KEO Discussion Paper No. 134

Misallocation, Productivity, and Trade Liberalization:
The Case of Vietnamese Manufacturing
Doan Thi Thanh Ha
Yokohama National University
Kozo Kiyota
Keio University

May 27, 2015

Misallocation, Productivity, and Trade Liberalization:
The Case of Vietnamese Manufacturing∗
Doan Thi Thanh Ha
Graduate School of International Social Sciences, Yokohama National University
andFaculty of International Economics and Business, Foreign Trade University
Kozo Kiyota†
Keio Economic Observatory, Keio University

Abstract
This paper attempts to measure the contribution of resource misallocation to aggregate manufacturing TFP, focusing on Vietnamese manufacturing firms for theperiod 2000–09. Our research questions are threefold. 1) To what extent are resources misallocated in Vietnam? 2) How large would the productivity gains havebeen in the absence of distortions? 3) Did the degree of misallocation decline after
entry into the World Trade Organization (WTO)? The answers to these questions
are as follows. First, misallocation in Vietnam is comparable to that in China
and India. Second, there would have been substantial improvement in aggregate
TFP in the absence of distortions. Finally, the accession to the WTO contributed
to reducing the distortions in output markets. However, this positive eﬀect was
oﬀset by increasing distortions in capital markets. These results together suggestthat further reforms in capital markets could improve aggregate TFP in Vietnam
through reduced misallocation.
Key words: Misallocation; Total factor productivity; Trade liberalization; WTO; Vietnam
JEL classification codes: O47; F14; D22∗

We wish to thank Iwan J. Azis, Flora Bellone, Minsoo Lee, Qing Liu, Atsushi Ohyama, Yoichi Sugita,
Kenichi Ueda, seminar participants at Hokkaido University, IDE-JETRO, Keio University, Kwansei Gakuin
University, Okinawa University, Osaka University, the University of Tokyo, and participants at the East
Asian Economic Association, 2015 Spring Meeting of the Japanese Economic Association, and the Yokohama
Workshop on International Trade and Growth. Thanks also to Kaoru Hosono and Miho Takizawa forproviding information on misallocation in Japan. Kiyota gratefully acknowledges financial support received
from the JSPS Grant-in-Aid (26220503, 26285058) and the MEXT-Supported Program for the Strategic
Research Foundation at Private Universities. The usual disclaimers apply.
†
Corresponding author: Keio Economic Observatory, Keio University, 2-15-45, Mita, Minato-ku, Tokyo
108-8345, Japan; E-mail: kiyota@sanken.keio.ac.jp; Tel: +81-3-5427-1480.

1

Introduction

Diﬀerences in per capita income across countries result mainly from diﬀerences in totalfactor productivity (TFP).1 Therefore, clarifying the underlying causes of low productivity
in developing countries is one of the central concerns in various fields of economics suchas development economics, international economics, and macroeconomics. Given the factthat production eﬃciency is heterogeneous across firms, some recent studies on this issue
argue that aggregate TFP depends not only on the TFP of individual firms but also on the
allocation of resources across firms.2 In other words, low productivity in developing countries
can be attributable to the misallocation of resources across heterogeneous firms.
What are the underlying factors causing misallocation? Recent studies have pointed
out that resource misallocation can be attributable to policy distortions, such as heavy
taxes/subsidies, and market imperfections, such as credit market imperfections. The removalof such distortions can bring about substantial benefits for the economy. For example,
Restuccia and Rogerson (2008) developed a growth model with heterogeneous establishments
and calibrated it using US data. They showed that policies that lead to price diﬀerences faced
by individual producers could lead to sizeable decreases in output and TFP in the range of
30–50 percent. Similarly, using establishment-level data in China, India, and the US, Hsieh
and Klenow (2009) found that, if China and India were to move to the US eﬃciency level,
TFP would be boosted by 39.3 percent for China and 46.9 percent for India. Therefore, therelationship between misallocation and policy distortions is an important aspect of aggregate
TFP in developing countries.
Among the various policy distortions, trade barriers seem to be an important sourceof misallocation because they tend to be higher in developing countries than in developed1“Large diﬀerences in output per worker between rich and poor countries have been attributed, in no smallpart, to diﬀerences in total factor productivity” (Hsieh and Klenow, 2009, p. 1403); “cross-country income
diﬀerences mostly result from diﬀerences in total factor productivity” (Waugh, 2010, p. 2095). McMillan
and Rodrik (2011) also argued for the importance of resource reallocation in enhancing productivity growthin developing countries.
2
See Restuccia and Rogerson (2013) for a survey. Another source of distortions is credit market constraints, which we will discuss in more detail in Sections 4.3 and 4.4.

1

countries.3 Trade liberalization is expected to diminish misallocation, reallocating resourcesfrom less productive to more productive firms (e.g., Melitz, 2003). This paper attempts tomeasure resource misallocation in Vietnamese manufacturing firms for the period 2000–09.
An advantage of the use of Vietnamese data is that the data cover the year 2007, when
Vietnam joined the World Trade Organization (WTO). This enables us to compare themisallocation before and after trade liberalization.
Our motivation comes from two strands of research. One is the literature that focuseson distortions that reflect the diﬀerence between the actual and eﬃcient outcomes. Such
distortions are called “wedges” in the literature. A seminal paper is Hsieh and Klenow(2009), which estimates wedges from data on value added and factor inputs for manufacturing
establishments in China, India, and the US. They found that the distortions were much larger
in China and India than in the US. Moreover, as mentioned above, Hsieh and Klenow (2009)
found that the removal of distortions has an insignificant eﬀect on aggregate TFP in China
and India. Following Hsieh and Klenow (2009), several studies have provided a similar
picture: large TFP gains could be expected from the removal of distortions.4 However, therelationship between tariﬀ reduction and misallocation has not been fully examined in theliterature yet.
The other strand is the analysis of the impacts of trade liberalization on aggregate TFP.
There is strong evidence linking trade barriers and aggregate TFP.5 However, little attention has been paid to the impacts of trade liberalization on misallocation measured by thevariation in wedges.6 As a recent theoretical study by Epifani and Gancia (2011) pointed

3

According to the World Bank (2014), for example, average tariﬀ rates for high-, middle-, and low-income
countries are 3.9, 8.6, and 11.5 percent, respectively. Restuccia and Rogerson (2013) also argued that tradebarriers could be one of the factors generating misallocation.
4
See Camacho and Conover (2010) for the case of Colombia; Busso, Madrigal, and Pages-Serra (2012)
for Latin America; Bellone and Mallen-Pisano (2013) for France; Hosono and Takizawa (2013) for Japan;
and de Vries (2014) for Brazil. Furthermore, Bach (2014) examined misallocation in Vietnam. However, his
study did not address the issue of trade liberalization.
5
See, for example, Pavcnik (2002) for the case of Chile, Lileeva and Trefler (2010) for Canada, and Ha
and Kiyota (2014) for Vietnam.
6
A number of studies have examined the eﬀects of trade liberalization on resource reallocation. Strictly
speaking, however, that misallocation is diﬀerent from reallocation. On the one hand, reallocation means
the changes in resource allocation, which can be defined between time t − 1 and t. On the other hand,misallocation means allocative ineﬃciency, which can be defined at a specific point in time t.

2

out, heterogeneity in markups induced by trade barriers can be a source of misallocationof resources because, for example, trade barriers aﬀect the degree of competition and thus
markups. Less-competitive industries may underproduce while more-competitive industries
may overproduce, which results in the resource misallocation. Trade liberalization thus canbe expected to solve this problem. Furthermore, from a welfare point of view, the changes
in resource allocation through trade liberalization are not trivial (e.g., Melitz and Redding,2015). For a deeper understanding of the eﬀects of trade liberalization on aggregate TFP, anempirical analysis on the relationship between trade liberalization and variation in wedges
is necessary.
Building upon these two strands of the literature, this paper attempts to measure thedegree of misallocation in Vietnamese manufacturing between 2000 and 2009. The major
contributions of this paper are twofold. First, to the best of our knowledge, this paper isthe first study that examines the relationship between tariﬀ reduction and misallocation
directly. While Camacho and Conover (2010) also examined the relationship between tradeliberalization and misallocation in Colombia, their analysis simply compared misallocation
before and after trade liberalization without controlling for any other factors. Because thechanges in misallocation could be attributable to various factors, their estimates could be
biased.7 Second, this study examines the factors that aﬀect output and capital wedges
separately. As we will discuss, such analysis is helpful if the eﬀects on output wedges areoﬀset by those on capital wedges. This study thus contributes to the literature through amore precise analysis of the eﬀects of trade liberalization on misallocation.
Our research questions are threefold. 1) To what extent are resources misallocated in
Vietnam? 2) How large would the productivity gains have been in the absence of distortions? 3) Did the degree of misallocation decline after entry into the WTO? Answering thesequestions, this paper goes one step further by providing a deeper understanding of the gains
from trade. The rest of this paper is organized as follows. In Section 2, we describe Hsieh
and Klenow’s (2009) methodology. Section 3 describes the Vietnamese firm-level data used
7

Similarly, Eslava, Haltiwanger, Kugler, and Kugler (2013) focused on the eﬀects of trade reform. However, their focus is on the exit of establishments, not misallocation itself.

3

in our study. Section 4 presents our main findings. Section 5 discusses the robustness of ourresults. Concluding remarks and policy implications are presented in Section 6.

2

Measurement of Misallocation

Hsieh and Klenow (2009) formulated an analytical framework to estimate misallocation.
Although some studies such as Bartelsman, Haltiwanger, and Scarpetta (2013) developed
an alternative framework, this paper employs Hsieh and Klenow’s (2009) framework for thefollowing three reasons. First, their framework is tractable in the sense that it is simple andits data requirement is minimal. This is a significant advantage in estimating misallocationin Vietnam because of the limited data availability, as we will discuss in the next section.
Second, the framework allows us to decompose the source of misallocation into distortions in output markets and in capital markets. Such a decomposition is useful if the distortions come from diﬀerent sources. Finally, a comparison with other countries is relatively
easy because of the numerous related studies (see footnote 4). Hsieh and Klenow’s (2009)
methodology is summarized below.
Consider an economy with S manufacturing industries. Each industry produces output,
Ys , using Ms diﬀerentiated goods produced by individual firm i with a CES technology(s = 1, ..., S). Output in industry s is then given by:8

Ys =

(M
s
∑

σ−1
σ

Ysi

σ
) σ−1

σ > 1,(1)i=1where σ is the elasticity of substitution between varieties and Ysi is the output of the differentiated good produced by firm i in industry s, using capital and labor, based on thefollowing Cobb–Douglas technology:

Ysi = Asi Ksi αs Lsi 1−αs ,8(2)We suppress the time subscript to avoid heavy notation although we utilize firm-level panel data in theempirical analysis.

4where Asi , Ksi , and Lsi denote productivity, capital, and labor of firm i in industry s,
respectively; αs represents the capital share, which is diﬀerent across industries but thesame across firms within an industry.
To assess the extent of misallocation, Hsieh and Klenow (2009) follow Foster, Haltiwanger,
and Syverson (2008) in making a distinction between physical productivity, denoted by
TFPQ, and revenue productivity, denoted by TFPR:△

T F P Qsi = Asi =Ysiαs
Ksi Lsi 1−αs

(3)Psi Ysi,αs
Ksi Lsi 1−αs

(4)

and
△

T F P Rsi = Psi Asi =respectively, where Psi represents the firm-specific output price.
In addition to firm heterogeneity in terms of productivity (as in Melitz (2003)), firms
potentially face diﬀerent output and capital distortions. More specifically, Hsieh and Klenow(2009) incorporated two types of firm-level wedges into this framework. One raises themarginal product of capital and labor by the same proportion, which is denoted by τY si .
The other increases the marginal product of capital relative to labor, which is denoted byτKsi . These wedges are given from the firm’s viewpoint, and we do not make any assumptions
about what generates them. With these wedges, the expected profits of the firm are written
as:9
πsi = (1 − τY si )Psi Ysi − wLsi − (1 + τKsi )RKsi ,(5)where w and R denote the common wages and rental price facing all firms, respectively. In
the presence of distortions, firms will produce a diﬀerent quantity compared with what theywould produce without these wedges (i.e., eﬃcient case).
Solving the profit maximization problem under a monopolistic competition framework

9

Distortions to output and to capital relative to labor are an observationally equivalent characterizationof those to the absolute levels of capital and labor. For more details, see Hsieh and Klenow (2009, Appendix
III).

5

and the equilibrium allocation of resources across industries, we have:( )αs ()1−αsσRw
(1 + τKsi )αs
A−1
=
,
si
σ − 1 αs1 − αs1 − τY si
σ
wLsi
=
,
σ − 1 (1 − αs )Psi Ysi
αs wLsi.
=1 − αs RKsi

Psi
1 − τY si
1 + τKsi

(6)
(7)
(8)

From equation (6), we have:

T F P Rsi = ξs

where
σ
ξs =
σ−1

(

R
αs

(1 + τKsi )αs,
1 − τY si)αs (w
1 − αs

(9)

)1−αs
.

(10)

Noting that ξs is diﬀerent across industries but is constant within an industry, equation (10)implies:T F P Rsi ∝(1 + τKsi )αs.
1 − τY si(11)

This equation means that the large deviation of firm TFPR from ξ is a sign that the firm
faces large distortions. Denote industry TFP as T F Ps .
Define industry TFP as a weighted geometric average of firm i’s T F P Qsi :1[M ()σ−1 ] σ−1s∑
T F P RsT F Ps =T F P Qsi,
T F P Rsi
i=1

△

(12)

where T F P Rs is the geometric average of the average marginal revenue product of laborand capital in industry s:

T F P Rs

[
]αs [
]1−αs
σ
R
w
=
∑ s 1−τY si Psi Ysi∑ s
Psi Ysiσ − 1 αs M
(1 − αs ) M
i=1 1+τKsi Ps Ys
i=1 (1 − τY si ) Ps Ys
)αs (
)1−αs
(
σ
M RP K s
M RP Ls=,σ−1αs1 − αs△6(13)

where M RP Ks and M RP Ls are the average marginal revenue products of labor and capitalin industry s, respectively. There are two remarks regarding equation (12). First, the higher
the dispersion in TFPR, the lower the industry TFP will be. Hsieh and Klenow (2013)
showed that when TFPQ and TFPR were jointly log-normally distributed and that when
there is only variation in log(1 − τY si ), aggregate TFP can be expressed as follows:10

log T F Ps =

(
)] σ
1 [
σ−1
log Ms + log E T F P Qsi− var (log T F P Rsi ) .
σ−1
2

(14)

This equation suggests that industry TFP will decline if the elasticity of substitution σ
and/or TFPR dispersion increase.
Second, without any distortions (i.e., τKsi = τY si = 0), TFPR will be equalized acrossfirms within industry s. From equation (10), T F P Rsi = ξs ∀ i if τKsi = τY si = 0. Thisin turn implies that T F P Rsi = ξs = T F P Rs ∀ i.11 Denote industry TFP without any
distortions as T F P Qs . From equation (12), we can obtain:△

T F P Qs = Ās =

(M
s
∑

1
) σ−1

Aσ−1
si

,

(15)

i=1

which is called “eﬃcient” industry TFP.
Note that in order to obtain “eﬃcient” TFP, one needs information on firm-level TFPQ
(i.e., Asi ). One problem is the limited availability of firm-level price data, Psi , which are notavailable in many countries including Vietnam.12 Hsieh and Klenow (2009) rewrote equation(3) as:σ(Psi Ysi ) σ−1
T F P Qsi = Asi = κs αs 1−αs ,Ksi Lsi(Ps Ys )− σ−1
where κs =
.
Ps
1

(16)

Noting that κs is a scaling constant by industry and does not aﬀect the relative diﬀerences
A similar property is obtained even when there is variation in log(1 − τKsi ), although the equation
becomes more complicated. For more details, see Hsieh and Klenow (2013).
11
Note that even when TFPR is equalized across firms, TFPQ can be diﬀerent across firms because
productive firms charge lower prices (see equation (6)). In other words, if Asi > Asj and Psi < Psj , Psi Asi
could be equal to Psj Asj for i ̸= j.
12There are a few countries in which firm-level (or plant-level) price data are available. For example,
Eslava, Haltiwanger, Kugler, and Kugler (2004) utilized plant-level price data for Colombia to estimate
plant-level TFPQ.
10

7

between firms within industry s, it can be normalized to unity (i.e., κs = 1). This manipulation enables us to estimate TFPQ without firm-level price data. Note that from equations(4) and (16), T F P Qsi > T F P Rsi if κs = 1 and Psi Ysi ≥ 1. In Hsieh and Klenow’s (2009)
framework, therefore, the dispersion of TFPQ tends to be larger than that of TFPR.

3

Data

3.1

SourceThis paper utilizes firm-level data from the Annual Survey on Enterprises collected by theGeneral Statistics Oﬃce of Vietnam (GSO).13 The survey was conducted in the year 2000
for the first time and annually afterwards, to provide researchers and policy makers withcomprehensive information on Vietnamese firms. These data cover registered firms operating
in private industries, including agriculture, industry and construction, and services.
The survey covers all state-owned enterprises and foreign-owned firms without any firm
size threshold. However, as for domestic private firms, those with fewer than 10 workers arechosen by random sampling.14 Household business activities are also not covered in this survey.15 The survey information includes the type of ownership, assets and liabilities, numberof employees, sales, capital stock, the industry that the firm belongs to, and obligations to
the government (for example, taxes) from January to December of that year.
The data have some disadvantages. Some of the input data, such as materials, are only
available for some years. Information on working hours and capital utilization rates is also
unavailable. Firms’ year of establishment and export status are not available every year.
13

We use the same data used in Ha and Kiyota (2014). This section is based on Section 3 of Ha and Kiyota(2014). Note also that the use of firm-level data is more consistent with the theory than the use of plantlevel data. This is because, as Nishimura, Nakajima, and Kiyota (2005) pointed out, resource allocation
within a firm is determined as a result of managerial decisions. Moreover, such information as research anddevelopment or headquarter activities are typically classified as service activities, which are not covered in
the manufacturing survey.
14This threshold was used in surveys before 2010. From 2010, diﬀerent regions set diﬀerent firm size
thresholds. As a result of changes in the sampling method, it is diﬃcult for us to extend our sample period
beyond 2009.
15
The survey covered 62.2 percent of total employment in manufacturing in 2009. The data on total
employment in manufacturing are obtained from the GSO online database on population and employment
at http://www.gso.gov.vn

8

This paper utilized firms with information on inputs, outputs, and cost shares. There aresome reentry firms that disappeared and reappeared later, which are omitted in our analysis.
Some firms changed industry and/or ownership during the sample period.16 We drop firms
with fewer than 10 employees, regardless of their ownership, to avoid the eﬀects of the random
sampling.

3.2

Variables and parameters

The main variables that we use are the two-digit Vietnam Standard Industry Classificationindustry code, ownership type, value-added, employment, total labor costs and capital stock.
Following Hsieh and Klenow (2009), we use wage bills instead of working hours to capture
the potential diﬀerences in employees’ quality.17 Capital stock is total fixed assets recordedat the end of each year. Both wage bill and capital stock are deflated by the manufacturing
GDP deflator.18
To compute dispersion, we follow other research in setting key parameters σ and R as
follows. We assume that the elasticity of substitution σ equals 3 and R is 10 percent,comprising a 5 percent depreciation rate and a 5 percent interest rate. We also follow Hsieh
and Klenow (2009) to set αs to be one minus the labor share in the corresponding industry
in the US. Under Hsieh and Klenow’s framework, the output elasticities of capital and labor(i.e., αs and 1 − αs ) do not embed distortions. Given the assumption that the US economy
is less distorted than the Vietnamese economy, the use of the US shares can be justified.
The US labor share is obtained from the NBER-CES Manufacturing Industry Database,which is a joint product of the National Bureau of Economic Research and the US Census Bureau’s Center for Economic Studies.19 Industry classification is based on the North
American Industrial Classification System (NAICS) version 1997. Based on the data, we
16

If a firm has switched industry, the industry to which the firm belonged for the majority of the surveyed
years is regarded as that firm’s industry. If a firm belonged to more than one industry for equal amounts oftime, we assign the industry code of the industry that the firm belonged to most recently.
17
The use of wage bills as labor input implies that w = 1. See Camacho and Conover (2010, p. 10).
18
As Aw, Chen, and Roberts (2001) pointed out, it is preferable to utilize the investment goods pricedeflator rather than the manufacturing GDP deflator to obtain real capital stock. However, as Ha andKiyota (2014) discussed, the investment goods price deflator is not available for our data set.
19
Data can be downloaded from NBER’s website at http://www.nber.org/nberces/9first match the NAICS code with the four-digit Vietnam Standard Industrial Classification(VSIC) code using concordance tables between NAICS, ISIC revision 3, and VSIC. We thenaggregate total payroll and total value-added by two-digit VSIC sectors. To compute thelabor share, we take the ratio of total payroll over total value-added by sector. Because total
payroll in the database does not include fringe benefits and employer’s contribution to social
security, this labor share only reflects two-thirds of the aggregate labor share in the whole
manufacturing sector. Therefore, we follow Hsieh and Klenow (2009) to inflate the labor
shares by 3/2 to obtain US labor elasticities.
As firms’ output price is not available, we have obtained TFPQ by raising nominal output
to the power of σ/σ − 1, assuming that demand relationships hold. If a firm’s real output ishigh, one would expect its price to be low so that consumers demand more output. Following
Ziebarth (2013), the dispersion of TFP is defined as the deviation of the log of TFP from1

its industry mean: log(T F P Rsi /T F P Rs ) and log(T F P Qsi · Msσ−1 /T F P Qs ), where T F P Rs
and T F P Qs are from equations (13) and (15), respectively.20 We trim 2 percent of firm
productivity and distortions by cutting values below the 1st percentile and above the 99th
1

percentile from the distribution of log(T F P Rsi /T F P Rs ) and log(T F P Qsi · Msσ−1 /T F P Qs ).
Then we recalculate T F P Qs , T F P Rs , and T F P s , respectively. As robustness checks, Section
5 examines whether the results are sensitive to the value of σ, αs , and the threshold level oftrimming.

4

Results

4.1

To what extent are resources misallocated in Vietnam?

To answer this question, we compare the dispersions of TFP in Vietnam with those in India,
China, Japan, and the US. The dispersions of TFPR are reported in Table 1 while those of
TFPQ are reported in Table 2. Both tables indicate standard deviation, diﬀerences between
the 90th and 10th percentiles, diﬀerences between the 75th and 25th percentiles, and average
20

Note that some of the eﬀects of the changes in prices are controlled for by taking the ratio.

10per capita GDP during the sample period.21 Data on China, India, and the US are retrieved
from Hsieh and Klenow (2009). Data on Japan are retrieved from Hosono and Takizawa(2013).
=== Tables 1 & 2 ===These tables indicate that the standard deviation of TFPR for Vietnam is 0.79, whichis comparable to those for China (0.68) and India (0.68), and is larger than those for Japan(0.55) and the US (0.45). Similar patterns are also confirmed for the diﬀerences between the75th and 25th percentiles and those between the 90th and 10th percentiles.22 Although more
careful examination is needed in a direct comparison, the results suggest that distortions indeveloping countries, including Vietnam, tend to be large as opposed to those in developed
countries.

4.2

How large would the productivity gains be without distortions?

What productivity gains can be expected without distortions? To answer this question, we
estimate TFP gains when the marginal products of labor and capital are equalized acrossfirms within each industry. For each industry, the gains are computed as the ratio of actual
TFP obtained from equation (12) to the “eﬃcient” TFP obtained from equation (15). We
then aggregate the gains across industries using industry value-added shares as the weights.

21

Noting that both TFPR and TFPQ are divided by their industry means, these statistics can be interpreted as the coeﬃcients of variation.
22
The diﬀerence between the 75th and 25th percentile firms is 0.97, which corresponds to a TFP ratio of
0.97
e= 2.63. Similarly, the diﬀerence between the 90th and 10th percentile firms is 2.00, which corresponds
to a TFP ratio of e2.00 = 7.39. These figures are much larger than those of the US. For more details, see
Syverson (2011).

11

In particular, we compute:

1 θs
[M (
)θs
)σ−1 ] σ−1
S (
S 
s

∑
∏
∏
Y △
1
T F PsT F P Rs==T F P Qsi
 T F P Qs
Y ∗ s=1 T F P Qs
T F P Rsi
s=1
i=1

 θs
)σ−1 σ−1S
Ms (∏∑Asi T F P Rs

 ,
=
Ā
T
F
P
R
s
si
s=1
i=1

(17)

where Y ∗ is an “eﬃcient” output, which corresponds to “eﬃcient” TFP; θs is the value-added∑
share of industry s:s θs = 1. As the total amount of inputs is fixed, the output gains
come solely from the reallocation of resources in the absence of distortions.
Table 3 represents the TFP gains from equalizing TFPR across firms within each industry.
The gains are measured relative to the TFP gains in the US in 1997.23 To report thepercentage TFP gains in Vietnam relative to those in the US, we take the ratio of Y ∗ /Y to
the US equivalent in 1997, subtract 1, and multiply by 100. If Vietnam hypothetically moves
to “US eﬃciency,” substantial gains are expected: 30.7 percent. The gains are smaller thanthose for China (39.2 percent) and India (46.9 percent) but larger than those for Japan (3.0
percent).24 Although this is a hypothetical exercise and thus should not be taken literally,
the results suggest that substantial productivity gains are expected in Vietnam by the kindof reallocation considered here.
=== Table 3 ===4.3

Did misallocation decline after entry to the WTO?

If trade liberalization contributes to decrease misallocation, the dispersion in TFPR will
decline after entry to the WTO. To examine the changes in the dispersion in TFPR, Table
4 reports standard deviations, diﬀerences between the 90th and 10th percentiles, and dif23

Hsieh and Klenow (2009) called this comparison a conservative analysis because the US gains are largest
in 1997.
24
One may be concerned that the dispersion of TFPR is larger (Table 1), whereas the gains are smaller(Table 3) in Vietnam than in China and India. This is because Y /Y ∗ reflects not only T F P Rs /T F P Rsi ,but also Asi /Ās . The result thus suggests that physical productivity is lower in Vietnam than in China andIndia.

12

ferences between the 75th and 25th percentiles of TFPR in Vietnam, by year.25 For moredetails about the sample selection, see Ha and Kiyota (2014). Based on the fact that Vietnam
joined the WTO in 2007, we expect that the dispersion of TFPR declined from 2007.
=== Table 4 ===Contrary to expectations, the dispersion of TFPR does not show any systematic patterns
before or after entry to the WTO. The arithmetic average of the standard deviations is 0.78
for 2000–06, which is slightly smaller than the average standard deviation for 2007–09 (0.79).
If the dispersion is measured by the diﬀerences between the 90th and 10th percentiles, thedispersion is larger for 2007–09 than for 2000–06.
As mentioned above, a simple comparison before and after entry to the WTO may not
be able to reveal the eﬀects of trade liberalization correctly because misallocation can beaﬀected by various factors. Besides, unobserved industry heterogeneity may also aﬀect theresults. To control for the eﬀects of other factors on the dispersion of TFPR, following
Guadalupe and Wulf (2010), we run the following regression:sd(log(T F P Rsi /T F P Rs )) = γAverage tariﬀ rates × WTOt
+λAverage liquidity ratios × Financial Crisist+X′st β + dt + ηs + ηs × t + εst ,(18)

where sd(T F P Ri )st is the dispersion of T F P Ri in industry s in year t; Average tariﬀ rates
is an average most-favored-nation (MFN) tariﬀ rate in industry s before 2007;26 WTOt is adummy variable that equals 1 if t ≥ 2007 and 0 otherwise; Average liquidity ratios is the industry average of the ratio of short-term assets to total assets before 2008;27 Financial Crisist25

One may be concerned that the number of firms changed dramatically in some years. Although the number of firms in the Annual Survey on Enterprises increased dramatically, a part of this change is attributableto our sample selection, because we use firms for which information on inputs, outputs, and cost shares isavailable. Section 5 examines the robustness of our results, using balanced panel data.
26
Note that the MFN tariﬀ is what countries commit to impose on imports from other WTO members.
Therefore, the MFN tariﬀ rate is used so that we can disentangle the impact of the WTO accession fromthat of other free trade agreements coming into eﬀect during the 2000s.
27
Short-term assets include cash, cash-equivalents, short-term financial investment, receivables, inventories,
and other short-term assets. Liquidity could be used to measure the ease with which firms respond to financial

13

is a dummy variable that equals 1 if t ≥ 2008 and 0 otherwise; Xst includes control variables;
dt is the year dummy, ηs is the unobserved industry fixed eﬀect; ηs × t is the industry-specifictime trend; and εst is an error term.
The coeﬃcient of interest, γ, captures the diﬀerential eﬀect of the liberalization on firms
according to their trade exposure prior to 2007. As was pointed out by Guadalupe andWulf (2010), this is a standard diﬀerences-in-diﬀerences specification that exploits tradeliberalization in which Average tariﬀ rates (the “treatment”) is continuous. If the accession
to the WTO has contributed to the decline in misallocation, γ will be significantly negative.
Our data cover not only the year 2007 when Vietnam joined the WTO, but also theyears 2008–09 when the global financial crisis hit the Vietnamese economy. One may thus
be concerned about the eﬀect of credit market constraints associated with the financial crisis(e.g., Restuccia and Rogerson, 2013). To control for such an eﬀect, we include the interaction
term between the average liquidity ratio (prior to 2008) and the financial crisis dummy. The
coeﬃcient λ captures the diﬀerential eﬀect of the global financial crisis on firms according
to their liquidity constraint prior to the financial crisis. If misallocation expands amongliquidity constrained firms after the global financial crisis, λ will be significantly negative.
The dispersion of TFPR is measured by the standard deviation, as in Tables 1 and 4.
The annual average MFN tariﬀ rate is available at the industry level and obtained from theWorld Integrated Trade Solution (WITS) developed by the World Bank.28 As for the control
variables, we use the number of workers (log value) in the industry, the Herfindahl–HirschmanIndex (HHI), the value-added share of state-owned enterprises, and the US bilateral tariﬀ
rates on Vietnamese imports.
The number of workers is used to control for industry size because the dispersion of
TFPR could be larger in industries with a larger number of firms (and thus a larger numberof employees). HHI is included to control for market structure. More concentrated mar-shocks. Firms with greater liquidity are more likely to be able to accommodate exogenous financial shocks
and thus are more resilient to financial crises (e.g., Campello, Graham, and Harvey, 2010). The measurementof financial constraints is an important issue (e.g., Hadlock and Pierce, 2010) but it is diﬃcult for us to employ
alternative measures because of limited data availability.
28
Changes in average MFN tariﬀ rates are presented in Table A1.

14

kets are more likely to be distorted because of the stronger market power of firms. The
value-added share of state-owned enterprises captures the possible eﬀect of state ownershipon distortions.29 The number of workers, HHI, and the share of state-owned enterprises arecomputed from the sample.30 The US bilateral tariﬀ rates on Vietnamese imports are included to control for the possible eﬀects of the US–Vietnam bilateral trade agreement (BTA).
The annual average bilateral tariﬀ rate is available at the industry level and obtained fromthe WITS developed by the World Bank.
A few points of caution related to the regression equation should be made here. The
first relates to the WTO dummy. While Vietnam established regional as well as bilateralfree trade agreements throughout the sample period, we observe a substantial reduction
in tariﬀs following 2007 when Vietnam gained WTO membership. Therefore, we use thisyear as a benchmark.31 Second, tariﬀ rates themselves could be determined endogenously.
For example, the declining industries may have high tariﬀ levels. Trefler (2004) addressed
this concern by controlling for industry-specific trends. Therefore, the potential endogeneity
biases can be absorbed by the industry secular trends ηs × t. Third, Average tariﬀ rates andAverage liquidity ratios are not included because of the perfect collinearity with industry
fixed eﬀect ηs . Finally, we estimate in levels rather than first diﬀerences because the data
cover a relatively short period.
Table 5 presents the regression results.32 Four findings stand out from this table. First,
the coeﬃcient of Average tariﬀ rates × WTOt is insignificant in almost all specifications.
Second, similarly, Average liquidity ratios × Financial Crisist is insignificant in almost all29

A state-owned enterprise (SOE) is defined as an enterprise for which state capital accounts for morethan 50 percent of the charter capital.
30
In the GSO’s Annual Enterprise Survey from 2002, there are five types of SOEs: 1) Central SOE, 2)
Local SOE, 3) Central SOE Limited Company, 4) Local SOE Limited Company, and 5) Joint Stock Company(JSC) with state capital of more than 50 percent. The categorization of SOEs is consistent between 2002
and 2009. Before 2002, there was no distinction between JSCs with state capital of no more than 50 percent
and JSCs with state capital of more than 50 percent. Therefore, we omit years 2000 and 2001 from ourregression when we include the value-added shares of SOEs.
31
Previous studies such as Rose (2004) introduced a WTO dummy that equals 1 at the year of accession,and 0 otherwise. One may be concerned that Vietnamese firms anticipated the WTO accession and, therefore,
the change might appear before 2007. To address this concern, we will conduct a placebo test.
32
The fixed-eﬀect model is chosen because the Hausman specification test rejects the null hypothesis thatthe random eﬀect is orthogonal to the independent variables.

15

specifications. These results together imply that neither entry into the WTO nor the global
financial crisis had significant eﬀects on the dispersion of TFPR.
=== Table 5 ===Third, state ownership, size of the industry, market structure, and the US tariﬀ have noeﬀects on the dispersion of TFPR, as none of the coeﬃcients of these variables is significant.
It may be surprising that US tariﬀ rates had significant eﬀects on misallocation. Indeed,
the result seems to be inconsistent with that of McCaig and Pavcnik (2014) who foundsignificant eﬀects of the US–Vietnam BTA on labor allocation. Note, however, that McCaig
and Pavcnik (2014) mainly found labor reallocation from informal household businesses tothe formal enterprises. Because our data do not cover household business activities andfirms with fewer than 10 workers, the diﬀerence in the results may be attributable to thediﬀerences in the coverage of the data.
Finally, trade liberalization does not seem to be anticipated. We replace the WTO
dummy in Average tariﬀ rates × WTOt with WTOt−1 (equals 1 from 2006 onwards), keeping
the same set of controls (which is a standard placebo test for diﬀerences-in-diﬀerences).
On the one hand, if the liberalization was anticipated, this new variable would capture a
discrete shock before it occurred. On the other hand, if it is zero, it provides support tothe maintained hypothesis that the shock was unanticipated. The result shows that thecoeﬃcient is insignificant. The result suggests that the eﬀect of anticipation is negligibly
small for many of the Vietnamese firms. In other words, the Vietnamese firms only started
to respond after 2007.
It is then natural to ask why the dispersion of TFPR did not decline after trade liberalization. The next section will address this issue in more detail.

4.4

Does trade liberalization aﬀect both output and capital wedges?

To examine further the eﬀect of the WTO accession on distortions, we investigate the eﬀects
of trade liberalization on output and capital wedges separately. As equation (11) indicates,
16

TFPR depends on both output and capital wedges. A concern may be that trade liberalization aﬀects only one of the wedges. Because trade liberalization directly aﬀects output (andinput) prices, its eﬀect is likely to appear in a decline in output wedges rather than a decline
in capital wedges.33 To address this concern, we first decompose the distortions in output
and capital markets and examine how they change over the period.
Table 6 presents the mean, median, and standard deviation of 1 − τY si and (1 + τKsi )αs inequations (7) and (8). Two messages are evident from this table. First, both the mean andmedian of 1 − τY si experience significant declines beginning in 2007. The standard deviation
also decreased from 2.47 to 1.74 in 2007. Second, both the mean and median of (1 + τKsi )αs
increased steadily from 2000 to 2009. Moreover, its standard deviation increased dramaticallyfrom 2008. This result suggests that more firms faced distortions in capital markets after
the global financial crisis. The results seem to suggest that trade liberalization aﬀects the
distortions in output markets even during the global financial crisis, although the increases
in the distortions in capital markets oﬀset the positive eﬀect of trade liberalization.34=== Table 6 ===One may then ask whether the wedges themselves (i.e., τY si and τKsi ) declined after the
trade liberalization. To see the relationship between trade liberalization and two wedges
while controlling for firm heterogeneity, similar to equation (18), we run the following regression:|τj,ist | = γAverage tariﬀ rates × WTOt + λAverage liquidity ratioi × Financial Crisist+X′ist β + dt + φi + ηs × t + εist ,(19)

where |τj,ist | is the absolute value of the output wedges (j = Y ) and capital wedges (j = K)
for firm i in industry s in year t; Average tariﬀ rates is the average MFN tariﬀ rate in
33

Note that output is measured by value added (i.e., net output). The output wedges in this paper reflect
the distortions in both output and intermediate input markets.
34
One may be concerned that there is a large gap between the mean and median of the capital wedges.
This may reflect measurement error of the capital stock. We discuss this issue in Section 5.

17

industry s before 2007; WTOt is a dummy variable that equals 1 if t ≥ 2007 and 0 otherwise;
Average liquidity ratios is the industry average of the ratio of short-term assets to total assets
before 2008; Financial Crisist is a dummy variable that equals 1 if t ≥ 2008 and 0 otherwise;
Xist includes control variables; φi is a firm fixed eﬀect; ηs × t is the industry-specific timetrend; and εit is an error term.
We focus on |τY | and |τK | rather than 1 − τY and 1 − τK because theoretically both τYand τK could take positive and negative values. We expect a significantly negative γ if theaccession to the WTO contributes to the decline in the output and capital wedges. Similarly,
we expect a significantly negative λ if the output and capital wedges increase among liquidity
constrained firms after the global financial crisis. As for the control variables, we utilize thenumber of workers (log value) to control for firm size, and HHI to control for market structure,
as in the previous section.35
The left panel of Table 7 presents the estimation results for the output wedges. Standard
errors are clustered by firm. Four findings are evident from the results. First, the coeﬃcient of
Average tariﬀ rates × WTOt indicates significantly negative coeﬃcients in all specifications.
This result suggests that trade liberalization through the WTO contributed to declines in
the output wedges. Second, the coeﬃcient of Average liquidity ratioi × Financial Crisist is
insignificant once we control for the year fixed eﬀect. This result implies that the global
financial crisis does not have significant eﬀects on the output wedges.
=== Table 7 ===Third, the coeﬃcients of firm size are significantly negative, implying that the smallerfirms face larger distortions in the output market. Finally, the coeﬃcient of Average tariﬀ rates ×
WTOt−1 has an insignificant coeﬃcient. The results suggest that the liberalization was anunanticipated shock for many of the firms.
The right panel of Table 7 presents the estimation results for the capital wedges. There are
four major findings. First, Average tariﬀ rates × WTOt has an insignificant coeﬃcient once
35

Most of the firms did not change ownership status throughout the period. Therefore, the (time-invariant)eﬀect of state ownership is absorbed by the firm fixed eﬀect φi .

18

we control for the year fixed eﬀect. This result implies that trade liberalization does not aﬀect
the capital wedges. Second, the coeﬃcients of Average liquidity ratioi × Financial Crisist are
significantly negative in all specifications. These results suggest that liquidity constrained
firms were more likely to face higher capital wedges during the global financial crisis period.
Third, the coeﬃcients of firm size are significantly positive, implying that the larger firms
faced larger distortions in capital markets. This result implies that large firms are treated differently from small firms in capital markets. Finally, the coeﬃcient of Average tariﬀ rates ×
WTOt−1 has an insignificant coeﬃcient, suggesting that many firms did not anticipate theliberalization and thus can be treated as experiencing a “shock.”
These results together suggest that the positive eﬀect of trade liberalization was oﬀset
by the negative eﬀect of the global financial crisis. A recent study by Midrigan and Xu(2014) found that the financial frictions generate dispersions in the returns to capital acrossexisting firms, which results in productivity losses from misallocation.36 Our results are
consistent with their finding. Another possible source of distortion is zombie lending and/or
subsidies: bank lending or government subsidies might allow many unprofitable firms tocontinue operating rather than exit. For example, during the banking crisis around 1997 in
Japan, less productive firms were more likely to survive because of zombie lending.37 Ha andKiyota (2014) found a relatively strong negative net entry eﬀect for 2008–09 in Vietnam,
which seems to support this possibility.
In sum, the results suggest that tariﬀ reduction after the accession to the WTO contributed to the decline in the output wedges. However, the increases in the capital wedges,
which are possibly attributable to the global financial crisis, oﬀset the positive eﬀects of tradeliberalization. As a result, overall misallocation did not decrease after the WTO accession.
These results imply that trade liberalization is not a panacea. Further reforms in capital
markets could improve aggregate TFP in Vietnam through a reduction in misallocation.

36

Similarly, Abiad, Oomes, and Ueda (2008) found that financial liberalization was associated with improved allocative eﬃciency in India, Jordan, Korea, Malaysia, and Thailand.
37
For more details about zombie lending, see Caballero, Hoshi, and Kashyap (2008).

19

5

Robustness Check

5.1

Diﬀerent parameter values

One may be concerned that our analysis is sensitive to the choice of parameter values because
our results are based on specific parameter values such as σ = 3. To address this concern,
we reconduct all the analyses, setting diﬀerent parameter values. Because it is tedious to
examine all the results, this section examines 1) how sensitive the estimated TFPR and TFPgains (reported in Section 4.2 and in Table 3) are to the choice of parameter values, and 2)
the correlation between alternative and baseline TFPR.
We first examine whether the results are sensitive to the value of the elasticity of substitution: σ. In the baseline analysis, following Hsieh and Klenow (2009), we set σ = 3. Thisimplies that the markup is 1.5 (= 3/(3 − 1)). As a robustness check, we set σ = 2 and σ = 6,
and the corresponding markups are 2 (= 2/(2 − 1)) and 1.2 (= 6/(6 − 1)), respectively.
The second and third columns in Table 8 present the results. The TFP gains are somewhat
sensitive to the value of the elasticity of substitution. The TFP gains are 15.6 percent whenσ = 2 and 83.3 percent when σ = 6.38 Nevertheless, the estimated TFPR is qualitatively
similar to the baseline results. Table 8 also reports the correlation with baseline TFPR.
The correlation with baseline TFPR is 0.997 when σ = 2 and 0.994 when σ = 6. Thesehigh correlations suggest that the results are quantitatively diﬀerent from, but qualitatively
similar to, the baseline results.39=== Table 8 ===We also examine how sensitive the results are to the value of the technology parameter(i.e., capital share αs ). We examine two diﬀerent technologies. One is αs = 1/3 as inZiebarth (2013) and the other is the Vietnamese cost share, which is defined as the industryyear average capital share of the sample firms. The results are presented in the fourth and38

This result is consistent with equation (14), which implies that the TFP gains will be large if the elasticity
of substitution is large.
39
It may also be important to allow the elasticities to vary across industries. Although Broda, Greenfield,
and Weinstein (2006) estimated the elasticity of substitution for various countries, Vietnam is not coveredin their analysis. We thus leave this exercise for future research.

20

fifth columns in Table 8. The TFP gains are 19.1 percent when αs = 1/3 and 69.2 percent
when we assume Vietnamese technology. The correlation with the baseline TFPR is 0.927
when αs = 1/3 and 0.933 when we assume Vietnamese technology. Similar to the value ofthe elasticity of substitution, the results are quantitatively diﬀerent from, but qualitatively
similar to, the baseline results.
One may also be concerned that the technology parameter αs is heterogeneous acrossfirms even within industries. To address this concern, we use the firm-level capital share sothat the capital share can vary across firms.40 The results are presented in the sixth column
in Table 8, which are similar to the baseline results, both quantitatively and qualitatively.
The TFP gains are 29.6 percent. The correlation with the baseline TFPR is 0.923. These
results together suggest that our results are not sensitive to the value of the technology
parameter.

5.2

Measurement error

Another concern may be that the data are not precise and thus Vietnamese firm-level data aresubject to measurement error problems. Although we cannot rule out arbitrary measurement
error, we can try to gauge whether our results are attributable to some specific forms of
measurement error. We focus on two forms of measurement error. First, serious measurement
error, possibly because of reporting error, tends to appear as outliers. We trimmed 2 percent
from the tails (below the 2nd percentile and above the 98th percentile), instead of 1 percent
as in the baseline analysis, and examined how sensitive the results are to the trim values.
The seventh column in Table 8 reports the results. The TFP gains are 22.9 percent. The
correlation with the baseline TFPR remains high at 0.997.
Second, the measurement error could also aﬀect firm “exit.” Reporting error could cause
missing values, which is regarded as “exit” in the sample. Even eﬃcient firms could exit
because of such reporting errors. If such a form of measurement error is serious in the40

Note that ξs can vary across firms if the capital share is diﬀerent across firms (see equation (10)).
Therefore, TFPR will not necessarily be proportional to the capital and output wedges. We thus present
the results for reference only.

21

Vietnamese data, the coeﬃcient from a regression of plant exit on TFPR will be biased
downward.
To address this issue, we run a regression of the dummy exit on TFPR and TFPQ. The
results are presented in Table 9. There are two major findings in this table. First, TFPR
has a significantly negative correlation with exit status. A one-log-point decrease in TFPR
results in a 1.2 percent higher probability of exit. This figure is comparable to the case of
China and the US (1.1 percent) and smaller than that of India (1.9 percent) in Hsieh andKlenow (2009, Table 8). If the probability of exit is the same across countries, the result
suggests that the eﬀect of the measurement error is not serious in Vietnam.41=== Table 9 ===Second, the correlation between TFPQ and firm exit is also significantly negative. In
particular, a one-log-point decrease in TFPQ results in a 3.8 percent probability of exit.
This figure is comparable to that for the US (3.9 percent) but smaller than that for China(5.0 percent). If the relationship between TFPQ and firm exit is the same across countries,
the results imply that measurement error for Vietnam is comparable to that for the US. In
sum, our results do not seem to be aﬀected substantially by various forms of measurement
error.42

6

Concluding Remarks

This paper employs the Hsieh and Klenow (2009) framework to investigate misallocation andproductivity linkage in Vietnamese manufacturing during the 2000–09 period using firm-level
data. Our major findings are threefold. First, misallocation in Vietnam is comparable tothat in China and India. This result is consistent with the common knowledge that resources
41

Note that the analysis covers not only the global financial crisis period but also other periods. The
results thus do not necessarily rule out the possibility of zombie lending and/or subsidies.
42
We also estimate the TFP gains for firms that survive throughout the sample period (i.e., balanced
panel). This exercise reduces the sample size substantially (N = 15,977). The estimated TFP gains are 19.9
percent, and the correlation with baseline TFP is 0.965. We can conclude that the results from the balanced
panel are similar qualitatively to the baseline results.

22

in developing countries are not eﬃciently allocated. Second, there would be substantial
improvement in TFP if no distortions existed. If Vietnam hypothetically moved to “USeﬃciency,” its TFP would be boosted by 30.7 percent. Finally, the accession to the WTO
contributed to reducing the distortions in output markets. However, this positive eﬀect was
oﬀset by increasing distortions in capital markets, which are possibly attributable to theglobal financial crisis. These results together suggest that further reforms in capital markets
could improve aggregate TFP in Vietnam through reduced misallocation.
In conclusion, there are several research issues for the future that are worth mentioning.
First, although our study is based on Hsieh and Klenow’s framework, their framework itself relies upon a number of restrictive assumptions such as CES/Cobb–Douglas functional
forms.43 It is thus important to extend the analysis to a more general framework, while
minimizing the data requirements. Second, our sample period may not be long enough to
examine the eﬀects of trade policy because the eﬀects could appear over a longer time span.
As a result of the changes in the sample selection of the firm-level data in Vietnam, it isdiﬃcult for us to extend the period of our analysis. However, our approach is applicable toother countries. An extension of our analysis to other countries, therefore, is an importantavenue for future research. Some of these issues will be investigated in the next stage of ourresearch.

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27

Table 1. Dispersion of TFPR in China, India, Japan, the United States, and Vietnam
Vietnam
2000-2009
0.79
0.97
2.00

China
1998-2005
0.68
0.89
1.72

India
1987-1994
0.68
0.80
1.66

Japan
1981-2008
0.55
0.70
1.40

United States
1977-1997
0.45
0.47
1.08

S.D.
75-25
90-10
GDP per capita(constant 2005
685
1,299
400
31,10830,501US$)
Note: The numbers for China, India, and the United States are arithmetic averages of Hsieh and Klenow(2009, Table 2). The numbers for Japan are obtained from Hosono and Takizawa (2013). TFPR is the log
value, scaled by industry TFPR. Industries are weighted by value-added shares. For more detail, see maintext. GDP per capita is annual average over each sample period.

Source: Author's calculation, based on the Annual Survey on Enterprises by the General Statistics Office ofVietnam. Per-capita GDP is obtained from World Bank (2014).
Table 2. Dispersion of TFPQ in China, India, Japan, the United States, and Vietnam
Vietnam
China
India
Japan
United States
2000-2009
1998-2005
1987-1994
1981-2008
1977-1997
S.D.
1.42
1.00
1.19
0.98
0.83
75-25
2.01
1.34
1.56
1.27
1.16
90-10
3.70
2.57
3.03
2.48
2.15
Note: The numbers for China, India, and the United States are arithmetic averages of Hsieh and Klenow(2009, Table 1). The numbers for Japan are obtained from Hosono and Takizawa (2013, Table 1). TFPQ is
the log value, scaled by industry TFPR. Industries are weighted by value-added shares. For more detail, seemain text.
Sources: Hsieh and Klenow (2009, Table 1) and author's calculation, based on the Annual Survey onEnterprises by the General Statistics Office of Vietnam.

Table 3. TFP Gains from Equalizing TFPR Relative to 1997 U.S. Gains
Vietnam
China
India
Japan
2000-2009
1998-2005
1987-1994
1981-2008
%
39.3
46.9
30.7
3.0
Note: The numbers for China, India and the United States are arithmetic averages of
Hsieh and Klenow (2009, Table 6). The numbers for Japan are from Hosono and Takizawa(2013, Table 2).
Sources: Hsieh and Klenow (2009, Table 6) and author's calculation, based on the Annual
Survey on Enterprises by the General Statistics Office of Vietnam.

Table 4. Dispersion of TFPR in Vietnam, by Year

p10
90-10
Year
N
S.D.
p25
p75
p90
75-25
0.71
0.85
1.74
2000
5,484
-0.88
-0.41
0.43
0.86
2001
5,398
0.75
-1.06
-0.55
0.39
0.81
0.94
1.86
2002
6,550
0.77
-0.99
-0.49
0.42
0.88
0.92
1.88
2003
7,346
0.79
-1.03
-0.50
0.46
0.94
0.97
1.97
-1.08
2.09
2004
8,248
0.82
-0.52
0.49
1.01
1.01
0.83
1.05
2005
12,360
-1.17
-0.63
0.42
0.92
2.10
2006
10,507
0.83
-1.07
-0.48
0.54
1.03
1.02
2.10
2007
13,098
0.84
-1.13
-0.54
0.50
1.02
1.03
2.14
2008
16,556
0.71
-0.89
-0.45
0.40
0.87
0.85
1.76
2009
15,054
0.82
-1.08
-0.49
0.50
1.03
1.00
2.11
Total
100,601
0.79
-1.05
-0.51
0.46
0.95
0.97
2.00
Note: Statistics are for the deviation of log(TFPR) from industry means. N is the number of firms.
S.D. is standard deviation. 75-25 is the difference between 75th and 25th percentiles, and 90-10 the
90th vs. 10th percentiles. Industries are weighted by value-added shares.
Source: Author's calculation, based on the Annual Survey on Enterprises by the General Statistics
Office of Vietnam.

Table 5. Dispersion of TFPR and the Accession to the WTO
Dependent variable: Standard deviation of TFPR, by industry(1)
Average tariff rate * WTO dummy
Average liquidity ratio* Financial crisis dummy
Average tariff rate * WTO(-1) dummy(placebo)
US bilateral tariff on Vietnamese imports

0.001(0.001)-0.032(0.027)

(2)

(3)

(4)

-0.000(0.001)0.291*(0.163)-0.003(0.002)0.024(0.165)-0.004**(0.002)-0.043(0.136)

198
0.429
Yes
Yes
Yes

0.002
(0.001)-0.069(0.074)0.493(0.403)0.140(0.144)
168
0.486
Yes
Yes
Yes

ln(Employment)
Herfindahl-Hirschman Index (HHI)
Share of state-owned enterprises
NR-squared
Industry-fixed effectYear-fixed effectIndustry-specific time trend

198
0.011
Yes
No
No

198
0.101
Yes
Yes
No

(5)

-0.038(0.147)0.002(0.002)-0.000(0.002)-0.074(0.075)0.564(0.426)0.121(0.138)
168
0.456
Yes
Yes
Yes

Note: Robust standard errors are in parentheses. ***, **, and * are statistically significant at 1%, 5%, and
10% levels, respectively. WTO dummy equal one from 2007 onward while WTO(-1) dummy equal one from2006 onward (placebo test). Due to the availability of the information on state ownership, columns (4)-(5)cover the period for 2002-09 while other columns (1)-(3) covers the period for 2001-09 due to theavailability of the information on state ownership. Constant is included (but not reported to save space).
Source: Author's calculation, based on the Annual Survey on Enterprises by the General Statistics Office ofVietnam.

Table 6. Distortions in Output and Capital Markets, by Year
Distortions in output market
Distortions in capital market
Year MeanMedianS.D.
MeanMedian
S.D.
2000
1.97
1.74
1.71
2.55
1.71
4.92
2001
2.01
1.73
2.02
2.74
1.68
7.86
2002
2.15
1.87
2.27
2.90
1.84
5.13
2003
2.14
1.90
1.75
3.19
1.95
5.79
2004
2.26
1.90
2.59
3.60
2.12
6.78
2005
2.25
1.89
2.34
3.88
2.17
9.16
2006
2.18
1.83
2.47
4.09
2.29
8.55
2007
2.02
1.77
1.74
4.21
2.35
8.45
2008
1.75
1.53
1.53
3.82
2.04
13.16
2009
1.74
1.46
1.90
3.58
1.92
20.54
Average
2.01
1.69
2.04
3.62
2.04
11.47
Note: Distortions in output and capital markets are computed, based onequations (7) and (8) in the main text, respectively. For more detail, see maintext.
Source: Author's calculation, based on the Annual Survey on Enterprises by theGeneral Statistics Office of Vietnam.

Table 7. Output Wedges, Capital Wedges, and the Accession to the WTO
Dependent variable:(1)
Average tariff rate * WTO dummy
Average liquidity ratio* Financial crisis dummy
Average tariff rate * WTO(-1) dummy(placebo)
US bilateral tariff on Vietnamese imports(2)

Output wedges(3)-0.009*** -0.008*** -0.005** -0.007***(0.003)(0.001)(0.002)(0.003)-0.370***
0.047
0.051
0.049
(0.029)(0.083)(0.086)(0.086)ln(Employment)
Herfindahl-Hirschman Index (HHI)N
R-squared
Firm-fixed effectYear-fixed effectIndustry-specific time trend(4)78,037
0.013
Yes
No
No

78,037
0.015
Yes
Yes
No

78,037
0.018
Yes
Yes
Yes

(5)

0.056
(0.086)-0.003(0.003)0.003
0.002(0.002)(0.002)-0.076*** -0.075***(0.019)(0.019)0.979
0.324(1.191)(1.177)
77,74977,749
0.018
0.018
Yes
Yes
Yes
Yes
Yes
Yes

(6)

(7)

Capital wedges
(8)

(9)

(10)

-0.090*0.147***
0.092-0.067(0.050)(0.042)(0.123)(0.047)-3.797*** -16.316*** -17.529*** -16.444*** -16.373***(1.311)(3.704)(3.789)(3.666)(3.677)0.003(0.053)0.033
0.025(0.056)(0.056)
3.881*** 3.889***(0.438)(0.438)19.58310.965(17.730) (16.436)
78,03778,03778,037
77,74977,749
0.001
0.003
0.012
0.005
0.005
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
No

Note: Satandard errors are clustered by firm. ***, **, and * are statistically significant at 1%, 5%, and 10% levels, respectively. WTO dummy equal one from 2007
onward while WTO(-1) dummy equal one from 2006 onward (placebo test). Constant is included (but not reported to save space).
Source: Author's calculation, based on the Annual Survey on Enterprises by the General Statistics Office of Vietnam.

Table 8. Robustness Check: TFP Gains from Equalizing TFPR Relative to 1997 U.S. Gains(1)
Baseline(2)
Robustness
check 1

Elasticity ofsigma = 3
sigma = 2
substitution
Technology (capital
United States United Statessahre)Trim
1%
1%
TFP Gains (%)
30.7
15.6
N
100,601
100,601
Correlation with
1.000
0.997
baseline TFPR
Note: Baseline is obtained from Table 3.

(3)
Robustness
check 2

(4)
Robustness
check 3

(5)
Robustness
check 4

(6)
Robustness
check 5

(7)
Robustness
check 6sigma = 6

sigma = 3

sigma = 3

sigma=3

sigma = 3

United States

1/3

VietnamFirm-specificUnited States

1%
83.3
100,6121%
19.1
100,8481%
69.2
100,8791%
29.6
100,8302%
22.9
97,263

0.994

0.927

0.933

0.923

0.997

Sources: Hsieh and Klenow (2009, Table 6) and author's calculation, based on the Annual Survey on Enterprises by the General Statistics
Office of Vietnam.

Table 9. TFPR, TFPQ, and Exit
Dependent variable: Exit dummy-0.012***
TFPR(0.002)
TFPQ-0.038***(0.001)
0.234***
0.181***
Constant(0.001)(0.002)
100,601
100,601N
0.00040.0146R-squared
Note: Regressions are weighted least squares, with industry valueadded share being the weight. Robust standard errors are inparentheses. *** is statistically significant at 1% level. Results arepooled for all years between 2000 and 2009.
Source: Author's calculation, based on the Annual Survey onEnterprises by the General Statistics Office of Vietnam.

Table A1. Annual Average MFN Tariff Rate, by Industry and by Year

Industry
1 Manufacture of food products and beverages
2 Manufacture of tobacco products
3 Manufacture of textiles
4 Manufacture of wearing apparel; dressing and dyeing of fur
Tanning and dressing of leather; manufacture of luggage, handbags, saddlery,5harness and footwear
Manufacture of wood and of products of wood and cork, except furniture;6
manufacture of articles of straw and plaiting materials
7 Manufacture of paper and paper products
8 Publishing, printing and reproduction of recorded media
9 Manufacture of coke, refined petroleum products and nuclear fuel10 Manufacture of chemicals and chemical products
11 Manufacture of rubber and plastics products
12 Manufacture of other non-metallic mineral products
13 Manufacture of basic metals
14 Manufacture of fabricated metal products, except machinery and equipment
15 Manufacture of machinery and equipment n.e.c.
16 Manufacture of office, accounting and computing machinery
17 Manufacture of electrical machinery and apparatus n.e.c.
Manufacture of radio, television and communication equipment and18
apparatus
Manufacture of medical, precision and optical instruments, watches and19
clocks20 Manufacture of motor vehicles, trailers and semi-trailers
21 Manufacture of other transport equipment
22 Manufacture of furniture; manufacturing n.e.c.
23 Recycling
Source:World Bank (2014) World Integrated Trade Solution (WITS).

(%)

2000
NA
NA
NA
NA

2001
30.6
65.0
32.3
47.7

2002
30.5
65.0
32.2
47.4

2003
31.9
65.0
32.6
47.7

Year
2004
32.1
65.0
32.6
47.7

NA

30.2

30.3

28.9

28.9

28.9

28.9

28.9

22.9

21.2

NA

13.1

13.1

12.7

12.7

12.7

12.7

12.7

11.0

10.2

NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA

17.4
20.1
2.5
3.5
16.3
20.5
2.8
16.7
6.3
5.4
11.7

17.7
19.6
2.7
3.4
16.4
20.6
3.0
17.0
6.4
5.4
12.1

18.5
19.1
4.9
3.8
16.9
22.2
3.1
17.9
6.5
5.2
12.5

18.5
19.1
4.9
3.8
16.9
22.3
3.1
17.9
6.5
5.2
12.5

18.5
19.1
4.9
3.8
16.9
22.2
3.1
17.9
6.5
5.2
12.5

18.5
19.1
4.9
3.8
16.9
22.2
3.1
17.9
6.5
5.2
12.5

18.5
19.1
4.9
3.8
16.9
22.2
3.1
17.9
6.5
5.2
12.5

15.0
14.4
2.8
2.9
14.7
19.0
2.1
15.5
4.9
3.3
10.4

13.9
13.2
5.1
2.8
13.9
18.3
2.1
14.9
4.6
2.7
9.9

NA

17.6

15.2

14.6

14.6

14.6

14.6

14.6

9.6

9.7

NA

10.2

10.1

10.0

10.0

10.0

10.0

10.0

7.5

6.9

NA
NA
NA
NA

36.7
12.0
24.1
NA

34.8
12.0
24.1
NA

32.8
15.1
24.1
NA

32.8
15.1
24.1
NA

32.8
15.1
24.1
NA

32.8
15.1
24.1
NA

32.8
15.1
24.1
NA

21.6
13.9
20.6
NA

24.1
13.3
19.4
NA

2005
31.9
65.0
32.6
47.7

2006
31.9
65.0
32.6
47.7

2007
31.9
65.0
32.6
47.7

2008
24.0
81.3
10.0
20.1

2009
22.9
83.3
10.0
20.0