The Role of Fixed and Variable Costs in Firm’s Export Decisions
Shi-Shu Peng
Institute of Economics
Academia Sinica
Zhihao Yu
Department of Economics
Carleton University
April 2014
ABSTRACT
Marginal cost heterogeneity and fixed cost heterogeneity are two key elements in
explaining the performance of different firms in the “New New Trade Theory”. In
this study we compare the effect of the two types of costs on firm’s exporting
decisions. Using a merged comprehensive longitudinal firm-level dataset of Chinese
firms in manufacturing sector in the period 2000-2006, we first apply the approach of
difference in differences (DID) and find that Chinese firms exhibit, on average, higher
degree of heterogeneity in terms of the fixed costs than the variable costs. We further
employ the Probit model to evaluate the relative importance of these two types of
costs on firms’ exporting decisions. We find that both variable and fixed costs play a
significant role in such decisions. Although the magnitude of marginal effect of the
fixed cost is lower than that of the variable cost, its potential overall effect could be
higher than the variable cost since its average level is about five times of that of the
variable cost.
Keywords: Fixed cost, Variable cost, Firm heterogeneity, Export decisions.
JEL Classification: F12
1 1. Introduction
It has been a decade since Marc Melitz published his seminal work on the theory
of monopolistic competition with heterogeneous firms (i.e. Melitz, 2003). Since
then, the literature, which has been called the “New New Trade Theory,” has thrived
in the field of international trade because it nicely fits the recent empirical findings
about the behavior of exporting firms, based on increasingly available firm-level
datasets. For example, Bernard and Jensen (1995, 1999) find that more efficient
firms become exporters and they also tend to be larger than non-exporters.1 As a
result, the New New Trade Theory has been developed mainly along the line of the
heterogeneity in marginal costs and found that only the more productive firms are
able to bear the fixed entry costs of exports and self-select themselves to export.
This inter-firm reallocation will lead to aggregate industry productivity growth and
welfare gain. Such marginal cost heterogeneity and the fixed market entry costs are
the two critical elements that are added to the new trade theory, and they also offer a
theoretical explanation for the empirical findings of export premium in many
empirical studies. Helpman, Melitz and Yeaple (2003), and other theoretical studies
along this strand of literate, follow Melitz’s structure to study firm’s other decisions
such as foreign direct investment or exporting destinations. The contribution of this
literature is that it points to a re-thinking of the driving force behind the globalization
process of cross-border trade and investment.
Another strand of the literature, initiated by Schmitt and Yu (2001, 2002),2
instead focuses on the heterogeneity in fixed costs to study the issue of scale
economies and intra-industry trade volume. These fixed costs may refer to the fixed
costs of export that are associated with administrations, the changes in product
designs to meet local tastes or regulations, information collection, marketing, or the
distribution network in a foreign country. Several studies, such as Leonidou (1995,
2004), Das, Roberts and Tybout (2001) and Roberts and Tybout (2004), have offered
evidence for such fixed cost heterogeneity. Fixed cost heterogeneity has also been
identified as an important channel for export market selection in Roberts and Tybout
(1997), Lawless and Whelan (2008) and Lawless (2009, 2010). Firms with lower
1
Also see Sofronis Clerides, Saul Lach, and James R. Tybout (1998), and Bee Yan Aw, Sukkyun
Chung, and Mark J. Roberts (2000) for the stylized facts about the behavior and performance of firms
for the different countries. They basically find that exporters are in the minority and they tend to be
larger and more productive. See Wagner (2007) for a survey.
2
Baldwin (2005) provides a mini-survey of the earlier studies on heterogeneous firms.
2 fixed costs, according to this strand of literature, are also predicted to be more likely
to export, similar to those with lower marginal costs in the literature with
heterogeneous marginal costs. Although not as broadly adopted as the Melitz-type
model, some recent studies such as Jørgensen and Schröder (2006, 2008), Cole (2011)
and Jørgensen, Schröder and Yu (2012) follow this line of literature on fixed cost
heterogeneity to study the welfare effects of the trade liberalization.
Both strands of the literature start their theoretical exploration based on the
observation that firms in the real world are heterogeneous and the magnitude of these
heterogeneities differs across industries and countries. Their main contributions to the
field of international trade are as follows. First, they are able to explain why some
firms choose to export or invest abroad while others do not. Second, they find a new
channel --the extensive margin-- through that new exporters enter the foreign market,
which complements the channel of the intensive margin we already know (see Chaney,
2008). Third, the structure of international trade can be analyzed for the
redistribution among heterogeneous firms.
So far, the literature seems to lack an assessment of these two strands of the
literature.3 The recent trend and the development in organizational theory motivate
such an assessment because during the last few decades, the structure of some
industries has changed from conventional firms that involve most production
activities to modern enterprises that only perform a part in the value chain. Such
structure changes are more prevalent in some industries such as consumer electronics,
apparels, or footwear. A well-known example is that Apple has given up almost all
production and focused only on product design, marketing and retail sales. It
outsources the production to some original equipment manufacturing (OEM) firms
like Foxconn. In this example of production network, the fixed costs associated with
these activities could be more relevant than the marginal costs of production. This
suggests that the question whether marginal cost heterogeneity or fixed cost
heterogeneity is more empirically relevant may also depend on the characteristics of
the industries.
In this study we investigate how fixed costs and variable costs affect firms’
export decision and their relative importance by using a firm-level dataset of Chinese
firms in the period of 2000-2006. We first measure and compare the heterogeneities
of fixed costs and variable costs using the difference-in-difference method and the
method of Propensity Score Matching. Then, we apply the Probit model for a
3
Recently, XXX , XXX
have considered both marginal and fixed cost
heterogeneities in their studies.
.
3 statistical evaluation to investigate whether the fixed costs or variable costs play a
more important role in firm’s exporting decision. We find that both fixed and
variable costs play significant roles in such decisions, and although the magnitude of
marginal effect of the fixed cost is lower than the variable cost, its potential overall
effect could still be higher than the variable cost since its average value is about five
times of that of the variable cost.
The rest of the paper is organized as follows. Section 2 provides a description
of the dataset used in this study. Section 3 discusses in details the methodology used
for our analysis. Section 4 discusses the results, and Section 5 concludes.
2. Data and Descriptive Statistics
We first introduce the dataset and variables used in this study in Section 2.1, and
then illustrate the descriptive statistics in Section 2.2.
2.1. Data and Variables
The data we use in this study is the Chinese Industrial Enterprises Database
conducted by National Bureau of Statistics (NBS) of China in the period of
2000-2006. It is a panel data containing all firms that operate business in China with
annual revenue higher than RMB five million yuan. It provides detailed firm-level
information in three categories: (1) basic: including firm ID, location, main products,
ownership, founding year, employment, and so on; (2) financial: capital, assets and
liabilities, profit or loss, wages, etc; and (3) production: final products, sales, exports,
among others.
This dataset offers rich information for this study. We are able to identify
whether a firm is an exporter by observing whether its export value is zero, and to
observe directly each firm’s fixed and variable costs, which help to form potential
proxies for both costs in the trade literature with heterogeneous firms. In addition,
we can also use other firm-level variables such as employment, sales, shipments,
value-added, founding year, industry, province, and ownership as control variables in
the statistical analysis. The variables are listed in Table 1.
[Insert Table 1 here]
The dependent variable is the exporting decision, namely whether or not to
export. We construct this variable
for each firm by a dummy variable that equals
1 if the export value (variable code V213 in the dataset) is greater than zero, while
equals 0 if such value is zero.
The most important independent variables are the fixed and variable costs. In the
4 literature such as in Schmitt and Yu (2001), the fixed costs that affect the exporting
decision are the ones associated with the domestic sales: only those firms whose fixed
costs lower than a threshold level will be able to export. For each non-exporter, the
fixed costs (FC) of each firm is then formed by directly adding its three types of costs:
the operating expense (code F328), the management cost (code F336), and financial
cost (code F340). For each exporter, however, since we cannot identify which
proportions of these fixed costs are associated with its domestic and exporting sales,
we have to adjust the above sum to figure out its fixed costs that are associated only to
its domestic sales. Among the three types of fixed costs, only the operating expense
(F328) closely expands with the sales due to exporting decision. We thus assume that
of the operating cost is associated with the
for year , the proportion of
exports and that of
with the domestic sales (
).
Such operating cost associated exclusively with domestic sales, however, is not
directly observed in the dataset. Therefore, we calibrate
with the observable data
using the firm-level sales (that can be divided into exporting and domestic parts).
Specifically, we compute the average ratio of the exporting sales to the domestic sales
across all exporters, denoted as , and then use it to calculate the firm-level
estimated domestic operating cost for each exporter that is comparable with that of a
non-exporter.
In
short,
for
non-exporters
the
fixed
cost
is
and
for
the
exporters
it
is
.
In the Melitz-type models it is the marginal cost that plays a critical role in
driving the exporting behaviors of firms. However, marginal cost is not observable.
One appropriate measure can be the average variable cost, namely the total variable
cost per output (i.e. the firm-level TVC/Q where Q is the output). For the variable
costs, we can directly observe the operating costs (also called “the costs of product
sales”; code F327). But in our dataset, for each firm we only observe its shipment
(i.e. P*Q; code V207), not output (Q). To find AVC, we should multiply the TVC
per shipment by the firm-level price (P) but unfortunately, such price seems to be
unavailable in any dataset. Therefore, we compute the industry-level price using the
China Custom Database instead to calculate the firm-level AVC.4
Two caveats: first, the average industry-level prices are obtained using the
Custom data only (i.e. rigorously speaking, they are export prices). These prices are
4
The China Custom Database contains information for all transactions of exports and imports such as
the commodity HS codes, volumes, and prices, among others. We first compute for each commodity
the weighted average price using the transaction values as weights. Then using these average
commodity prices and following a table transferring between commodities and industries, we compute
the weighted average price for each industry using the total transaction volume of commodities as
weights.
5 different between exporters and non-exporters, however. For non-exporters, it is
almost impossible to compute the domestic industry prices due to lack of data. Second,
using the industry-level price is equivalent to assuming that the firm-level prices of all
firms in the same industry are identical. This assumption will definitely move the
distribution of the AVC away from its true value within an industry and affect our
results.
The control variables include the shipment (V207), employment (V210),
capital-labor ratio (F301/V210), year, ownership, and industry. The year-fixed-effect
variable ranges from 2000 through 2006. The ownership variable includes seven types
dummy variables: the (1) state-owned, (2) collectively-owned, (3) private-owned, (4)
capital from Hong Kong, Macao, and Taiwan, (5) foreign-owned, (6) Chinese and
foreign joint-venture mainly owned by Chinese capital, and (7) Chinese and foreign
joint-venture mainly owned by foreign capital. The industry dummy variables denote
29 manufacturing industries listed in Table Appendix 1.
2.2. Descriptive Statistics
Firms are heterogeneous in both their fixed costs and variable costs with
probably different magnitudes. In this subsection, we simply observe the
heterogeneity in the two types of costs by first comparing the variation of total fixed
costs (FC) with that of average variable costs (AVC) using firm-level data in different
years during the period of 2000-2006. Then, we implement a similar analysis using
the same data not only in different years, but also in 30 different industries.
Table 2 shows the comparison results between the variations of FC and AVC for
each year from 2000 to 2006. We calculate the coefficients of variation (CV) for
both FC and AVC with all firms included regardless of what industry they are in. We
consider the fixed costs to be more heterogeneous than the average variable costs if
the CV of the former is higher than that of the latter, and vice versa. From Table 2, we
see that for all seven years, FC exhibits higher degree of variation, or heterogeneity,
than AVC.
[Insert Table 2 here]
Next we proceed in more detail with such comparison between FC and AVC for
each industry and for each year between 2000 and 2006. Table 3 demonstrates the
coefficients of variation for both costs on the yearly-industry basis.5 From this table,
5
We drop 13 firms (out of 1,287,177) as outliers when preparing Table 3 since in the following 12
industries in different years they cause the coefficients of variation for AVC extremely large (ranging
from 20 to 60; we accept CVs lower than 20): industries 13, 14, 18, and 19 in 2000, industry 30 in 2002,
industries 29, 31, and 35 in 2003, industries 32 and 35 in 2004, industry 30 in 2005, and industry 42 in
6 we see that various industries exhibit different properties. In the following fourteen
industries: No. 14, 18, 19, 21, 22, 23, 24, 28, 29, 32, 36, 37, and 40, the FC is always
more heterogeneous than the AVC for all years. For the other fifteen industries, FC is
more heterogeneous than AVC most of the time since only in industry 16, AVC
exhibits higher degree of heterogeneity than FC (in five years out of six) and in the
rest fourteen industries, this is the case only in one or two years out of six.
Therefore, we may conclude from Table 3 that FC is in general more heterogeneous
than AVC if the degree of heterogeneity is measured by coefficient of variation.
[Insert Table 3 here]
3. Methodology
We introduce the statistical strategies for comparing the degree of heterogeneities
in the above two types of costs and then explore their effects on firms’ export
decisions. More specifically, Section 3.1 illustrates how we compare the degree of
the heterogeneity in both costs using the method of difference in difference (DID) and
its advanced version adjusted by the approach of propensity score matching (PSM) to
address the problem of selection bias. Then in Section 3.2, a Probit model is
implemented to compare the magnitudes of the two types of costs in affecting the
export decisions.
3.1. Difference in Difference and Propensity Score Matching
Difference in Difference
In the previous section we compared the degree of heterogeneity of the fixed and
variable costs simply using the coefficients of variations. To provide a more
rigorous analysis, here we apply the method of Difference in Difference (DID). The
DID method is an econometric quasi-experimental technique that is used to measure
the effect of a treatment or an event within a given period of time. In particular, this
method aims to measure the pre-post and within-subjects differences of the treatment
and control groups induced by such a treatment or event.
The concept of this method can be applied in this study, although there is no
effect of a treatment or event to be measured. The pre-post and within-subjects
differences of the two groups can be viewed as the fixed-variable costs and
within-subjects heterogeneity differences of the exporter and non-exporter groups
induced by profitability. In other words, we view the exporters and non-exporters as
treatment and control groups, and apply the DID method to measure whether the fixed
cost or the variable cost has higher degree of heterogeneity between the two groups of
2006. This may be related to the assumption of equal price within an industry.
7 firms.
Consider the following equation:
(1)
where
represents the fixed cost (FC) or average variable cost (AVC) of firm
(notice that the total number of observations should be twice of the number of all
firms because each firm has observations on both the FC and AVC);
is a
dummy variable for exporters (equals 1 if firm is an exporter and 0 otherwise);
is a dummy variable for FC (equals 1 if the observed cost is FC and 0
otherwise);
is an error term.
For the AVC, the average for exporters is
, while the average for non-exporters is
since
since
and
and
.
The difference in the average in terms of the AVC between exporters and
non-exporters is
since
. For the FC, the average for exporters is
and
and
, while the average for non-exporters is
.
since
Thus, the difference in the average in terms of FC
between exporters and non-exporters is
.
The above differences between the two groups of firms in terms of the AVC and
FC,
and
, measure the degrees of heterogeneity between the two groups
,
of firms in AVC and FC, respectively. The difference of the above two differences,
further tells us which of AVC and FC has larger degree of heterogeneity between the
two groups of firms. A simple statistical test of whether
.is different from zero can
tell us whether the two groups of firms have a larger degree of heterogeneity in FC
than in AVC.
Propensity Score Matching
The method of DID may be subject to certain biases (e.g., the sample selection
bias). To adjust such biases, the Propensity Score Matching (PSM) is a common
method for obtaining unbiased estimation of the treatment effects. The bias occurs
probably because the effectiveness of a treatment may have something to do with a
participant’s certain characteristics that are related to whether or not he chooses or is
chosen to be in a treatment.
The concept of the Propensity Score Matching is to choose from the control
group the observations with certain characteristics that are most similar to those of the
8 observations in the treatment group. With such matching, the distribution of
characteristics of the observations in the two groups will be fairly similar and
therefore the above problem of endogeneity may be greatly reduced. In brief, the
method of the PSM assigns a propensity score to each observation with certain sample
characteristics controlled by using the estimation of a Logit model, and then matches
the samples in the two groups. There are various approaches of matching, with no
consensus about which one is the best. Here we apply the one-to-one non-replacement
approach. Namely, we match each sample in the treatment group (exporters) exactly
with one corresponding sample in the control group (non-exporters) that is most
similar to it. After being adjusted by the PSM method, we implement the DID method
again, and report/compare the results with and without the PSM method.
In Section 4, we will report the results from the method of DID without and with
the adjustment of the PSM. In addition, considering of the issue of lacking of suitable
measure for marginal cost, we use the total variable costs, total variable costs per
shipment, and total variable costs per sales as proxy candidates for the purpose of
robustness check.
3.2. Probit model
Now we implement the Probit model to investigate whether the fixed or variable
costs play a more important role in export decisions. The Probit model is proposed as
follows:
(2)
where the dependent variables
denotes the dummy variable of firm
’s
is firm
exporting decision, which equals to 1 if this firm exports and 0 otherwise;
’s total fixed costs;
should theoretically represent firm ’s marginal cost, but
due to the data limitation, we use average variable costs as proxies. As for control
variables,
is its value of shipment for firm ;
denotes its sales;
denotes firm ’s employment;
denotes the capital labor ratio of firm ;
controls for the time fixed effect from the year of 2000-2006;
is a
group of six dummy variables of ownership to control for seven types of ownership
is another group of 29 dummy variables of industries to
structures;
control for firms’ industries in two-digit level; and finally,
is an error term. In
addition, we compute the marginal effects of the main variables by applying the
dProbit function in Stata and later will report together with the results of Probit
9 model.
4. Results
4.1.
Difference in Difference without and with Propensity Score Matching
In Table 4 we report the results from the comparison of the heterogeneity
between exporters and non-exporters for fixed and variable costs, using the method of
DID without and with the adjustment of the PSM. Table 4 lists, for the year in the
period of 2000-2006, the values and significance for the coefficient of the cross term
in equation (1), namely
.
Again, the degree of heterogeneity between
exporters and non-exporters for the fixed costs will be significantly greater, smaller,
and not different from that for the variable costs if
and not different from zero.
is significantly greater, smaller,
[Insert Table 4 here]
Table 4 exhibits consistent results regardless of whether using DID or DID+PSM
methods. For all years,
s are all positive, suggesting that the degree of
heterogeneity between exporters and non-exporters for the fixed costs is statistically
significantly greater than that for the variable costs. This indicates that among various
sources and aspects of firm heterogeneity, we may need to pay more attention to the
fixed cost since it can be statistically more heterogeneous than the average variable
cost.
4.2. Probit model
The next question of our interest is whether the fixed cost or the variable cost is
more important in affecting firms’ exporting behaviors. We employ the Probit model
to evaluate the relative importance of the role of the fixed and average variable costs
in firm’s exporting decision.
First, to avoid the problem of multi-collinearity, Table 5 shows the matrix of the
correlation coefficients between independent variables to make sure that they are
indeed not mutually highly correlated. From Table 5, if we define high correlation
as the correlation coefficient exceeding 0.8, the MC problem only exists between the
shipment and sales (because the value of the coefficient is 0.99). We thus drop the
variable of sales because the variable of shipment is more relevant in the concept of
firm’s production activities.
[Insert Table 5 here]
10 The results of the Probit model are illustrated in Table 6. From the estimated
coefficients, we find that both the fixed and average variable costs, on average, affect
firms’ exporting decision negatively, and significantly at 1% and 10% significance
levels, respectively. In other words, a higher value of fixed or average variable costs
decreases firms’ probability becoming exporters, with a higher significance level for
the fixed cost. This result confirms the findings related to fixed and variable costs in
the literature of heterogeneous firms.
The dProbit function in Stata can further offer the quantitative explanations for
the marginal effect of the two types of costs on the probability of exporting with the
related coefficients. We find that when the cost rises by 1 million RMB, for the fixed
cost the probability of exporting will decrease by 0.0148% while for the average
variable cost it will decrease by 0.0175%, suggesting that the variable cost may be
more effective in exporting decision than the fixed cost. However, we also find that
the average fixed cost and variable cost for all firms across all years to be 6.93 million
RMB and 1.33 million RMB, respectively. Considering this, the overall effect of the
fixed cost can be larger than that of the average cost since on average we cannot lower
the variable cost by more than 1.33 million RMB.
Both shipment and employment affect firms’ exporting decision positively and
significantly, implying that firms that produce more and hire more workers are more
likely to export. The effect of capital-labor ratio is negative and significant,
suggesting that more labor-intensive firms are more likely to export, possibly due to
the export-led characteristics of China.
[Insert Table 6 here]
5. Conclusion
We investigate how the variable cost and fixed cost affect the exporting decisions
of firms. For the variable cost, both the theoretical and empirical literatures find that
higher variable cost prevents firms from exporting; for the fixed cost, however,
although some theoretical studies have found such relationship, the empirical ones are
still lacking. As far as we know, this study is the first to simultaneously consider and
compare the effects of the two types of costs on exporting decisions.
In this study, by using a merged comprehensive longitudinal firm-level dataset of
Chinese firms in manufacturing sector in the period 2000-2006, we first apply the
approach of difference in differences (DID) (and in addition DID with propensity
11 score matching (PSM) for the sake of robustness) to examine the degrees of
heterogeneity of the two types of costs. We find that Chinese firms on average exhibit
higher degree of heterogeneity in terms of the fixed cost than the variable cost,
suggesting that the fixed cost is one non-negligible dimension among various
dimensions of heterogeneity of firms.
We further employ the Probit model to evaluate the relative importance of these
two types of costs on firms’ exporting decisions. We find that both the variable and
fixed costs both play significant roles in such decisions in a negative relationship. In
addition, with the function of dProbit in Stata we find that although the magnitude of
marginal effect of the fixed cost is lower than that of the variable cost, its potential
overall effect on firms’ export decision can still be higher than the variable cost since
its average level is about five times of that of the variable cost.
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14 Table 1: List of Variables.
Variables
Description
Dummy variable of exporting decision: equals 1 if firm ’s exports, 0 otherwise..
The fixed costs of firm .
The average variable costs of firm .
The shipment of firm .
The sales of firm .
The employment of firm .
The capital-labor ratio of firm .
The year of the dataset in which firm
was surveyed.
The ownership type of firm .
The industry firm
is in.
Table 2: Coefficients of Variation of Fixed and Variable Costs (Yearly).
Year
Fixed costs
Variable costs
2000
2001
2002
2003
2004
2005
2006
6.27
6.44
7.01
7.66
8.40
8.56
9.03
6.27
6.01
5.12
5.20
4.90
4.61
4.60
Source: Computed by the authors.
15 Table 3: Coefficients of Variation of Fixed and Variable Costs (Yearly-Industry).
2000
2001
2002
2003
2004
2005
2006
Ind
FC
VC
FC
VC
FC
VC
FC
VC
FC
VC
FC
VC
FC
VC
13
2.82
4.06
3.03
1.56
2.97
2.10
3.07
0.70
3.18
0.46
3.40
4.97
3.51
0.50
14
3.22
0.89
3.31
0.73
3.69
0.67
3.92
0.46
4.50
1.07
5.08
0.58
5.31
0.51
15
3.32
0.78
3.44
0.52
3.89
1.87
4.33
0.58
3.89
1.00
4.07
1.04
4.30
6.52
16
1.74
4.69
1.67
4.99
1.74
1.75
1.67
1.11
1.75
7.87
1.79
2.35
2.22
0.75
17
2.34
1.18
2.46
3.65
2.65
1.16
3.02
1.34
3.51
0.74
4.13
3.77
4.25
0.60
18
2.76
0.70
3.46
0.89
3.40
0.44
3.07
0.88
3.50
0.38
4.01
1.14
4.68
0.88
19
2.41
0.48
2.42
0.46
2.66
0.59
3.27
0.39
3.50
0.23
3.97
0.23
3.27
0.26
20
2.19
3.60
2.30
0.56
2.84
0.40
2.42
0.35
4.39
0.49
4.80
0.37
4.94
0.39
21
2.75
0.65
2.03
0.45
2.06
0.33
2.16
0.30
2.55
0.80
2.71
0.30
2.63
1.42
22
3.57
0.49
4.30
2.29
4.76
0.96
4.61
0.35
5.21
0.44
5.08
0.31
5.87
0.35
23
1.86
0.41
2.12
0.46
2.29
0.58
2.37
0.52
2.53
0.68
2.53
0.74
2.71
0.29
24
2.14
1.00
2.34
0.71
2.35
1.75
2.50
0.22
2.71
0.38
2.57
0.56
2.66
0.25
25
4.40
19.85
4.19
0.76
4.05
1.33
3.70
0.49
4.13
2.45
4.08
1.18
4.06
0.96
26
4.95
8.40
6.02
2.14
5.66
0.59
7.34
0.56
10.00
0.45
8.30
0.90
7.60
2.13
27
3.68
0.68
3.21
1.41
3.42
1.42
3.85
0.54
4.14
3.79
4.74
6.79
4.98
0.59
28
4.46
0.22
3.07
0.29
3.16
0.31
3.55
0.27
3.93
0.20
3.62
0.20
3.58
2.79
29
2.97
0.65
3.22
0.47
3.62
0.39
3.84
0.37
4.30
0.43
4.47
0.69
4.44
1.00
30
2.24
0.37
2.73
0.54
2.74
12.41
2.76
0.37
10.20
3.81
2.85
0.66
4.07
1.56
31
2.63
0.67
2.63
1.87
2.66
0.75
2.71
0.41
2.84
10.77
3.02
0.47
2.98
2.02
32
7.13
2.42
6.77
1.76
7.64
0.52
8.96
0.49
10.11
0.52
9.18
0.46
9.40
5.88
33
5.61
0.68
4.16
0.64
4.11
0.40
4.45
11.10
6.60
4.61
6.09
0.46
7.33
0.35
34
2.51
0.92
3.23
7.33
2.49
0.89
2.86
0.36
3.02
1.31
3.36
1.05
3.52
2.46
35
3.31
1.21
3.43
0.53
3.82
15.22
4.06
0.40
4.11
3.46
4.38
11.35
4.60
0.52
36
2.50
1.29
2.44
1.17
2.78
1.23
3.22
2.44
4.44
1.48
3.65
1.26
4.00
0.65
37
7.11
1.29
7.60
1.59
8.42
0.61
8.11
0.62
6.35
0.66
8.20
0.53
8.56
0.46
39
1.11
0.21
1.10
0.24
1.09
0.26
6.17
0.61
7.45
8.95
7.49
10.02
7.91
1.72
40
7.03
0.97
6.91
6.73
5.89
0.62
6.52
4.28
8.44
1.86
9.44
2.57
10.18
1.59
41
5.36
5.25
5.62
0.77
6.26
13.37
3.20
0.46
2.74
3.10
2.65
0.88
2.73
0.89
42
2.40
0.61
2.44
0.96
2.43
0.95
2.16
0.48
2.63
13.22
2.63
0.51
2.86
1.92
Source: Computed by the authors.
Note: There is no industry-level price for industry 43 for computing variable costs, thus not being able
to obtain the coefficients of variation for this industry.
16 Table 4: Results of Heterogeneity Comparison between Exporters and
Non-exporters in the Fixed and Variable Costs using DID with and without PSM.
Year
DID
DID+ PSM
2000
0.50***
0.33***
0.25***
0.21***
0.18***
0.15***
0.54***
0.48***
0.36***
0.31***
0.27***
0.22***
0.23***
0.43***
2001
2002
2003
2004
2005
2006
Note: ***, **, *: Significant at 1%, 5%, 10% levels.
Table 5: Matrix of Correlation Coefficients between Independent Variables.
1.00
0.02
1.00
0.71
0.02
1.00
0.72
0.02
0.99
1.00
0.60
0.02
0.57
0.57
1.00
0.05
0.01
0.04
0.04
-0.01
17 1.00
Table 6: Results of the Probit Model.
Coefficients
-4.45***
-1.48***
-5.26*
-1.75*
0.11***
0.04***
3.91***
1.30***
-0.31***
-0.10***
Const
-1.35***
Obs.
1,287,151
R-squared
Marginal Effects
0.19
1,287,151
0.19
Note: (1) ***, **, *: Significant at 1%, 5%, 10% levels; (2) the marginal effects are
calculated with the dProbit function of Stata.
18 Table Appendix 1: Industry Classification.
Code
Industry name
13
Agriculture and food processing
14
Foodstuff manufacturing
15
Soft drink manufacturing
16
Tobacco manufacturing
17
Textile
18
Weaving costume, shoes and cap manufacturing
19
Leather, fur and feather manufacturing
20
Wood working and wood, bamboo, bush rope, palm, straw manufacturing
21
Furniture manufacturing
22
Paper making and paper products
23
Print and copy of record vehicle
24
Stationary and sporting goods manufacturing
25
Oil processing, coking and nuclear manufacturing
26
Chemical material and chemical product manufacturing
27
Medicine manufacturing
28
Chemical fiber manufacturing
29
Rubber product
30
Plastics product
31
Nonmetallic mineral product
32
Ferrous metal refining and calendaring processing
33
Non-ferrous metal refining and calendaring processing
34
Metal product
35
Universal equipment manufacturing
36
Task equipment manufacturing
37
Transport communication facilities manufacturing
39
Weapons and ammunition
40
Electric machine and fittings manufacturing
41
Communication apparatus, computer and other electric installation manufacturing
42
Instrument and meter, stationary machine manufacturing
Source: HuaMei Information (2012).
Note: The industry classification is grouped by International Standard Industrial Classification (ISIC)
of All Economic Activities.
19