. This proposition can be easily proved by substituting equation (11) and (10)into inequality xE < xCE ( i ) and making= 0.

Proposition 1 suggests that, when the fraction of domestic pro…ts used to …nance the…xed costs to export is close to zero (d ! 0), then there are always …rms that are prevented
from exporting. This is very realistic because …rms might not be able to use a large portionof domestic pro…ts to …nance export projects. One example of this is that in the presenceof principal-agent problems, stockholders may demand dividends at the end of each period
instead of entrusting management with the utilization of retained earnings. This indicatesthat the capability of obtaining external …nance is essential in determining whether or not…rms could export.
Figure 2 illustrates …rms’exporting behaviors. The vertical axis is the expected export
pro…ts and the horizontal axis is the productivity. The EM curve is the expected export
pro…ts curve if there is no …nancial friction, as in Melitz(2003). Firms with a productivity
level greater than xE can pro…tably export if there is no credit constraint. However, when
there are …nancial frictions, some of these …rms can no longer export because they cannot
obtain external loans to …nance the …xed costs to export. Curves EF and EN F are theexpected exports pro…ts curves for FIEs and non-FIEs, respectively. Thus, potential FIE
exporters and potential non-FIE exporters that are prevented from exporting are …rms
with productivity levels in the range of [xE ; xCE ( F )] and [xE ; xCE ( N F )] respectively.
These …rms cannot o¤er large enough repayments to investors and consequently cannot
obtain external loans. Thus, they are prevented from exporting. Instead, FIEs and nonFIEs with productivity levels greater than xCE ( F ) and xCE ( N F ), respectively, are able14

to obtain external loans and enter the export market. Firms with productivity levels
greater than xN EF have su¢ cient domestic pro…ts to …nance the …xed costs to export
internally and need not borrow from investors. However, as noted above, if the fraction ofdomestic pro…ts that can be used to …nance an export project is su¢ ciently small, then allexporting …rms are subject to credit constraints. Firms’capabilities of obtaining externalloans then determine their capabilities of exporting.
[Insert Figure 2 Here]
Therefore, our model predicts that …rms face di¤erent levels of credit constraint. The
credit constraints are related to …rms’previous domestic pro…ts. They a¤ect the amountof internal liquidity that can be used to …nance the entry costs to export as suggested
by Chaney (2005). Aside from this, there still exist other important channels that a¤ect…rms’credit constraints.
First, …rms with higher productivity are more capable of obtaining external loans.
Higher productivity …rms would generate larger export revenues and thus they would
be able to o¤er a larger repayment for the same amount of principal. Thus they areless credit constrained. More importantly, the higher productivity introduces the higher
probability of success of the projects. As a result, investors are more likely to break evenwhen they extend loans to more productive …rms, given the level of the principal and therepayment. Investors thus would be more inclined to extend loans to more productive…rms. Less productive …rms are then prevented from entering the export market. Again,
the capability of obtaining external loans is the key to exporting in the presence of …nancial
frictions.8
Secondly, our model clearly suggests that the …rm’s type plays a role in its accessto alternative funds, which in turn a¤ects the …rm’s export decision. Since xCE ( i ) is a8

Of course, in reality, a …rm’s capability of raising funds externally may be a¤ected by other factors
such as automatic customer insolvency, bad debts, overdue accounts, commercial risks, and even politicalrisks. However, the key lesson is that …rms that are capable of obtaining external loans will be able toexport.

15

decreasing function of

i , …rms that have atlternative channels (e.g., FIEs get loans fromtheir parent …rms) to obtain low-cost loans would depend less on external …nancing from…nancial intermediaries and thus have lower requirements of productivity levels to enter
the export market. Given that

F

>

NF

as assumed, FIEs might be able to export

more compared to non-FIEs even when they both have the identical loans from …nancial
intermediates, while otherwise they would be prevented from exporting. In sum, we makethe following proposition:
Proposition 2 All else equal, it is easier for …rms to ente the export market if (1) they
have higher probability of success of the project and consequently easier access to external…nancing from …nancial intermediaries; or (2) they have alternative sources, other thanfrom …nancial intermediaries, to obtain funds.

4

Econometrics, Data, and Measures

In this section, we …rst discuss the empirical speci…cations, followed by a description of
the dataset used in the paper. We then present the results and address the possible
endogeneity problem. Finally, we close the section with various robustness checks.

4.1

Empirical Spec…cation

Our theoretical model introduced above clearly predicts that it is easier for …rms withhigher probability of success to get loans from …nancial intermediaries and it is thus easierfor them to enter the export market. Put another way, the model predicts that …rms withlarger interest expenditures, an index of their capability of obtaining loans, would export
more. In addition, …rms with higher productivity also export more. We therefore consider
a speci…cation as follows:
ln EXit =0+

1 ln IEit +2 ln T F Pit 1 ++ 4 F IEi ln IEit +3 F IEi

5 ln Dprofit 1 +16

Xit + & i + #t + it ;(12)

where i is the …rm and t denotes year, ln EX, ln IE, and ln T F P are the …rm’s log
exports, log interest expenditures, and log total factor productivity, respectively. F IEi
is a dummy variable, equal to a unit if …rm i is a foreign-invested-enterprise and zerootherwise. Interest expenditures, TFP and FIE status all are predicted to contribute toexports. Thus coe¢ cients,

1,

2 and

3 , all are expected to be positive. The coe¢ cientof the interaction term of F IE and ln IE captures the di¤erential impact of interest
expenditures on exports for di¤erent types of …rms. The model suggests that FIEs areless dependent on external borrowing. Thus, ^ 4 is expected to be negative.
Firms might also be able to …nance the …xed costs to export using domestic pro…ts. In
order to capture this, we also include in the …rms’log domestic pro…ts (ln Dprof ). Since
domestic pro…ts as well as productivity are both predetermined before the …rms makeexport decisions, we include values for the previous period for ln T F P and ln Dprof in
the empirical speci…cation (12).9 In contrast, current period interest expenditures a¤ect…rms’exports, Thus, we use its current realization in the empirical model.
In addition, X denotes other control variables. In particular, traditional wisdom saysthat SOEs in China play a signi…cant role on its economy, although this is not formallyspeci…ed in the theoretical model. To shed light on this point, we also include a dummy
for SOEs and its interaction term with interest expenditures, SOEi ln IEit , in the model.
Finally, all other unspeci…ed factors are absorbed in the error term, which can be
decomposed into the three following components: (1) …rm-speci…c …xed e¤ects & i to control
for time-invariant factors; (2) year-speci…c …xed e¤ects #t to control for …rm-invariant
factors; and (3) an idiosyncratic e¤ect

itwith normal distributionits N (0;2 ) toicontrol for other unspeci…ed factors. The impact of credit constraints on exports is thefocus of this paper; thus the coe¢ cient of

1 is our main interest.

9This setup also enjoys an extra advantage that we do not need to worry about the reverse causality ofexports and productivity in the estimations.

17

4.2

Data

The sample used in this paper comes from a rich …rm-level panel dataset which covers morethan 160,000 manufacturing …rms per year for the years 2000-2007. The number of …rms
doubled from 162,885 in 2000 to 336,768 in 2007. The data are collected and maintainedby China’s National Bureau of Statistics in an annual survey of manufacturing enterprises.
It covers two types of manufacturing …rms: (1) all SOEs; (2) non-SOEs whose annual sales
are more than …ve million yuan.10 The dataset includes more than 100 …nancial variables
listed in the main accounting sheets of all these …rms.11
Although this dataset contains rich information, a few samples in the dataset are noisy
and misleading due, in large part, to the misreporting by some …rms.12 For example,
information on some family-based …rms, which usually did not set up formal accounting
systems, is based on a unit of one yuan, whereas the o¢ cial requirement is a unit of
1,000 yuan. Following Je¤erson et al.(2008), we clean the sample and rule out outliers byusing the following criteria: …rst, observations whose key …nancial variables (such as total
assets, net value of …xed assets, sales, gross value of industrial output) cannot be missing;otherwise, they will be dropped. Secondly, the number of employees hired for a …rm mustnot be less than 10 people13 .
Following Cai and Liu (2009), and guided by the General Accepted Accounting Principles (GAAP), we delete observations if any of the following rules are violated: (1) the
total assets must be higher than the liquid assets; (2) the total assets must be larger than
the total …xed assets; (3) the total assets must be larger than the net value of the …xed
assets; (4) a …rm’s identi…cation number cannot be missing and must be unique; and (5)10Indeed, aggregated data on the industrial sector in the annual China’s Statistical Yearbook by the
Natural Bureau of Statistics (NBS) are compiled from this dataset.
11
Following Levinsohn and Petrin (2003), plants were treated as …rms. In the present paper, we do not
capture scope economics due to their multi-plant nature. This remains a topic for future research.
12
Holz (2004) o¤ers careful scrunity on possible measurement problems in Chinese data, especially on
the aggregated level.
13
Levinsohn and Petrin (2003) suggest covering all Chilean plants with at least 10 workers. Here, we
follow their cretirion.

18

the established time must be valid. In particular, observations in which the opening year
is after 2007 or the opening month is later than December or earlier than January are
dropped as well.
Since FIEs as well as SOEs play a signi…cant role in our model, it is an advantage to takea careful look at these …rms. We …rst construct a dummy for foreign-invested …rms (F IEs)
to distinguish domestic from non-domestic …rms. Here, we consider a broad classi…cationof FIEs. In particular, the FIEs include both foreign …rms and Hong Kong/Macao/Taiwan(H/M/T)-owned …rms.14 In a robustness check, we consider a narrower classi…cation of
FIEs by excluding the H/M/T-owned …rms. Turning to the dummy for SOEs,15 to avoid apossible "adverse selection" problem faced by small SOEs in borrowing external loans, we
use "above-scale" SOEs as a default sample by dropping SOE observations whose operation
scales are smaller than the "above-scale" threshold. In particular, the observations are
dropped if any of following indicators is lower than 5 million yuan: (1) the value of the…rm’s sales; or (2) the value of the total assets; or (3) the value of the …xed assets. In
robustness checks, we include all SOEs.
After this very rigorous …lter, we obtain a sample of 1; 294; 596 observations from theoriginal sample of 1; 898; 958, which accounts for 69.2% of the original dataset.16 All
nominal terms are originally measured in current Chinese yuan. We therefore use theproducer price index (PPI) by sector, which is measured at the two-digit Chinese industrial
classi…cation and obtained from the National Bureau Statistics, as the GDP de‡ator bychoosing year 2000 as the base year.
14

Speci…cally, FIEs include the following …rms: foreign-invested joint-stock corporations (code: 310),
foreign-invested joint venture enterprises (320), fully foreign-invested enterprise (330), foreign-invested
limited corporations (340), H/M/T/ joint-stock corporations (code: 210), H/M/T joint venture enterprises(220), fully H/M/T-invested enterprises (230), and H/M/T-invested limited corporations (340).
15
By de…nition, SOEs include …rms such as domestic SOEs (code:110); state-owned joint venture enterprises (141); state-owned and collective joint venture enterprises (143); and state-owned limited corporations.
16
Including small- and medium- sized SOEs whose scales are lower than the threshold would increase
our sample to 1,401,569, which accounts for around 73.8% of the original size of the dataset.

19Panel A of Table 1 provides some basic statistical information about the Chinese …rm
data. After the …lter, the number of "above-scale" FIEs is 273; 160, which accounts for
21.1% of the full samples. In particular, the number of FIEs excluding H/M/T-owned…rms is 132; 826, 10.2% of all "above-scale" …rms. The remaining 79% are domestic …rms,of which 9.1% are SOEs and 5.8% are "above-scale" SOEs.
[Insert Table 1 Here]In the sample, some …rms export while some do not. Only 360; 106 …rms have positive
exports, which accounts for 27.7% of all "above-scale" …rms. However, 880; 026 …rms, a
67.3% of all …rms, have positive interest expenditures. We plot the logarithm of exports
against the logarithm of interest expenditures at the SIC 2-digit industrial level as shownin Figure 1B. We can clearly observe a positive correlation between exports and interest
expenditures across sectors. It seems di¢ cult to …nd obvious outliers in the scatter plots.
This suggests that the positive correlation between the two is unlikely due to outliers.
Finally, a …rm’s domestic pro…ts are measured as the di¤erence between the …rm’s
overall pro…ts and its export value. A …rm’s productivity is measured by using both totalfactor productivity (TFP) and labor productivity. In particular, the measure of TFP,
by de…nition, requires that …rms’ output be measured by real but not nominal terms.
Following Amiti and Konings (2007), we therefore use China’s PPI at the SIC 2-digit level
as the de‡ator to convert data from nominal terms to real terms. To be consistent withother data, the base year of the de‡ator is also chosen as 2000.

4.3

Measures of TFP

As one of the most important control variables in our regressions, it is essential for us tomeasure TFP precisely. However, many existing works on measuring TFP show that it isimprecise and biased (Amiti and Konings, 2007).
TFP is usually measured as the Solow residual, de…ned as the di¤erence between theobserved output and its …tted value calculated via OLS. However, this method su¤ers from20

two biases: a simultaneity bias and a selection bias. The …rst bias comes from the fact thata pro…t-maximizing …rm would readjust its input decision as a response to productivity
shocks that are observed by …rms but not by econometricians. Second, all …rms covered in
the samples are those that have relatively high productivity and survived during the period
of investigation. Those …rms that had low productivity, shut down, and left the market
were not observed nor included in the sample. Put another way, the sample covered in
the regressions is not randomly selected. Hence, all related estimates would su¤er from aselection bias.
To overcome these two empirical challenges, we use the augmented Olley-Pakes (1996)
approach to estimate and calculate the …rms’TFP. Given China’s WTO accession in 2001,
which was a positive demand shock for China’s exports, we also include a WTO dummyin the Olley-Pakes estimations to capture the e¤ect of the WTO accession. We report thedetailed estimation procedure in Appendix B.
Appendix Table B also presents the estimated survival probability of the …rms in thenext year by industry at the SIC 2-digit level. Its mean of 0.993 suggests that the problem
of …rms’exiting is not so severe during this period. The rest of Appendix Table B reports
the di¤erence in the estimated coe¢ cients for labor, material, and capital by using a
regular OLS approach and the OP methodology. We cover 36 manufacturing industries
coded from 6 to 46, according to China’s new industrial classi…cations (GB/T4754), whichwere adopted in 2002.17 Compared to the original …rm-level dataset, four industries are
not covered after this …lter process.18
17

Firm data before 2002 were clustered into industrial data by adopting the old industrial classi…cation.
We concord such data so that they are consistent with data after 2002.
18
The …ve industries dropped include extraction of petroleum and natural gas (7), mining of other ores
(11), processing of food from agricultural products(12), and recycling and disposal of waste (43).

21

5Empirical Results

5.1

Main Estimation Results

Table 2 presents the estimation results for equation (12). To consider the e¤ect of a …rm’s
interest expenditures on its exports, we …rst run a simple OLS regression of …rms’exports
on …rms’ log interest expenditures, log TFP in the previous year, and FIE dummy as abenchmark. The estimated coe¢ cient ^ 1 in equation (12) is :290, which is signi…cant at
the conventional statistical level. This suggests that a one percentage increase in a …rm’s
interest expenditures is associated with a :290 percentage increase in its exports. The
benchmark …nding is consistent with the simple cross-sectional plot in Figure 1B. The
positive e¤ect of TFP is also consistent with the previous …ndings like those of Bernard
and Jensen (1999) that …rms with higher productivity export more.
In Column (1), we also observe that FIEs are shown to export more than non-FIEs.
However, the …nding that FIEs have more exports could be due to their quick learning,
better technology adoption, or higher quality inputs (Amiti and Konings, 2007), or evensimply because they are established to export per se. However, FIEs might export morebecause of their easier access to low-cost loans (e.g., …nancial support from their parent…rms). If this is correct, FIEs should be less sensitive to external …nance. Similarly, it
is also interesting to ask whether SOEs export more compared to non-SOEs and check
whether or not they are sensitive to external …nance.
We then consider a speci…cation including four more variables: the SOEs dummy, theinteraction terms between FIEs and the log interest expenditure (F IEi
between SOEs and log interest expenditures (SOEiln IEit ) and

ln IEit ), and the …rm’s log domestic

pro…t in the previous period (ln Dprofit 1 ). The OLS estimation results show that theinteraction term between FIEs and interest expenditures is negative, which suggests thatexports of FIEs, compared to those of non-FIEs, are less sensitive to external …nance.
The variable of FIEs itself is positively correlated with exports as expected. In addition,

22

the signi…cantly positive sign of a …rm’s domestic pro…ts is consistent with the prediction
suggested by the theoretical model: …rms with high predetermined domestic pro…ts do notsu¤er from credit constraints as much and hence they export more.
In addition, the signi…cant negative sign of SOEs suggests that SOEs, compared tonon-SOEs, do not export more. There exist several possible reasons for this …nding. First,
China’s SOEs may su¤er from an ine¢ cient incentive mechanism (Lin, 2003). Secondly, theSOEs enjoyed less preferential treatment in access to external …nance after China’s WTO
accession in 2001 (Bajona and Chu, 2004). Accordingly, SOEs have lower productivity
and export less.
[Insert Table 2 Here]
Aside from the variables introduced above, other variables may a¤ect a …rm’s exports
as well, though they are not speci…ed in the theoretical model for simplicity. Such variables
include time-varying but …rm-insensitive factors like Chinese yuan (RMB) appreciation.19
Similarly, some other unspeci…ed time-invariant but …rm-varying factors like a …rm’s location may also have impact on the …rm’s exports. To control for such factors, we include
both year-speci…c and …rm-speci…c …xed e¤ects in the estimations.
Columns (3) and (4) of Table 2 report the …xed-e¤ect estimation results. Since thedummies of FIEs and SOEs are time invariant, they are automatically dropped from theestimations. In all speci…cations, the …rm’s TFP is shown to be signi…cantly positively
associated with its exports. The key coe¢ cients ^ 1 are all positive and signi…cant at theconventional statistical levels. In terms of economic magnitude, the coe¢ cients of the…rms’interest expenditures in Columns (3) is much smaller than the coe¢ cients in Column (1). However, after including more control variables, ^ 1 in Column (4) is close to
its counterpart in Column (2). Aside from this, most other control variables in Columns(1)-(4) have the expected signs, though they are insigni…cant. We suspect that the in19

Chinese yuan was revaued against the US dollar by around 20% during 2005-2007.

23

signi…cance is due to the lack of a control for the endogeneity of interest expenditures,
which will be handled shortly.

5.2

The Zero Trade Problem

As mentioned above, more than 73% of …rms had zero exports in our sample. Recent works
like those by Santos Silva and Tenreyro (2006), Helpman et al. (2007), and Yu (2009) haveargued that OLS estimates can lead to serious biases due to zero trade volume. The loglinearization of …rms’ exports may cause some bias since the entire portion of the datawith zero trade is dropped. In addition, the zero trade problem in our study could re‡ect
the rounding down of …rms’exports, which creates an endogenous censoring problem.
To address the empirical challenges raised by the zero trade problem, Santos Silva
and Tenreyro (2006) proposed a truncated Poisson pseudo-maximum likelihood (PPML)
estimation to address the zero trade problem. We therefore perform the PPML …xed-e¤ects
estimation by adopting …rms’exports EXi directly as the regressand. As shown in Column(5) of Table 2, the coe¢ cients of interest expenditures and TFP are still signi…cantly
positive in the PPML …xed-e¤ects estimates. Compared to the OLS …xed-e¤ects estimatesin Column (4), the coe¢ cient of the predetermined domestic pro…ts turns out to be highly
signi…cant with the expected sign. The interaction term between interest expenditures
and SOEs turns to be signi…cantly negative. However, the interaction term between FIEs
and interest expenditures has a strikingly positive sign. We again suspect that this is dueto the lack of consideration of the endogeneity of interest expenditures. We now turn tothis issue.

5.3

Endogeneity Issues

A …rm’s interest expenditures are not exogenously given, but a¤ected by its exports.
With more exports, …rms need more upfront …xed costs (e.g., …rms need to have a greater
distribution network abroad when its exports increase), which in turn require …rms to
obtain more debt to do business. One needs to control for the endogeneity of interest
24expenditures in order to obtain accurate estimated e¤ects of a …rm’s interest expenditures
on exports. Otherwise, the related estimates would be suspect. Instrumental variable (IV)estimation is a powerful econometric method that can address this problem.20
An economic indicator, a …rm’s weighted monetary supply, is constructed to serve asP
the instrument for interest expenditures. This indicator is de…ned as (lijt 1 = i2j lijt 1 )M 1t 1 ,Pwhere lijt 1 is the number of employees of …rm i in industry j, i2j lijt 1 is the total num-

ber of employees in industry j, and M 1t 1 is China’s base monetary supply (M 1) in theprevious year t

1. The last row in Table 1 lists the basic statistical information for thisindicator.21
The intuition is straightforward. Monetary expansion increases the supply of investments, making external …nance less costly. As a result, …rms would …nance more externallyand interest expenditures would increase. Moreover, marginal …rms, i.e., previous credit
constrained …rms, are now able to borrow from …nancial intermediaries and they might
be able to enter the export market. Since the money supply is exogenous to exports but
it a¤ects interest expenditures, it makes good economic sense to use it as an IV. We notethat the previous-period money supply is used to construct the IV based on the fact thatthe monetary policy has a time lag to take e¤ect.22
However, the e¤ects of monetary expansion on external …nance are not homogeneous
across …rms. Larger …rms might be more competitive in the competition for loans andthus they may enjoy larger bene…ts from easier access to external …nance. We therefore
20

The IV approach is a good way to control for endogeneity issues. Wooldridge (2002, Chapter 5)
provided a careful scrutiny of this topic.
21
For robustness, we construct an alternative indicator using a …rm scale, relative to the total economy,
as a weight of the monetary supply. The results are similar.
22
One may worry that monetary expansion might not a¤ect the external …naning of small-sized …rms in
China, because they might not have any access to formal …nancial intermediaries whatsoever. However,this is not a severe problem for two reasons. First, monetary expansion stimulates investments frominformal …nancial intermediaries as well. Small-sized …rms would have easier external …nancing from theseintermediaries following a monetary expansion. Secondly, all observation used in the default sample are"above-scale" …rms, which usually have access to formal …nancial intermediaries. We will include smalland midium-sized …rms later for robustness check.

25

consider a …rm’s relative size when constructing the IV. One might worry that a …rm’s
sales, a usual index for measuring a …rm’s scale, is highly correlated with the dependent
variable (i.e., the …rm’s exports) and hence not exogenous. We address this concern byusing the number of employees as a proxy for a …rm’s size. More importantly, we use its
Pindustrial weight, lijt 1 = i2j lijt 1 , rather than the national economy’s weight, to control

for …rm size.23 In this way, it is less likely that the level of exports is highly correlatedwith the weight. Indeed, their simple correlation is very small in our sample (corr = :04).
Also, as observed in Panel B of Table 1, the log of the weighted money supply has asmall simple correlation with the log of exports (corr = :39), which is much lower than
the simple correlation between the log of the weighted money supply and the log of theinterest expenditure (corr = :49).
We perform several tests to verify the quality of our instrument. First, we checked
whether the excluded instrument (i.e., the …rm’s weighted monetary supply) is "relevant".
That is, whether this instrument is correlated with the endogenous regressor (i.e., interest
expenditures). In our econometric model, the error term is assumed to be heteroskedastic:its N (0;2 ).
iTherefore, the usual Anderson (1984) canonical correlation likelihood-ratio test is not valid since it only works under the i:i:d: assumption. Instead, we use theKleibergen-Paap (2006) Wald statistic to check whether or not the excluded instrument iscorrelated with the endogenous regressors (i.e., interest expenditures). The null hypothesisthat the model is under-identi…ed is rejected at the 1% signi…cance level.
Secondly, we also tested whether or not the weighted monetary supply is weakly correlated with interest expenditures. If so, then the estimates will perform poorly in the
IV estimate. The Kleibergen-Paap (2006) F-statistics provide strong evidence to reject
the null hypothesis that the …rst stage is weakly identi…ed at a highly signi…cant level.24
P
The reason for using industrial weight and not national weight (lit 1 = i lit 1 ) is that, in China, thecompetition to borrow from banks is usually stronger in the same industry. However, using a national
weight to construct the IV also delivers very similar …ndings, which are not reported here to save space
but available upon request.
24
Note that the Cragg and Donald (1993) F-statistic is no longer valid since it only works under the23

26

Third, the Anderson and Rubin (1949)

2 statistics reject the null hypothesis that thecoe¢ cient of the endogenous regressor is equal to zero. In short, these statistical tests
give su¢ cient evidence that the instrument performs well, and therefore, the speci…cation
is well justi…ed.
The IV …xed-e¤ects estimates in Columns (1) and (2) of Table 3 show that, after
controlling for endogeneity, interest expenditures still have a positive e¤ect on a …rm’s
exports. In particular, the corresponding coe¢ cients of the IV (i.e., the logarithm of
the lagged weighted money supply) in the …rst stage are also statistically signi…cant andpositive as expected. Their …rst-stage excluded F-statistics are de…nitely high enoughto pass the F-tests. The coe¢ cients ^ 1 are higher than their counterparts in Table 2
without controlling for the endogeneity. The elasticity of a …rm’s interest expenditures on
its exports is around a unit, which clearly suggests that high interest expenditures lead tomore exports.
Equally important, in Column (2), the interaction term between FIEs and interest
expenditures is ^ 4 =:256; which is signi…cant at the conventional level, indicating that

FIEs face less severe credit constraints. On average, a one percentage increase of non-FIEs’interest expenditure leads to a 1:068 percentage increase in its exports, ceteris paribus. By
way of comparison, a one percentage increase in an FIE’s interest expenditures leads to a:812 percentage increase in its exports since 1:068:256 = :812. Similarly, the interaction

term between SOEs and interest expenditures is signi…cantly negative, which is consistent
with the PPML …xed-e¤ect …nding in Table 2. The economic rationale, arguably, is that,compared to non-SOEs, SOEs are less sensitive to external …nance due in part to theirrelatively easier access to subsidies or low-cost loans from the government.
After controlling for the endogeneity and …xed e¤ects, the coe¢ cient of domestic pro…ts
in Column (2) has a very small magnitude with an unexpected but insigni…cant sign. To
some extent, this serves as side evidence that domestic pro…ts is not a important channel
i:i:d: assumption.

27

through which …rms …nance their …xed costs for exports. Instead, the main channel toalleviate the credit constraints is borrowing externally.
[Insert Table 3 Here]

5.4
5.4.1

Additional Robustness Checks
Estimates with Narrower De…nition of FIEs

As discussed above, being an FIE is important to a …rm’s exports in the sense that being
such can introduce alternative …nancing channels such as funding from parent …rms. To
shed light on this point, we use a broad classi…cation of FIEs in the estimations above.
That is, …rms with funding from H/M/T are considered as foreign-invested …rms. Given
that Hong Kong is one of the most important sources of China’s foreign direct investment,
our estimations so far thus provide a upper bound for the role of FIEs in exports. Yet,
it is still a plus to understand the e¤ect of FIEs on exports if we exclude the impact offunding from H/M/T.
We therefore rede…ne a narrow classi…cation of FIEs by excluding H/M/T-owned …rms
and present the corresponding estimation results in Column (3) of Table 3. Comparedwith the …ndings in Column (2) using the broad de…nition of FIEs, the e¤ect of interest
expenditures is slightly larger: ^ 1 = 1:123. In contrast, the e¤ect of the interaction term,
F IEi

ln IEit , is much lower. These …ndings together suggest that a one percentage

increase in FIEs’interest expenditures leads to a percentage increase in its exports since
1:123:157 = :966. This makes good economic sense. With fewer alternative channels

through which to raise …nancing, …rms, on average, have to rely much more on externalborrowing to …nance their …xed costs for exports.
5.4.2

Estimates Using "All-Scale" Data

As mentioned above, the data used for the estimations above are all "above-scale" because
they include …rms with annual sales higher than 5 million yuan. One advantage of using

28

all "above-scale" data is to avoid a possible "adverse selection" problem. In China, it isdi¢ cult for small- and medium-sized …rms to obtain external funds since …nancial intermediaries like banks are usually unwilling to lend to them due to the high risk of default.
However, large …rms usually do not have this problem.
The trade-o¤ of using "above-scale" data is that the role of SOEs is over-estimatedsince some small- and medium-sized SOEs are dropped. Equally importantly, the problemof the exiting of …rms cannot be well controlled: it does not necessarily mean that a large…rm that was included in the early years of the study period but is not included in thelater years and exited from the market. It could be just that its scale is smaller in the later
period than the “above-scale” threshold and hence not counted in the truncated dataset.
As observed in Table 1, once we consider the small- and medium-sized SOEs, the number of SOEs increases to 127; 598, which accounts for 9.1% of the sample. When including
small- and medium-sized SOEs, we …rst recalculated …rms’average Olley-Pakes(1996)-type
TFP, and then performed all related estimations. The results are reported in Column (4)Table 3. All coe¢ cients have identical signs as the previous …ndings. The e¤ect of interest
expenditures for non-FIEs on exports, ^ 1 , is shown to be smaller than the estimates using
the "above-scale" data. However, the e¤ect of interest expenditures for FIEs on exports,^ + ^ , are insensitive to the estimation results using the "above-scale" data. In any case,1

4

our previous …ndings of the positive impact of interest expenditures on exports is robust.
5.4.3

Estimates without Productivity

Regardless of di¤erent empirical speci…cations, all the estimates from Tables 2 and 3 showthat higher productivity leads to more exports. Our theoretical model also clearly showsthat a …rm’s pre-realized productivity is the main driving force for exporting. With higher
productivity, the probability of success is higher a …rm’s project, which in turn helps the…rm to secure outside loans and to export more. In short, credit constraints are one of
the channels by which …rm’s productivity a¤ect its exports.

29

It is interesting to consider the importance of the channel of credit constraints inrelation to other channels. After controlling for …xed e¤ects and reverse causality ofinterest expenditures, Column (2) of Table 3 shows that the economic magnitude of interest
expenditures is much higher than TFP (e.g., in column (5), ^ 1 = 1:068 whereas ^ 2 = :156).
This raises a question: if ignoring the impacts of other channels of productivity on
exports, do credit constraints pick up the residual e¤ects of productivity on exports? If
so, then our estimations would be suspect since they are sensitive to di¤erent speci…cations.
By dropping the variable of productivity, Columns (1)-(3) of Table 4 provide more evidence
on this point. We report the estimation results using di¤erent econometric methods: OLS,
Fixed-E¤ects, and Fixed-e¤ect IV. All of the results obtained from these methods are
not substantially quantitatively di¤erent from their counterparts in Tables 2 and 3. In
particular, the IV …xed-e¤ect estimates in Column (3) of Table 4 show that the magnitudeof interest expenditures by excluding the variable of TFP, ^ 1 = 1:063, is almost identicalto its counterpart in Column (2) of Table 3 with the presence of TFP: ^ 1 = 1:068. This
thus provides strong evidence that interest expenditures do not pick up the residual e¤ectof productivity on a …rm’s exports.
[Insert Table 4 Here]
5.4.4

Estimation with Labor Productivity

In the previous section, we use total factor productivity (TFP) to measure productivity
since it is close to reality. However, since our theoretical model essentially is a one-input(i.e., labor) Krugman (1979) model, it is useful to use labor productivity to measureproductivity as well. Columns (4) and (5) of Table 4 report the estimation results using
labor productivity to measure productivity.
As shown in Column (4), with the control of both …rm-speci…c and year-speci…c …xede¤ects, the coe¢ cient of labor productivity, :174, is smaller than its counterpart in Column
30(4) of Table 2: :204. The result makes good economic sense given that labor is only one of
the inputs. After controlling for the endogeneity of a …rm’s interest expenditures, the IV…xed-e¤ects estimates in Column (5) also present the anticipated positive sign. However,
it is insigni…cant, which in turn suggests that the TFP may be a more ideal measure of a…rm’s productivity.
5.4.5

Additional Estimates with Ratio Speci…cations

Thus far, all estimates suggest that high interest expenditures lead to more exports. Yet,
the key variables in the estimates above are measured by levels. It is useful to consider
estimations with ratio speci…cations to check if our previous …ndings hold. We therefore
take the log ratio of a …rm’s exports to its sales as the regressand. By the same token,
we construct the log ratio of interest expenditures to sales and the log ratio of domestic
pro…ts to sales as the regressors. We then perform the Fixed-e¤ects and IV …xed-e¤ects
estimations to reconsider the impact of credit constraints on exports.
Columns (1) of Table 5 presents the …xed-e¤ects estimation results for our three key
variables: interest expenditures, TFP, and the interaction term between interest expenditures and FIEs. We can clearly observe that, even when measured in ratios, all coe¢ cientsstill have the anticipated signs and are statistically signi…cant. Adding more control variables in Column (2) does not change our …ndings qualitatively. After controlling for theendogeneity issue, estimates in Columns (3) and (4) are still in line with our previous…ndings. The only slightly surprising …nding is that, in Column (4), the coe¢ cients on theratio of interest expenditures to sales and TFP are insigni…cant. However, they still havethe anticipated positive signs. In sum, all our robustness checks suggest that our …ndings
are robust regardless of speci…cation.
[Insert Table 5 Here]31

6Concluding Remarks

In this paper, we …rst constructed a theoretical model to consider how a …rm’s credit
constraints a¤ect its exports. Firms with higher productivity have higher probabilities
for success of its projects. They thus are more capable of obtaining external …nancefrom …nancial intermediates. Consequently, they are less credit constrained and are able
to export more abroad. Moreover, FIEs are less sensitive to the availability of external…nance since they have access to funds from their foreign parent …rms.
We then test the theoretical predictions using a very rich Chinese …rm-level dataset.
After controlling for productivity and the endogeneity of a …rm’s capacity for externalborrowing, we …nd strong empirical evidence to support our theoretical argument. In
particular, …rms that are more capable of obtaining external …nance are shown to havehigher levels of exports. Moreover, the exports of FIEs are less sensitive to the …rm’s
access to external …nance from …nancial intermediaries. All of these …ndings are robust todi¤erent measures and econometric settings.
Our work enriches our understanding of impact of productivity on exports. Productivity could a¤ect exports through credit constraints. Di¤erent productivity levels inducedi¤erent project-speci…c success probabilities. Observing this, …nancial intermediaries prefer to extend …nancing to projects with less risk, given the same levels of principal andrepayment. Thus, …rms with higher productivity face less severe credit constraints andare able to export and to export more. In short, higher productivity leads to easier accessto external …nance, and, consequently, to more exports.
Our work contributes to the literature in three important ways. First, our theoreticalmodel suggests that project-speci…c risk leads to di¤erent levels of credit constraints faced
by …rms. Secondly, empirically, we o¤er …rm-level evidence that credit constraints are
an important determining factor of a …rm’s exports. Firms with easier access to external…nance or alternative sources of funds face fewer credit constraints, and, consequently, they32

export more. Thirdly, we o¤er evidence that foreign capital in‡ow a¤ects …rms’exports
through credit constraints.
Several extensions and possible generalizations merit special consideration. One ofthem is to consider outward foreign direct investment (FDI) into the model in the sensethat …rms with higher productivity would perform outward FDI in addition to exports.
Another possible extension is to consider how policy shocks like exchange rate changesa¤ect a …rm’s exports and FDI decisions in the presence of credit constraints. These are
the topics that we will pursue in the future.

33

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and Productivity: Evidence from Indonesia,”American Economic Review 97 (5), pp.
1611-1638.
[2] Bajona, Claustre and Tianshu Chu (2004), “China’s WTO Accession and Its E¤ect
on State-owned Enterprises,” East-West Center Working Paper, No. 70.
[3] Bernard, Andrew, and Bradford Jensen (1999), "Exceptional Exporter Performance:
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[5] Buch, Claudia, Iris Kesternich, Alexander Lipponer, and Monika Schnitzer (2008),"Real versus Financial Barriers to Multinational Activity," mimeo, University of Tuebingen.
[6] Cai, Hongbin and Qiao Liu (2009),"Competition and Corporate Tax Avoidance: Evidence from Chinese Industrial Firms," Economic Journal, forthcoming.
[7] Chaney, Thomas (2005), "Liquidity Constrained Exporters", mimeo, University of
Chicago.
[8] China’s National Bureau of Statistics, China Statistical Yearbook (2007), China Statistics Press.
[9] Claessens, Stijn and Tzioumis, Konstantinos (2006), "Measuring Firms’ Access toFinance," mimeo, World Bank and Brooking conference paper.
[10] Harrison, Ann E. and Margaret S. McMillan (2003), "Does Direct Foreign Investment
A¤ect Domestic Credit constraints?" Journal of International Economics 61, 73-100.
[11] Helpman, Elhanan, Marc Melitz, and Yona Rubinstein (2007), "Estimating Trade
Flows: Trading Partners and Trading Volumes," Quarterly Journal of Economics
123, pp. 441-87[12] Héricourt, Jerome and Sandra Poncet (2009), "FDI and Credit Constraints: Firmlevel Evidence from China," Economic Systems 33, pp. 1–21.
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Problems, and Institutional Innovations," Review of Income and Wealth 50(3), pp.
381-409.
[14] Holz, Carsten and Yi-Min., Lin (2001), "The 1997-1998 Break in Industrial Statistics:
Facts and Appraisal," China Economic Review 12(4), pp. 303-316.
[15] Je¤erson Gary, Thomas G. Rawski and Yifan Zhang (2008), "Productivity Growthand Convergence Across China’s Industrial Economy,” Journal of Chinese Economic
and Business Studies 6(2), pp.121-140.

34

[16] Kleibergen, Frank and Richard Paap (2006), "Generalized Reduced Rank Tests Using
the Singular Value Decomposition," Journal of Econometrics, 133(1), pp. 97-126.
[17] Krugman, Paul R. (1979), “Increasing Returns, Monopolistic Competition, and International Trade,” Journal of International Economics 9, pp. 469-479.
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Inputs to Control for Unobservable," Review of Economic Studies 70(2), pp. 317-341.
[19] Lin, Justin Yifu (2003), "Development Strategy, Viability, and Economic Convergence," Economic Development and Cultural Change 51(2), pp. 277-308.
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35

Table 1: Summary Statistics (2000-2007)
Panel A: Basic Statistics for Key Variables
VariablesYear
Firm’s Exports
Firm’s Interest Expenditure
Log Exports (ln EXit )Log Interest Expenditure (ln IEit )Log Domestic Pro…ts in Previous YearLog TFP (Olley-Pakes) in Previous Year
Log Labor Productivity in Previous Year
Log Ratio of Exports to Sales
Log Ratio of Interest Expenditure to Sales
Log Ratio of Domestic Pro…ts to Sales
SOEs Dummy (all scale)
SOEs Dummy (above scale: SOEi )FIEs Dummy (inclusive H/M/T: F IEi )FIEs Dummy (exclusive H/M/T)
F IEi ln IEit
SOEi ln IEit
WTO Dummy
Money Supply (M1)P
Weight of Firm’s Scale (lijt 1 = i2j lijt 1 )Log Weighted Money Supply

Obs.
1,294,596
1,294,596
1,294,596
360,106
872,208
525,533
840,826
846,495
360,106
872,208
812,966
1401,569
1,294,596
1,294,596
1,294,596
872,208872,208
1,294,596
1,294,596
846,495
846,495

Mean
2,004.348
17303.12
1198.837
9.516
5.398
6.533
-.031
5.250
-965
-4.888-3.564
.091
.058
.211
.102
1.038
.444
.857
1.51e+10.000
11.484

Std.Dev.
2.244
334,443.5
14325.02
1.691
1.896
1.905
.322
.999
1.412
1.512
1.446
.288
.234
.408
.303
2.355
1.732
.350
3.17e+09
.001
1.271

Panel B: Simple Correlations for Some Key Variables

Log Exports
Log Interest Expenditure
Log Lag TFP
Weighed M1

Log
Exports
1.000
.328
.197
.390

Log Interest
Expenditure

Log
Lag TFP

Weighed
M1

1.000
.083
.487

1.000
.094

1.000

36

Min
2000
0
0
0
0
0
-9.746-1.421-15.194-15.704-13.147
0
0
0
0
0
0
0
5.31e+09
3.07e-07
7.398

Max
2007
1.81e+08
5,363,291
19.01415.49517.5572.444
13.417
3.890
2.486
3.338
1
1
1
1
15.49514.6001
1.53e+10.040
19.773

Table 2: Benchmark Estimates, Log of Exports as the Dependent Variable
Regressand
OLS
FE
PPML+FE
Log Firm’s Exports (ln EXit )
(1)
(2)
(3)
(4)
(5)
Log Interest Expenditure (ln IEit ).290**.170**.105** .112**.351**(137.19) (24.33) (29.64) (5.87) (14,406.35)
Log TFP (ln T F PitOP1 ).960**.470**.215** .204**.635**(50.69)(10.81) (15.99) (3.05)(4,422.97)
FIEs Dummy (F IEi ).775**.596**–––(100.29)(7.81)F IEi ln IEit-.032**-.007.087**(-2.63)(-.46)(12,237.53)
Log Domestic Pro…t (ln Dprofit 1 ).145**.008.386**(22.88)(.70)(16,775.06 )SOEs Dummy (SOEi )-1.650**––(-8.59)SOEi ln IEit
.174**-.018-.023**(7.23)(-1.08) (-2,326.87)
Firm Fixed E¤ects
No
No
Yes
Yes
No
Year Fixed E¤ects
No
No
Yes
Yes
Yes
Observations
178,136
33,492
178,136 33,492
33,493
Root MSE
1.62
1.85
.000
.000
.000
(Pseudo) R-squared
.16
.11
.09
.03
.50
Notes: Robust t-values corrected for clustering at the …rm level in parentheses. *(**) indicates signi…cance at the 10(5) percent level. Column (5) is the Poisson pseudo-maximum likelihood (PPML)…xed-e¤ects estimation in which the regressand is EXit > 0:37

Table 3: IV Fixed-E¤ects Estimates of Interest Expenditures on Firms’Exports
Regressand:
Log Firm’s Exports (ln EXit )Log Interest Expenditure (ln IEit )Log TFP (ln T F PitOP1 )FIEs Dummy (F IEi )F IEi"Above-Scale" Estimates
Broad FIE Broad FIE Narrow FIEs
(1)
(2)
(3)
.928**
1.068**
1.123**(28.72)(4.67)(6.37).193**.156**.165**(13.78)(2.01)(2.08)–––"All Scale"
Estimate(4).753**(3.76).135(1.47)–-.256**(-4.18)-.006(-.45)–-.157**(-5.26)-.008(-.58)–-.178**(-3.16)
.012
(.81)
–

-.107**(-3.93).445**(64.36)
4,142.19y
2,914.84y4,143.18y
1,067.70 yYes
Yes
33,488.12.05-.098**(-4.36).430**(62.15)7636.93y3014.38y3862.19y
657.38yYes
Yes
33,493.14.07-.098**(-3.32).475**(60.16)3619.71y
2720.54y
3620.77y602.62yYes
Yes
27,291.15.06

ln IEitLog Domestic Pro…ts (ln Dprofit 1 )SOEs Dummy (SOEi )SOEi

ln IEit

Log Weighted M1 (IV in the First-stage)
Excluded F Statistic in the First-stage
Kleibergen-Paap rk LM statistic
Kleibergen-Paap Wald rk F statistic
Anderson-Rubin 2 Statistic
Firm Fixed E¤ects
Year Fixed E¤ects
Observations
Partical R-squared in the 1st StageR-squared in the 2nd Stage

.717**(223.64)
50,014.16y
23,282.69 y50,015.29y
25804.69yYes
Yes
178,130.24.11

Notes: Robust t-values corrected for clustering at the …rm level in parentheses. *(**) indicates significance at the 10(5) percent level. y means the p-value is less than 0.01.

38

Table 4: More Robustness Checks
Regressand:
Log Firm’s Exports (ln EXit )Log Interest Expenditure (ln IEit )Without Productivity
OLS
FE
FE+IV
(1)
(2)
(3)
.166**.113** 1.063**(23.76)(5.91)(4.65)

Labor Productivity
FE
FE+IV
(4)
(5)
.108**
1.206**(5.64)(7.11)

Log TFP (ln T F PitOP1 )Log Labor Productivity (ln LPit 1 )FIEs Dummy (F IEi )F IEi

ln IEitLog Domestic Pro…ts (ln Dprofit 1 )SOEs Dummy (SOEi )SOEi

ln IEitFirm Fixed E¤ects
Year Fixed E¤ects
Observations
Prob.>F
R-squared.605**(7.94)-.030**(-.2.55).167**(27.90)-1.703**(-8.88).181**(7.53)
NoNo
33,604
.000
.11

–

–

.179**(4.75)–.057(1.30)–-.009(-.53).017(1.41)–-.257**(-4.17)
.001
(.08)
–

-.007
(-.44)
.001
(.07)
–

-.295**(-6.25)-.006(-.46)–-.018(-1.10)Yes
Yes
33,604.000.15-.107**(-3.92)
Yes
Yes
33,600
.000
.05

-.016
(-.96)
Yes
Yes
33,604.000.15-.120**(-5.01)
Yes
Yes
33,600.000.05Notes: Robust t-values corrected for clustering at the …rm level in parentheses. *(**) indicates significance at the 10(5) percent level.

39

Table 5: Estimates of Ratio Speci…cations

Regressand:
Log Ratio of Exports to Sales (ln(EX=Sales)it )Log Ratio of Interest Expenditure to Sales (ln(IE=Sales)it )Log TFP (ln T F PitOP1 )FIEs Dummy (F IEi )F IEiln(IE=Sales)itFE(1).019**(6.66).035**(3.73)–(2).049**(2.77)
.003
(.05)
–

-.013**(-4.09)-.024(-1.19)-.051(-4.21)–

Log Ratio of Domestic Pro…ts to Sales (ln(Dprofit 1 =Salesit ))SOEs Dummy (SOEi )SOEiln(IEit =Sales)it

Firm Fixed E¤ects
Year Fixed E¤ects
Observations
Prob.>F
R-squared

Yes
Yes
178,136
.000
.01

.019
(.73)
Yes
Yes
33,492.000.01

Notes: Robust t-values corrected for clustering at the …rm level in parentheses. *(**) means signi…cantat the 10(5) percent level.

40

FE+IV
(3)
(4)
.535**.562(4.64)(1.16).091**.062(5.54)(.70)––-.327**(-4.66)

Yes
Yes
178,130
.000
.01

-.272
(-1.15)-.036*(-1.87)–-.078(-.81)
Yes
Yes
33,488.000.01

Source: Data are from Chinese City Statistical Yearbook, various years

Figure 1A: Log exports vs. Log Inward FDI
at Chinese City Level during 2000-2007

Source: Authors calculation based on the dataset

Figure 1B: Log Exports vs. Log Interest Expenditure
at Two-digit Industrial Level during 2000-2007

41

Figure 2: Firm’s Productivity, Credit Constraints, and exports

42Appendix A: Solving the cuto¤s
Domestic cuto¤
xd

1

=w

1

1
1P

1

L

Cd = w

Z

1

1
w

1

1

x

dFx (x)Cd

1

h

(Cd )

x>xd

exports cuto¤ without credit constraints
xE

1

=
=

1

w
1P

L
CE
( w)
Cd

1

1

1

CE
CE =
( w)
Cd
Z
1

1

P

1

1
w
1 x

Cd

L

1

dFx (x)Cd =

x>xd

CE
Cd

w
w

1

1

h

(Cd )

exports cuto¤ with credit constraintsrE (xCE )rE (xCE )1(xCE )w CE =(xCE ) i ) w CE
(xCE )
Substituting in revenue and pro…t, we get
=

(1

1 ((1

i+

1
1

xCE =

w L

1

d d (xCE ))1(xCE )

1 d d (xCE )

i+

+

1
(xCE )

w
1 dwL
P

1

1
(xCE )(xCE ) i ) w CE+
(xCE )(1

1w
Pi ) w CE1

1 dwCd1

1

1

In equilibrium, by substituting in P and P , we can solve the cuto¤s:
xCE =

(1

i+

(xCE ) i ) w CE+
(xCE )
1

w L

=

(1

1
i+

(1
(xCE ) 1

1(xCE ) i ) w CE+
(xCE )

w Cd ( w)1

=

w
P

1
w
i+
w
w

1
(xCE )+

1(xCE )(Cd ) +

(xCE ) i ) ww CE + (1
Cd h1

(Cd ) + (143

1

1 dwCd1(xCE )

1

h1

1

1

1 dwL

w
P

1

1

! 1
1

1
1

1 dwCd1(xCE )

1 dw2

Cd(xCE )) dCd(xCE )) dCd h1

(Cd )

1
w

! 1

1

1

! 1
1

h1

(Cd )

Appendix B: TFP Calculation by the Olley-Pakes (1996) Approach
One of the most important features of the Olley-Pakes approach is to model investment
as a function of unobserved productivity as well as capital input. By assuming that threefactor inputs (i.e., capital, labor, and material) are used to produce goods, the OlleyPakes estimation method includes three steps. First, a semi-parametric empirical method
is used to estimate the coe¢ cients of both labor and material inputs. In particular, theunobserved productivity shock is modeled as an inverse function of investment, which ischaracterized by a fourth-order polynomial. Given China’s WTO accession in 2001, which
was a positive demand shock for China’s exports, we also include the WTO dummy inthis polynomial to capture the e¤ect of the WTO accession. In particular, we consider apolynomial g(:) as following:g(kit ; Iit ; EFit ; W T Ot ) = (1 + W T Ot + EFit )

4 X
4
X

h q
hq kit Iit ;h=0 q=0

where the polynomial depends on capital k, investment I, the dummy of the exporting…rm EF , and a WTO dummy that equals one since 2002. The coe¢ cients hq; 8h; 8q areof interest to estimate. For a more detailed introduction to this approach, see Yu (2008).
Second, after the coe¢ cients of labor and material inputs are obtained, the coe¢ cientof capital can be estimated by using a non-linear square estimation. In this way, Olley
and Pakes (1996) show that the simultaneity problem is well controlled. Third, to control
the selection bias problem, we …rst use a probit function to estimate the probability of a…rm’s survival in the next period. Once the …tted value of …rms’survival ratio is obtained,
we put it into the inverse investment function once again to estimate all the three inputcoe¢ cients. Finally, the residual between the data and the …tted value obtained from thethree estimated input coe¢ cients is the Olley-Pakes TFP.

44

Symbol
q0
!

p
P
L; L
r(!)
Cd ; CE
w; w
x

G
i

t
d

lijt
M1
&i
#t
it

Appendix Table A: Main Notation for the Models
De…nition
Panel A: Theoretical Framework
Quantity of homogeneous good
Variety of di¤erentiated good
Overall set of varieties available to the consumer
Elasticity of substitution between di¤erentiated goods, > 1
Expenditure share in homogeneous goodprice of each variety
Price index of countries
Home and foreign population
Revenue of each …rm producing variety !
Fixed entry cost of domestic market and exports market
Home and foreign wage
Firm’s productivity
Pro…t
Success possibility of project
Repayment demanded by …nancial intermediaries
The portion of …xed entry cost …nanced by alternative external fund
The fraction of the domestic …xed cost pledgeable as collateral
Iceberg transportation cost
The fraction of the domestic pro…t used as internal …nancePanel B: Empirical Speci…cation
Number of employees of …rm i in industry j in year t
China base money supply
Firm-speci…c …xed e¤ectYear-speci…c …xed e¤ect
Idiosyncratic error term

45

Appendix Table B: Total Factor Productivity of Chinese Plants
Industry (code)
Est.
Labor
Materials
CapitalProb. OLS OP OLS OP OLS OP
Mining & Washing of Coal (6).992.063 .043 .834 .813 .059 .081
Mining & Processing of Ferrous Metal Ores (8) .998
.096 .092 .872 .898 .040 .038
Mining & Processing of Non-Ferrous Metal (9) .999
.058 .072 .889 .876 .042 .101
Mining & Processing of Nonmetal Ores (10).995
.083 .066 .819 .791 .044 .099
Processing of Food (13).994.068 .043 .833 .890 .048 .058
Manufacture of Foods (14).995.057 .058 .850 .840 .049 .023
Manufacture of Beverages (15).994.089 .068 .820 .855 .052 .044
Manufacture of Tobacco (16).999.053 .048 .848 .854 .161 .182
Manufacture of Textile (17).994
.066 .056 .863 .879 .033 .036
Manufacture of Apparel, Footware & Caps (18) .993.100 .096 .792 .796 .053 .019
Manufacture of Leather, Fur, & Feather (19).989
.082 .082 .846 .842 .043 .078
Processing of Timber, Manufacture of Wood, .989
.074 .051 .841 .881 .038 .045
Bamboo, Rattan, Palm & Straw Products (20)Manufacture of Furniture (21).989.107 .154 .802 .732 .046 .077
Manufacture of Paper & Paper Products (22).990
.066 .061 .851 .849 .044 .048
Printing, Reproduction of Recording Media (23) .994
.088 .063 .796 .847 .068 .052
Manufacture of Articles For Culture, Education .991
.086 .068 .822 .827 .049 .045
& Sport Activities (24)Processing of Petroleum, Coking, &Fuel (25).992
.035 .041 .864 .906 .062 .061
Manufacture of Raw Chemical Materials (26).991
.053 .031 .830 .857 .063 .074
Manufacture of Medicines (27).990
.101 .064 .785 .803 .060 .002
Manufacture of Chemical Fibers (28).995
.047 .029 .901 .923 .028 .032
Manufacture of Rubber (29).996
.078 .089 .801 .729 .067 .142
Manufacture of Plastics (30).994
.079 .074 .821 .816 .056 .051
Manufacture of Non-metallic Mineral goods (31) .993
.049 .038 .858 .870 .040 .870
Smelting & Pressing of Ferrous Metals (32).991
.054 .043 .891 .921 .036 .036
Smelting & Pressing of Non-ferrous Metals (33) .995
.052 .038 .887 .889 .031 .052
Manufacture of Metal Products (34).994
.078 .102 .793 .710 .067 .063
Manufacture of General Purpose Machinery (35) .995
.066 .049 .827 .835 .057 .058
Manufacture of Special Purpose Machinery (36) .993
.067 .029 .809 .868 .060 .070
Manufacture of Transport Equipment (37).992
.086 .077 .809 .804 .065 .058
Electrical Machinery & Equipment (39).996
.085 .068 .812 .833 .063 .119
Manufacture of Communication Equipment, .994
.103 .094 .776 .785 .082 .148
Computers & Other Electronic Equipment (40)
Manufacture of Measuring Instruments & Ma- .992
.089 .049 .724 .815 .096 .050
chinery for Cultural Activity & O¢ ce Work (41)
Manufacture of Artwork (42).992
.084 .073 .821 .849 .046 .045
Electric Power & Heat Power (44).996
.156 .140 .611 .590 .219 .217
Production & Supply of Gas (45).999.072 .035 .653 .558 .184 .275
Production & Supply of Water (46).981
.046 .019 .671 .636 .172 .163
All industries.993.068 .061 .825 .828 .062 .075
Notes: I do not report standard errors for each
coe¢
cient
to
save
space,
which are available upon request.
46