Misallocation and Productivity: The Case
of Vietnamese Manufacturing
∗
DOAN THI THANH HA, KOZO KIYOTA, AND KENTA YAMANOUCHI
This paper attempts to measure the effect of resource misallocation on
aggregate manufacturing total factor productivity, focusing on Vietnamese
manufacturing firms during the period 2000–2009. One of the major findings of
this paper is that there would have been substantial improvement in aggregate
total factor productivity in Viet Nam in the absence of distortions. El
results imply that potential productivity gains from removing distortions in
Vietnamese manufacturing are large. We also find that smaller firms tend to face
advantageous distortions, while larger firms tend to face disadvantageous ones.
Además, the efficient size distribution is more dispersed than the actual size
distribución. These results suggest that Viet Nam’s policies may constrain its
largest and most efficient producers, and coddle its smallest and least efficient
unos.
Palabras clave: misallocation, total factor productivity, Viet Nam
JEL codes: D22, F14, O47
I. Introducción
Differences in per capita income across economies result mainly from
differences in total factor productivity (TFP).1 Por lo tanto, clarifying the underlying
causes of low productivity in developing economies is one of the central concerns
in various fields of economics such as development economics, international
economics, and macroeconomics. Given the fact that production efficiency is
heterogeneous across firms, some recent studies on this issue argue that aggregate
∗Doan Thi Thanh Ha: Asian Development Bank Institute. Correo electrónico: hato.doan@gmail.com; Kozo Kiyota (correspondiente
author): Keio Economic Observatory, Keio University, Japón. Correo electrónico: kiyota@sanken.keio.ac.jp; Kenta Yamanouchi:
Graduate School of Economics, Keio University, Japón. Correo electrónico: ymdkntr40723331@yahoo.co.jp. The authors would
like to thank Iwan J. Azis, Flora Bellone, Minsoo Lee, Qing Liu, Atsushi Ohyama, Yoichi Sugita, Kenichi Ueda, el
participants at the Asian Development Outlook–Asian Development Review Conference held in Seoul in November
2015, the managing editor, and an anonymous referee for helpful comments. Kozo Kiyota gratefully acknowledges
financial support received from the Japan Society for the Promotion of Science Grant-in-Aid (26220503) y
the Ministry of Education, Cultura, Sports, Ciencia, and Technology-Supported Program for Strategic Research
Foundations at Private Universities. The usual disclaimer applies. ADB recognizes “China” as the People’s Republic
of China and “Vietnam” as Viet Nam.
1“Large differences in output per worker between rich and poor economies have been attributed, in no
small part, to differences in total factor productivity” (Hsieh and Klenow 2009, pag. 1403); “[C]ross-economy income
differences mostly result from differences in total factor productivity” (Waugh 2010, pag. 2095). McMillan and Rodrik
(2011) also argued for the importance of resource reallocation in enhancing productivity growth in developing
economías.
Asian Development Review, volumen. 33, No. 2, páginas. 94–118
C(cid:3) 2016 Asian Development Bank
and Asian Development Bank Institute
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MISALLOCATION AND PRODUCTIVITY IN VIETNAMESE MANUFACTURING 95
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 economies
can be attributed to the misallocation of resources across heterogeneous firms.
How do we measure the misallocation of resources? One way to answer this
question is to focus on distortions that reflect the difference between the actual
and efficient outcomes. Such distortions are called “wedges” in the literature. El
seminal work of Hsieh and Klenow (2009) estimates wedges from data on value
added and factor inputs for manufacturing establishments in the People’s Republic
of China (PRC), India, y los estados unidos (US). They found that the distortions
were much larger in the PRC and India than in the US. Hsieh and Klenow (2009)
also found that the removal of distortions has a significant effect on aggregate TFP
in the PRC 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.3
This paper extends the analysis of Hsieh and Klenow (2009) to Vietnamese
manufacturing between 2000 y 2009 and asks the following questions:
(i) To what extent are resources misallocated in Viet Nam?
(ii) How large would the productivity gains have been in the absence of
distortions?
(iii) Are the distortions related to firm size?
(iv) What would the distribution of firm size have been in the absence of
distortions?
Answering these questions has important implications for potential growth
because reallocation would lead to productivity gains that can accelerate potential
growth through improved interfirm resource allocation.
Our study is closely related to Bach (2014), who also examined resource
misallocation in Viet Nam using firm-level data. His study addressed the first two
questions above but did not compare resource misallocation in Viet Nam with
misallocation in other Asian economies. Nor did his study address the last two
preguntas. From a policy perspective, the last two questions are important because
many economies give preferential treatment to small and medium-sized enterprises
(SMEs). En efecto, size-dependent policies, which limit the size of firms, could be an
2See Restuccia and Rogerson (2013) and Hopenhayn (2014) for a survey.
3Ver, Por ejemplo, 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; de
Vries (2014) for Brazil; Dheera–Aumpon (2014) for Thailand; Bach (2014) for Viet Nam; and Calligaris (2015) para
Italia.
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96 ASIAN DEVELOPMENT REVIEW
important source of misallocation (Restuccia and Rogerson 2013). In answering the
four questions, this paper goes one step further by providing a deeper understanding
of the potential productivity gains from removing distortions in Viet Nam.4
The rest of this paper is organized as follows. In section II, we describe
the methodology of Hsieh and Klenow (2009). Section III describes the Vietnamese
firm-level data used in our study. Section IV presents the results. Concluding remarks
and policy implications are presented in section V.
II. Measurement of Misallocation
Hsieh and Klenow (2009) formulated an analytical framework to estimate
misallocation. Although some studies such as Bartelsman, Haltiwanger, y
Scarpetta (2013) developed an alternative framework, this paper employs Hsieh and
Klenow’s framework for the following reasons. Primero, their framework is tractable
in the sense that it is simple and its data requirements are minimal. This provides a
significant advantage in estimating misallocation in Viet Nam because of the limited
data availability, as we will discuss in the next section. Segundo, the framework allows
us to decompose the source of misallocation into distortions in output markets and
those in capital markets. Such decompositions are useful if the distortions come
from different sources. The Hsieh and Klenow (2009) methodology is summarized
abajo.
Assume that a representative firm produces a single final good, Y , en un
perfectly competitive final goods market. The firm produces Y , using the output
Ys of S manufacturing industries, with the following Cobb–Douglas production
tecnología:
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.
Y =
S(cid:2)
s=1
Y θs
s
, dónde
S(cid:3)
s=1
θs = 1
(1)
and θs is the output share of each industry s.
Each industry produces output, Ys, using Ms differentiated goods produced by
individual firm i with a constant elasticity of substitution technology (s = 1, . . . , S).
Output in industry s is then given by:5
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Ys =
(cid:4)
EM(cid:3)
yo=1
(cid:5) pag
σ −1
σ −1
pag
Y
si
σ > 1
(2)
4Another important difference between Bach (2014) and our study is that his study did not control for the
skill differences of workers across firms in measuring quantity-based TFP and revenue-based TFP.
5We suppress the time subscript to avoid heavy notation, although we utilize firm-level panel data in the
empirical analysis.
MISALLOCATION AND PRODUCTIVITY IN VIETNAMESE MANUFACTURING 97
where 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 the following Cobb–Douglas technology:
Ysi = Asi K
si L 1−αs
αs
si
(3)
where Asi , Ksi , and L si denote the productivity, capital, and labor of firm i in
industry s, respectivamente; and αs represents the capital share, which is different across
industries but the same across firms within an industry.
To assess the extent of misallocation, Hsieh and Klenow (2009) seguido
Foster, Haltiwanger, and Syverson (2008) in making a distinction between physical
productivity, denoted by TFPQ, and revenue productivity, denoted by TFPR:
TFPQsi
(cid:5)= Asi =
Ysi
si L 1−αs
αs
si
k
y
TFPRsi
(cid:5)= Psi Asi = Psi Ysi
si L 1−αs
αs
k
si
(4)
(5)
respectivamente, where Psi represents the firm-specific output price.
In addition to firm heterogeneity in terms of productivity (ver, Por ejemplo,
Melitz 2003), firms potentially face different output and capital distortions. Más
specifically, Hsieh and Klenow (2009) incorporated two types of firm-level wedges
into this framework. One raises the marginal 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 by τK si . These wedges are given from
the firm’s viewpoint and we do not make any assumptions about what generates
them.6
An example of such distortions is subsidized credit. If two firms have identical
technologies but one of the firms can borrow from the financial market at a lower
interest rate (and the other firm can borrow at a higher interest rate), the marginal
product of capital of the firm that can access the subsidized credit will be lower than
that of the other firm. This results in the misallocation of capital because one firm
enjoys a lower interest rate even though the two firms have the same technologies.
6Distortions can be generated by various factors such as trade policies and credit market imperfections. En nuestro
companion paper (Ha and Kiyota 2015), we examined the determinants of distortions in Vietnamese manufacturing.
Le´on–Ledesma and Christopoulos (2016) examined the effects of access to finance obstacles on misallocation. Usando
firm-level data covering 45 economías, they found that access to finance obstacles and private credit increase the
dispersion of distortions. Sin embargo, they also found that the financial variables explain a small part of the dispersion
of factor market and size distributions.
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98 ASIAN DEVELOPMENT REVIEW
En otras palabras, in the framework of Hsieh and Klenow (2009), the differences in
factor prices mean the existence of distortions.
With these wedges, the expected profits of the firm are written as follows:7
πsi = (1 − τY si ) Psi Ysi − wL si − (1 + τK si ) R Ksi
(6)
where w and R denote the common wages and rental costs facing all firms,
respectivamente. Firms maximize their profits under the following constraint:
(cid:7)pag
(cid:6)
Ps
Psi
Ysi = Ys
dónde
Ps ≡
(cid:5) 1
1−σ
(cid:4)
EM(cid:3)
yo=1
P 1−σ
si
(7)
(8)
In the presence of distortions, firms will produce a different quantity compared with
what they would produce without these wedges (the efficient case).
Solving the profit maximization problem under a monopolistic competition
framework and the equilibrium allocation of resources across industries, tenemos:
Psi =
1 − τY si =
1 + τK si =
pag
σ − 1
pag
σ − 1
αs
1 − αs
(cid:7)αs
(cid:6)
(cid:6)
R
αs
w
1 − αs
(cid:7)
1−αs
A−1
si
(1 + τK si )αs
1 − τY si
,
, y
wL si
(1 − αs) Psi Ysi
wL si
R Ksi
From equation (9), tenemos:
TFPRsi = ξs
(1 + τK si )αs
1 − τY si
dónde
ξs =
(cid:7)αs
(cid:6)
pag
σ − 1
(cid:6)
R
αs
w
1 − αs
(cid:7)
1−αs
(9)
(10)
(11)
(12)
(13)
7Distortions to output and to capital relative to labor are an observationally equivalent characterization of
distortions to the absolute levels of capital and labor. For more details, see Hsieh and Klenow (2009, Appendix III).
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MISALLOCATION AND PRODUCTIVITY IN VIETNAMESE MANUFACTURING 99
Noting that ξs is different across industries but constant within an industry, equation
(12) implies:
TFPRsi ∝
(1 + τK si )αs
1 − τY si
(14)
This equation means that the large deviation of firm TFPR from ξs is a sign that the
firm faces large distortions.
If we denote industry TFP as TFPs and define industry TFP as a weighted
geometric average of firm i’s TFPQsi , tenemos
⎡
EM(cid:3)
⎣
(cid:5)=
yo=1
TFPs
(cid:4)
TFPQsi
TFPRs
TFPRsi
(cid:5)σ −1
1
σ −1
⎤
⎦
(15)
where TFPRs is the geometric average of the average marginal revenue product of
labor and capital in industry s:
(cid:12)
TFPRs
(cid:5)=
pag
σ − 1
(cid:13)
EM
yo=1
αs
R
1−τY si
1+τK si
Psi Ysi
Ps Ys
(cid:14)αs
(cid:12)
(1 − αs)
(cid:13)
w
(cid:14)
1−αs
EM
yo=1 (1 − τY si ) Psi Ysi
Ps Ys
(16)
There are two points of clarification regarding equation (15). Primero, the higher the
dispersion in TFPR, the lower the industry TFP will be. Hsieh and Klenow (2013)
showed that when TFPQ and TFPR are jointly log-normally distributed and when
there is only variation in log (1 − τY si ), aggregate TFP can be expressed as follows:8
logTFPs = 1
σ − 1
(cid:15)
log Ms + log E
(cid:16)
TFPQσ −1
si
(cid:17)(cid:18)
−
pag
2
era (log TFPRsi )
(17)
This equation suggests that industry TFP will decline if the elasticity of substitution
σ and/or TFPR dispersion increase.
Segundo, TFPR will be equalized across firms within industry s if τK si and τY si
are equalized. Por ejemplo, from equation (12), TFPRsi = ξs∀i if τK si = τY si = 0.
This implies that TFPRsi = ξs = TFPRs∀i.9 Denoting industry TFP without any
8A similar property is obtained even when there is variation in log (1 + τK si ), although the equation becomes
more complicated. For more details, see Hsieh and Klenow (2013).
9Note that even when TFPR is equalized across firms, TFPQ can be different across firms because more
productive firms charge lower prices (see equation [9]); eso es, if Asi > As j and Psi < Ps j , Psi Asi could be equal to
Ps j As j for i (cid:7)= j.
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100 ASIAN DEVELOPMENT REVIEW
distortions as TFPQs from equation (15), we can obtain
TFPQs
(cid:5)= ¯As =
(cid:5) 1
σ −1
(cid:4)
Ms(cid:3)
i=1
Aσ −1
si
(18)
which is called “efficient” industry TFP.
In order to obtain “efficient” TFP, one needs information on firm-level TFPQ
(Asi ). One problem is the limited availability of firm-level price data, Psi , which are
not available for many economies, including Viet Nam.10 Hsieh and Klenow (2009)
rewrote equation (4) as
TFPQsi
= Asi = κs
σ
σ −1
(Psi Ysi )
si L 1−αs
αs
K
si
, where κs = w1−αs
σ −1
(PsYs)− 1
Ps
(19)
Noting that κs is a scaling constant by industry and does not affect the relative
differences between firms within industry s, it can be normalized to unity (κs =
1). This manipulation enables us to estimate TFPQ without firm-level price data.
> TFPRsi if κs = 1 and Psi Ysi ≥ 1.
Note that from equations (5) y (19), TFPQsi
Por lo tanto, in the Hsieh and Klenow (2009) framework the dispersion of TFPQ tends
to be larger than that of TFPR.
III. Datos
A.
Fuente
This paper utilizes firm-level data from the Annual Survey of Enterprises
collected by the General Statistics Office of Viet Nam.11 The survey was conducted
for the first time in 2000 and then annually thereafter to provide researchers and
policy makers with comprehensive information on Vietnamese firms. These data
cover registered firms operating in all sectors, including agriculture, industry and
construction, and services.
The survey covers all state-owned enterprises and foreign-owned firms
without any firm size threshold. Sin embargo, for domestic private firms, those with
fewer than 10 workers are chosen by random sampling.12 Household business
10There are some economies for which firm-level (or plant-level) price data are available. Por ejemplo, Eslava
et al. (2004) utilized plant-level price data for Colombia to estimate plant-level TFPQ.
11We use the same data as Ha and Kiyota (2014); this section is based on section III of their study. Note also
that the use of firm-level data is more consistent with the theory than the use of plant-level data. This is because, como
Nishimura, Nakajima, and Kiyota (2005) point out, resource allocation within a firm is determined by managerial
decisiones. Además, research and development and headquarters activities are typically classified as service activities,
which are not covered in the manufacturing survey.
12This threshold was used in surveys before 2010. De 2010, different regions set different firm size
umbrales.
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MISALLOCATION AND PRODUCTIVITY IN VIETNAMESE MANUFACTURING 101
activities are not covered in this survey.13 The survey information includes type of
ownership, assets and liabilities, number of employees, sales, capital stock, industria
that the firm belongs to, and obligations to the government (p.ej., taxes) from January
to December of that year.
The data have some disadvantages. Some of the input data, such as materials,
are not available for all years. Information on working hours and capital utilization
rates is also unavailable. Firms’ year of establishment and export status are not
available every year. This paper uses firms with information on inputs, outputs, y
cost shares. Reentry firms, which are those that disappeared from the data and then
reappeared later, are omitted from our analysis. Some firms changed industry and/or
ownership during the sample period.14 We drop firms with fewer than 10 employees,
regardless of their ownership, to avoid the effects of the random sampling.
B.
Variables and Parameters
The main variables that we use are the two-digit Viet Nam Standard Industry
Classification (VSIC) industry code, ownership type, value added, employment,
total labor costs, and capital stock. Following Hsieh and Klenow (2009), we use
wage bills instead of the number of workers to capture the potential differences in
employee quality.15 Capital stock is measured as total fixed assets recorded at the
end of each year. Both wage bills and capital stock are deflated by the manufacturing
gross domestic product (PIB) deflator.16
To compute dispersion, we follow other research in setting the key parameters
σ and R as follows. We assume that the elasticity of substitution σ equals 3 and R is
10%, comprising a 5% depreciation rate and a 5% interest rate. We also follow Hsieh
and Klenow (2009) to set αs equal to 1 minus the labor share in the corresponding
industry in the US. Under Hsieh and Klenow’s framework, the output elasticities of
capital and labor (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 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.17 Industry classifications
13The survey covered 62.2% of total employment in manufacturing in 2009. The data on total employment
in manufacturing were obtained from the General Statistics Office online database on population and employment.
14If a firm has switched industries, the industry to which the firm belonged for the majority of the surveyed
years is regarded as the firm’s industry. If a firm belonged to more than one industry for equal amounts of time, nosotros
assign the industry code of the industry that the firm belonged to most recently.
15The use of wage bills as a measure of labor input implies that w = 1 (Camacho and Conover 2010, pag. 10).
16As Aw, Chen, and Roberts (2001) pointed out, it is preferable to utilize the investment goods price deflator
rather than the manufacturing GDP deflator to obtain the real capital stock. Sin embargo, as Ha and Kiyota (2014)
discussed, the investment goods price deflator is not available for our data set.
17Data can be downloaded from the National Bureau of Economic Research’s website at http://www.nber.org
/nberces/
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102 ASIAN DEVELOPMENT REVIEW
are based on the North American Industry Classification System (NAICS) versión
1997. Based on the data, we first match the NAICS code with the four-digit VSIC
code using concordance tables between NAICS, International Standard Industry
Classification revision 3, and VSIC. We then aggregate total payroll and total value
added by two-digit VSIC sectors. To compute the labor 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. Por lo tanto, we follow Hsieh and Klenow (2009) to inflate the labor shares by
1.5 to obtain US labor elasticities.
As firms’ output prices are not available, we have obtained TFPQ by
raising nominal output to the power of σ/(σ − 1), assuming that normal demand
relationships hold. If a firm’s real output is high, 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 from its
industry mean: registro(TFPRsi /TFPRs) and log(TFPQsi M
/TFPQs), where TFPRs
and TFPQs are from equations (16) y (18), respectively.18 We trim 2% de
firm productivity and distortions by removing values below the first percentile
and above the 99th percentile from the distribution of log(TFPRsi /TFPRs) y
/TFPQs). Entonces, we recalculate TFPRs, TFPQs, and TFPs. Para
registro(TFPQsi M
robustness checks, section V examines whether the results are sensitive to the values
of σ , αs, and the threshold level of trimming.
1
σ −1
s
1
σ −1
s
IV. Resultados
A.
To what extent are resources misallocated in Viet Nam?
This section addresses the first question of the paper: To what extent are
resources misallocated in Viet Nam? To answer this question, we compare the
dispersions of TFP in Viet Nam with those in the PRC, India, Japón, Tailandia, y
the US. The dispersions of TFPR are reported in Table 1, while those of TFPQ are
reported in Table 2. Both tables present standard deviations, differences between the
90th and 10th percentiles, differences between the 75th and 25th percentiles, y
average per capita GDP during the sample period.19 Figures for the PRC, India, y
the US are from Hsieh and Klenow (2009); for Japan, from Hosono and Takizawa
(2013); and for Thailand, from Dheera–Aumpon (2014).
These tables indicate that the standard deviation of TFPR for Viet Nam is
0.79, which is comparable to the standard deviations for the PRC (0.68), India (0.68),
18Some of the effects of the changes in prices are controlled for by taking the ratio.
19Noting that both TFPR and TFPQ are divided by their industry means, these statistics can be interpreted as
the coefficients of variation.
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MISALLOCATION AND PRODUCTIVITY IN VIETNAMESE MANUFACTURING 103
Mesa 1. Dispersion of Revenue-Based Total Factor Productivity
Viet Nam Thailand
2000–2009
2006
People’s
Republic of
Porcelana
1998–2005
India
1987–1994
Japón
1981–2008
United
Estados
1977–1997
0.68
0.80
1.66
400
0.79
0.97
2.00
685
0.68
0.89
1.72
1,304
0.85
1.04
2.09
2,813
0.55
0.70
1.40
31,101
0.45
0.47
1.08
30,533
Dakota del Sur
75–25
90–10
GDP per capita
GDP = gross domestic product, SD = standard deviation, TFPR = revenue-based total factor productivity.
Notas: Data for Thailand are from Dheera–Aumpon (2014, Mesa 3). Data for the People’s Republic of China are
arithmetic averages from Hsieh and Klenow (2009, Mesa 2). Data for Japan are from Hosono and Takizawa (2013).
TFPR is calculated from equation (5) and then scaled by the geometric mean of TFPR across all firms in an industry
s. Industries are weighted by value-added shares. GDP per capita is the annual average over each sample period in
constant 2005 US dollars.
Fuentes: Hsieh, C.-T., y P. j. Klenow. 2009. Misallocation and Manufacturing TFP in China and India. Quarterly
Journal of Economics 124 (4): 1403–48; Hosono, K., y M. Takizawa. 2013. Misallocation and the Dynamics
of Establishment. Financial Review 112 (1): 180–209 (in Japanese); Dheera–Aumpon, S. 2014. Misallocation
and Manufacturing TFP in Thailand. Asia-Pacific Economic Literature 28 (2): 63–76; and authors’ calculations
based on Government of Viet Nam, General Statistics Office. Annual Survey of Enterprises. https://www.gso.gov.vn
/default_en.aspx?tabid=479&idmid=5; per capita GDP data obtained from World Bank. 2014. World Development
Indicators. Washington, corriente continua.
Mesa 2. Dispersion of Quantity-Based Total Factor Productivity
People’s
Viet Nam Thailand Republic of China
2000–2009
1998–2005
2006
India
1987–1994
Japón
1981–2008
United States
1977–1997
1.59
2.18
4.12
1.42
2.01
3.70
0.98
1.27
2.48
1.00
1.34
2.57
Dakota del Sur
1.19
75–25
1.56
90–10
3.03
SD = standard deviation, TFPQ = quantity-based total factor productivity.
Notas: Data for Thailand are from Dheera–Aumpon (2014, Mesa 2). Data for the People’s Republic of China, India,
and the United States are arithmetic averages from Hsieh and Klenow (2009, Mesa 1). Data for Japan are from
Hosono and Takizawa (2013, Mesa 1). TFPQ is calculated from equation (19) and then scaled by the geometric mean
of TFPQ across all firms in an industry s. Industries are weighted by value-added shares.
Fuentes: Hsieh, C.-T., y P. j. Klenow. 2009. Misallocation and Manufacturing TFP in China and India. Quarterly
Journal of Economics 124 (4): 1403–48; Hosono, K., y M. Takizawa. 2013. Misallocation and the Dynamics
of Establishment. Financial Review 112 (1): 180–209 (in Japanese); Dheera–Aumpon, S. 2014. Misallocation
and Manufacturing TFP in Thailand. Asia-Pacific Economic Literature 28 (2): 63–76; and authors’ calculations
based on Government of Viet Nam, General Statistics Office. Annual Survey of Enterprises. https://www.gso.gov.vn
/default_en.aspx?tabid=479&idmid=5
0.83
1.16
2.15
y Tailandia (0.85), and is larger than the standard deviations for Japan (0.55) y
the US (0.45). Similar patterns were also confirmed for the differences between the
75th and 25th percentiles, and between the 90th and 10th percentiles.20 Although
more careful examination is needed in the form of a direct comparison, the results
20The difference between the 75th and 25th percentile firms is 0.97, which corresponds to a TFP ratio of
e0.97 = 2.63. Similarmente, the difference between the 90th and 10th percentile firms is 2, which corresponds to a TFP
ratio of e2.00 = 7.39. These figures are much larger than those for the US. For more details, see Syverson (2011).
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104 ASIAN DEVELOPMENT REVIEW
suggest that distortions in developing economies, including Viet Nam, tend to be
large relative to those in developed economies.
B.
How large would the productivity gains be without distortions?
This section addresses the second question of this paper: How large would the
productivity gains have been in the absence of distortions? To answer this question,
we estimate TFP gains when the marginal products of labor and capital are equalized
across firms within each industry. For each industry, the gains are computed as the
ratio of actual TFP obtained from equation (15) to the “efficient” TFP obtained
from equation (18). We then aggregate the gains across industries using industry
value-added shares as the weights. En particular, we compute
(cid:5)=
Y
Y ∗
=
(cid:6)
S(cid:2)
s=1
S(cid:2)
s=1
⎧
⎪⎨
⎪⎩
⎡
(cid:6)
S(cid:2)
(cid:7)θs
=
s=1
TFPs
TFPQs
(cid:7)θs
Ys
Y ∗
s
⎡
⎣
(cid:4)
EM(cid:3)
TFPQsi
yo=1
1
TFPQs
TFPRs
TFPRsi
(cid:5)σ −1
1
σ −1
⎤
⎦
θs
⎫
⎪⎬
⎪⎭
S(cid:2)
EM(cid:3)
⎣
=
s=1
yo=1
(cid:4)
Asi
¯As
TFPRs
TFPRsi
(cid:5)σ −1
θs
σ −1
⎤
⎦
(20)
where Y ∗ is the “efficient” output that corresponds to the “efficient” TFP and θs is
= TFPs/
the value-added share of industry s (
TFPQs) is obtained when Ks and L s are given. As the total amount of inputs is fixed,
the output gains come solely from the reallocation of resources in the absence of
distortions.
θs = 1). The first equality (Ys/Y ∗
s
(cid:13)
s
Mesa 3 presents the TFP gains from equalizing TFPR across firms within
each industry. The gains are measured relative to the TFP gains in the US in 1997.21
To report TFP percentage gains in Viet Nam 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 Viet Nam hypothetically moves to “US efficiency,” substantial gains (30.7%) son
esperado. The gains are smaller than those for the PRC (39.2%), India (46.9%), y
Tailandia (73.4%), but larger than those for Japan (3%).
One may be concerned that the dispersion of TFPR is larger (Mesa 1), mientras
the gains are smaller in Viet Nam than in either the PRC or India (Mesa 3). Noting
that the gains are computed from the inverse of equation (20), (Y ∗/Y − 1) × 100),
21Hsieh and Klenow (2009) called this comparison a conservative analysis because the US’ gains are largest
en 1997.
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MISALLOCATION AND PRODUCTIVITY IN VIETNAMESE MANUFACTURING 105
Mesa 3. Total Factor Productivity Gains from Equalizing Revenue-Based Total
Factor Productivity Relative to 1997 Gains in the United States
Viet Nam
2000–2009
Tailandia
2006
People’s
República de China
1998–2005
India
1987–1994
Japón
1981–2008
%
30.7
73.4
39.3
46.9
3.0
Notas: The data for Thailand are calculated from Dheera–Aumpon (2014, Mesa 4). The data for the
People’s Republic of China, India, and the United States are arithmetic averages from Hsieh and Klenow
(2009, Mesa 6). The data for Japan are calculated from Hosono and Takizawa (2013, Mesa 2).
Fuentes: Hsieh, C.-T., y P. j. Klenow. 2009. Misallocation and Manufacturing TFP in China and
India. Revista trimestral de economía 124 (4): 1403–48; Hosono, K., y M. Takizawa. 2013.
Misallocation and the Dynamics of Establishment. Financial Review 112 (1): 180–209 (in Japanese);
Dheera–Aumpon, S. 2014. Misallocation and Manufacturing TFP in Thailand. Asia-Pacific Economic
Literature 28 (2): 63–76; and authors’ calculations based on Government of Viet Nam, General Statistics
Office. Annual Survey of Enterprises. https://www.gso.gov.vn/default_en.aspx?tabid=479&idmid=5
Y ∗/Y will be small if Asi / ¯As and/or TFPRs/TFPRsi become large. The results
suggest that, on average, Asi / ¯As is larger in Viet Nam than in either the PRC or
India. Similarmente, we find large TFP gains for Thailand, which is possibly attributed
to a small Asi / ¯As for Thailand.22 Although these are hypothetical exercises and thus
should not be taken literally, the results suggest that substantial productivity gains
are expected in Viet Nam by the kind of reallocation considered here.
C.
Are the distortions related to firm size?
This section examines whether the distortions are related to firm size. Este
question has important policy implications because, Por ejemplo, many economies
give preferential treatment to SMEs. If SMEs tend to face larger disadvantageous
distortions, preferential treatment to SMEs can be justified. Following Hsieh and
Klenow (2009) and Ziebarth (2013), we examine the relationship between firm size
and TFPR.
Cifra 1 presents the relationship between firm size percentile as measured
by value added and scaled TFPR relative to a given industry. Cifra 1 indicates
that TFPR is increasing as firm size increases. Noting that TFPR is proportional
to the distortions (equation 14), this result implies that smaller firms tend to face
advantageous distortions, while larger firms tend to face disadvantageous ones. Este
result is similar to that found for India (Hsieh and Klenow 2009, Cifra 6) and for
the US in the 19th century (Ziebarth 2013, Cifra 3).
Curiosamente, this correlation with firm size is different for the distortions
in output and the distortions in capital markets. Cifra 2 presents the relationship
between the distortions in output markets and firm size (in terms of value added).
22Cifra 1 in Dheera–Aumpon (2014) suggests that the distribution of TFPQ in Thailand moves to the left
and its mean takes a negative value. Although it is not clear why the distribution moves to the left, this may be a
reason why large TFP gains are expected in Thailand.
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106 ASIAN DEVELOPMENT REVIEW
Cifra 1. Revenue-Based Total Factor Productivity and Size
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TFPR = revenue-based total factor productivity.
Nota: This figure presents the relationship between scaled TFPR relative to a given industry and size percentile as
measured by value added.
Fuente: Authors’ calculations based on Government of Viet Nam, General Statistics Office. Annual Survey of
Enterprises. https://www.gso.gov.vn/default_en.aspx?tabid=479&idmid=5
Cifra 2 indicates that the distortions in output markets decrease as firm size
aumenta. Noting that the distortions in output markets are measured by 1 − τY ,
this result is similar to that in TFPR: smaller firms tend to face advantageous
distortions, while larger firms tend to face disadvantageous ones.
Cifra 3 presents the relationship between the distortions in capital markets
and firm size. In contrast to the distortions in output markets, Cifra 3 muestra
an inverse U-shaped relationship. Noting that the distortions in capital markets
are measured by 1 + τK , this result suggests that both small and large firms
tend to face advantageous distortions. A diferencia de, middle-sized firms tend to
face disadvantageous distortions. This pattern is different from those of TFPR
and distortions in output markets. This may be because small firms are treated
preferentially, while large firms can diversify their capital procurement.
It is also interesting to note that the result for TFPR mainly reflects that
of distortions in output markets. This result implies that the distortions in output
markets have stronger effects on TFPR than those in capital markets. This result
is consistent with the findings of Midrigan and Xu (2014), who showed that
financial frictions, measured by borrowing constraints, had relatively small impacts
on productivity.
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MISALLOCATION AND PRODUCTIVITY IN VIETNAMESE MANUFACTURING 107
Cifra 2. Distortions in Output Markets and Size
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Nota: This figure presents the relationship between scaled 1 − τY relative to a given industry and size percentile as
measured by value added.
Fuente: Authors’ calculations based on Government of Viet Nam, General Statistics Office. Annual Survey of
Enterprises. https://www.gso.gov.vn/default_en.aspx?tabid=479&idmid=5
One may be concerned that our measurement of firm size, following Hsieh and
Klenow (2009), is based on value added rather than employment. In many economies,
SMEs are defined by the number of employees rather than by the size of their value
added. To address this concern, we examine the relationship between distortions and
firm size as measured by employment. The results are presented in Figures 4, 5, y
6. The results are different from—but qualitatively similar to—those when firm size
is measured by value added: as firm size (in terms of employment) aumenta, TFPR
is increasing, the distortions in output markets are decreasing, and the distortions
in capital markets show an inverse U-shaped relationship except for the top quintile
of firms. Noting that the results for TFPR mainly reflect the distortions in output
markets, we can conclude that our main messages remain unchanged even when
firm size is measured by employment.
D. What would the distribution of firm size have been in the absence
of distortions?
The model also has an implication for the distribution of firm size. Ecuación
(7) is rewritten as
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108 ASIAN DEVELOPMENT REVIEW
Cifra 3. Distortions in Capital Markets and Size
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Nota: This figure presents the relationship between scaled 1 + τK relative to a given industry and size percentile as
measured by value added.
Fuente: Authors’ calculations based on Government of Viet Nam, General Statistics Office. Annual Survey of
Enterprises. https://www.gso.gov.vn/default_en.aspx?tabid=479&idmid=5
Psi Ysi = Y
σ −1
pag
1
pag
si PsY
s
From equations (7) y (9), tenemos
(cid:12)
σ − 1
pag
(cid:26) αs
R
(cid:6)
(cid:27)αs
1 − αs
w
Ysi =
(cid:14)pag
(cid:7)
1−αs
(cid:28)
P σ
s Ys
(cid:29)pag
Asi (1 − τY si )
(1 + τK si )αs
Similar to equation (14), from equations (21) y (22), tenemos
Psi Ysi ∝
(cid:28)
Asi (1 − τY si )
(1 + τK si )αs
(cid:29)σ −1
(21)
(22)
(23)
Ecuación (23) suggests that without distortions, más (menos) productive firms tend
to be larger (smaller). When Asi and 1 − τY si are correlated negatively, más
productive firms tend to be smaller than the efficient size. Similarmente, if Asi and
1 + τK si are correlated positively, less productive firms tend to be larger than the
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MISALLOCATION AND PRODUCTIVITY IN VIETNAMESE MANUFACTURING 109
Cifra 4. Revenue-Based Total Factor Productivity and Employment Size
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TFPR = revenue-based total factor productivity.
Nota: This figure presents the relationship between scaled TFPR relative to a given industry and size percentile as
measured by employment.
Fuente: Authors’ calculations based on Government of Viet Nam, General Statistics Office. Annual Survey of
Enterprises. https://www.gso.gov.vn/default_en.aspx?tabid=479&idmid=5
efficient size. Both cases result in smaller size dispersion. This implies that when
distortions are large, the efficient size distribution is more dispersed than the actual
size distribution.
To examine this implication, we compare the actual firm size distribution
with the efficient firm size distribution. The size is measured as the value added of
the firms, following Hsieh and Klenow (2009). Let P ∗
si be the efficient firm size.
The efficient sizes relative to actual sizes are
(cid:29)σ −1
(cid:7)σ −1
si Y ∗
(cid:6)
(cid:28)
si Y ∗
P ∗
si
Psi Ysi
= Y ∗
Y
Ys
Y ∗
s
(1 + τK si )αs
1 − τY si
(24)
where the efficient firm size is obtained when τK si and τY si are equalized within
industry s. Both Y ∗/Y and Ys/Y ∗
s are obtained from equation (20).23 We compute
the actual and efficient sizes from this equation by year, and then take averages over
the period.
23For the derivation of equation (24), see the Appendix.
110 ASIAN DEVELOPMENT REVIEW
Cifra 5. Distortions in Output Markets and Employment Size
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Nota: This figure presents the relationship between scaled 1 + τY relative to a given industry and size percentile as
measured by employment.
Fuente: Authors’ calculations based on Government of Viet Nam, General Statistics Office. Annual Survey of
Enterprises. https://www.gso.gov.vn/default_en.aspx?tabid=479&idmid=5
Mesa 4 y figura 7 present the results. En mesa 4, the rows are the actual
firm size quartiles with equal numbers of firms. The columns are the bins of efficient
firm size relative to actual firm size. We classify firms into four bins. Por ejemplo,
0%–50% means that the firm size would be less than half of the actual firm size if all
distortions are removed. Similarmente, 200+% means that the firm size would be more
than double without distortions. The entries are the shares of firms (averaged over
the period). The rows sum to 25%; the rows and columns together sum to 100%.
Examining Table 4, we highlight two results. Primero, although average output
rises substantially (as we confirmed in section IV), many firms of all sizes would
shrink without distortions. Segundo, the largest quartile indicates the largest expansion
among all firm sizes (8.7%). This result means that large firms are less likely to shrink
and more likely to expand. This finding is also confirmed by Figure 7.
As the model suggests, the efficient size distribution is more dispersed than
the actual size distribution. This result is consistent with the finding of the previous
sección. Like the case of India (Banerjee and Duflo 2005, pag. 507), Viet Nam’s policies
may constrain its largest and most efficient producers and coddle its smallest and
least efficient ones. En efecto, Vietnamese SMEs are supported by various policies
such as government-supported financing (Tran, Le, and Nguyen 2008, páginas. 347–59).
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MISALLOCATION AND PRODUCTIVITY IN VIETNAMESE MANUFACTURING 111
Cifra 6. Distortions in Capital Markets and Employment Size
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Nota: This figure presents the relationship between scaled 1 + τK relative to a given industry and size percentile as
measured by employment.
Fuente: Authors’ calculations based on Government of Viet Nam, General Statistics Office. Annual Survey of
Enterprises. https://www.gso.gov.vn/default_en.aspx?tabid=479&idmid=5
Mesa 4. Actual Size versus Efficient Size
Efficient firm size relative to actual firm size
2000–2009 (promedio)
0%–50% 50%–100% 100%–200% 200%+ Total
Actual firm size
Top quartile
Second quartile
Third quartile
Bottom quartile
Total
5.1
8.0
9.1
13.7
36.0
5.5
5.6
6.3
5.1
22.4
5.7
4.6
4.4
3.0
17.6
8.7
6.8
5.2
3.1
25.0
25.0
25.0
25.0
23.9
100.0
Notas: The rows are the actual firm size quartiles with equal numbers of firms. The columns are the
bins of efficient firm size relative to actual firm size. We classify firms into four bins by the value
added of firms. Por ejemplo, 0%–50% means that the firm size would be less than half of the actual
firm size if all distortions were removed. Similarmente, 200%+ means that the firm size would be more
than double without distortions. The entries are the shares of firms (averaged over the period).
Fuente: Authors’ calculations based on Government of Viet Nam, General Statistics Office. Annual
Survey of Enterprises. https://www.gso.gov.vn/default_en.aspx?tabid=479&idmid=5
These results for Viet Nam are similar to those of the PRC, India, and the US in
Hsieh and Klenow (2009).24
24The Government of Viet Nam has launched various schemes to improve the performance of SMEs,
including credit funds and worker trainings (Tran, Le, and Nguyen 2008, páginas. 347–59). Sin embargo, unlike India, dónde
size-related policies are explicitly imposed by law, such policies in Viet Nam are only guidelines. We cannot identify
from the data which individual firms are eligible for support or have received any form of support. It is thus difficult
for us to conduct an analysis similar to that of Hsieh and Klenow (2009).
112 ASIAN DEVELOPMENT REVIEW
Cifra 7. Distribution of Firm Size
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Nota: The solid line indicates the actual size distribution, while the dashed line indicates the efficient size distribution.
Fuente: Authors’ calculations based on Government of Viet Nam, General Statistics Office. Annual Survey of
Enterprises. https://www.gso.gov.vn/default_en.aspx?tabid=479&idmid=5
mi.
Robustness check: different parameter values
One may be concerned that our analysis is sensitive to the choice of parameter
values and sample selection because our results are based on specific parameter
values such as σ = 3. To address this concern, we reconduct all the analyses using
different parameter values. Because it is tedious to examine all the results, este
section examines (i) how sensitive the estimated TFPR and TFP gains (reported
in section IV and in Table 3) are to the choice of parameter values and sample
selección, y (ii) the correlation between alternative and baseline TFPR. En esto
robustness check, we report absolute TFP gains rather than relative TFP gains (a
the US) because we only change the parameter values in Viet Nam (not in the US).
We first examine whether the results are sensitive to the value of the elasticity
of substitution, pag . In the baseline analysis, following Hsieh and Klenow (2009), nosotros
set σ = 3. This implies 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)) y
1.2 (= 6/(6 − 1)), respectivamente. The second and third columns in Table 5 present
the results. The TFP gains are somewhat sensitive to the value of the elasticity of
substitution. The TFP gains are 65.3% when σ = 2 y 161.9% when σ = 6, mientras
the baseline TFP gains are 86.8%.25
25This result is consistent with equation (17), which implies that the TFP gains will be large if the elasticity
of substitution is large.
MISALLOCATION AND PRODUCTIVITY IN VIETNAMESE MANUFACTURING 113
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114 ASIAN DEVELOPMENT REVIEW
Sin embargo, the estimated TFPR is qualitatively similar to the baseline
resultados. Mesa 5 also reports the correlation with baseline TFPR, cual es 0.997
when σ = 2 y 0.994 when σ = 6. These high correlations suggest that the results
are quantitatively different from—but qualitatively similar to—the baseline results.26
The standard deviation of lnTFPR is 0.78 when σ = 2 y 0.79 when σ = 6, ambos
of which are similar to that of the baseline model (0.79).
We also examine the sensitivity of the results to the value of the technology
parameter (capital share αs). We examine two different technologies. One is αs =
1/3, as in Ziebarth (2013), and the other is the Vietnamese cost share, cual es
defined as the industry-year average capital share of the sample firms. The results are
presented in the fourth and fifth columns in Table 5. The TFP gains are 70.1% cuando
αs = 1/3 y 68% when we assume Vietnamese technology. The correlation with
the baseline TFPR is 0.927 when αs = 1/3 y 0.889 when we assume Vietnamese
tecnología. The standard deviation of lnTFPR is 0.64 for both cases. Similar to
the value of the 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
across firms even within industries. To address this concern, we use the firm-level
capital share so that the capital share can vary across firms.27 The results are
presented in the sixth column in Table 5 and are similar to the baseline results,
although the TFP gains are somewhat sensitive to the technology parameters. El
TFP gains are 40%. The correlation with the baseline TFPR is 0.794. El estandar
deviation of lnTFPR is 0.61. These results together suggest that our main messages
remain unchanged even when we use different values for the technology parameter.
Another concern may be that the data are not precise, and thus Vietnamese
firm-level data are subject 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. Primero, serious measurement error, possibly because of reporting
error, tends to appear as outliers. We trimmed 2% from the tails (below the second
percentile and above the 98th percentile), instead of 1% as in the baseline analysis,
and examined how sensitive the results are to the trim values. The seventh column
informa los resultados. The TFP gains are 75.7%. The correlation with the baseline
TFPR remains high at 0.995. The standard deviation of lnTFPR (0.71) is slightly
lower than that of the baseline model (0.79).
26It 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 economies, Viet Nam is not covered in their
análisis. We leave this exercise for future research.
27Note that ξs can vary across firms if the capital share is different across firms (see equation [12]); eso es,
TFPR will not necessarily be proportional to the capital and output wedges. We thus present the results for reference
solo. From equation (11), if the technology parameter is heterogeneous across firms (αs (= R Ksi /Psi Ysi ), distortions
appear only in τY si because τK si will be zero.
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MISALLOCATION AND PRODUCTIVITY IN VIETNAMESE MANUFACTURING 115
We also estimate the TFP gains for firms that survived throughout the sample
período (balanced panel). This exercise enables us to control for the effects of firm
entry and exit. The eighth column presents the results. This exercise reduces the
sample size substantially (norte = 10,186). Sin embargo, the estimated TFP gains are
large and the correlation with baseline TFP is high: 64.5% y 0.948, respectivamente.
The standard deviation of lnTFPR is 0.68, which is comparable to that of the baseline
modelo. The results suggest that about three-quarters of TFP gains come from the
incumbent firms, while the rest of the gains come from firms entering and exiting
the market. We can thus conclude that the results from the balanced panel are
qualitatively similar to the baseline results.
En suma, the magnitude of the TFP gains are somewhat sensitive to the choice
of the values of parameters σ and α. Sin embargo, our main messages remain
unchanged even if we use different parameter values or employ different sample
selection criteria: the potential TFP gains from removing distortions in Vietnamese
manufacturing are large.
V. Concluding Remarks
This paper employed the Hsieh and Klenow (2009) framework to investigate
misallocation and productivity linkages in Vietnamese manufacturing during the
period 2000–2009 using firm-level data. Our study has four major findings. Primero,
misallocation in Viet Nam is comparable to that in the PRC, India, y Tailandia.
This result is consistent with the common knowledge that resources in developing
economies are not efficiently allocated.
Segundo, there would be substantial improvement in TFP if no distortions
existió. If Viet Nam hypothetically moved to “US efficiency,” its TFP would be
boosted by 30.7%. Tercero, smaller firms tend to face advantageous distortions, mientras
larger firms tend to face disadvantageous ones. Finalmente, the efficient distribution of
firm size is more dispersed than the actual size distribution. This result implies that
Viet Nam’s policies may constrain its large and most efficient producers and coddle
its smallest and least efficient ones.
These findings have policy implications. The first finding suggests that,
similar to other developing economies, resource misallocation caused by the
distortions seems to be an important issue in Viet Nam. The second finding states
that the potential productivity gains from removing distortions in Vietnamese
manufacturing are large. El resultado
implies that reallocation would lead to
productivity gains that can accelerate potential growth through improved interfirm
resource allocation. The last two findings together imply that Viet Nam’s policies,
as stated earlier, may constrain its largest and most efficient producers and coddle
its smallest and least efficient ones. This suggests that policy makers need to focus
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116 ASIAN DEVELOPMENT REVIEW
more attention on the allocation of resources. An important question, por lo tanto, es
whether or not resources are being allocated to productive firms.
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Apéndice. Derivation of Equation (24)
From equations (7), (8), y (9), actual firm size is written as
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Psi Ysi = P σ
si
s Ys P 1−σ
(cid:7)
(cid:6)
Psi
Ps
= PsYs
1−σ
= θsY
(cid:28)
(1 + τK si )αs
Asi (1 − τY si )
(cid:30)
(cid:29)
1−σ
(cid:3)
(cid:12) (cid:16)
(cid:17)αs
(cid:14)
1−σ
1 + τK s j
(cid:16)
1 − τY s j
As j
j
(cid:17)
(A-1)
118 ASIAN DEVELOPMENT REVIEW
Efficient firm size is obtained when τK si and τY si are equalized within industry s
(p.ej., τK si = τK s and τY si = τY s). From equation (A-1), the efficient firm size is
written as
si Y ∗
P ∗
si
= θsY ∗ Aσ −1
j Aσ −1
si(cid:13)
s j
From equations (A-1) y (A-2), tenemos
(cid:29)σ −1
(cid:7)σ −1
(cid:6)
(cid:28)
P ∗
si Y ∗
si
Psi Ysi
= Y ∗
Y
Ys
Y ∗
s
(1 + τK si )αs
1 − τY si
(A-2)
(A-3)
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