Productivity Analysis of Manufacturing Industry in Laos andLessons Learnt from Japan
Vanxay Sayavong
Visiting Research Fellow, Japan Center for Economic Research (JCER), Tokyo, Japan
Researcher, National Institute for Economic Research (NIER), Vientiane, Laos
Email: sai_vanxay48@yahoo.com
September 2017
Abstract
This study aims to assess the productivity of Lao economy and manufacturing industry
based on qualitative and quantitative analysis. Firms in enterprise survey and economic
census survey are used for quantitative analysis facilitated by Data Envelopment
Analysis (DEA) and Tobit estimate. The study argues that Laos could develop the
manufacturing industry further in the future since its share in GDP is small. Improving
the productivity is crucial to develop manufacturing industry in the future. There is aroom to improve the productivity via efficiency component since many existing firms
achieved only 20－40 percent of their full efficiency. To end this, this study proposesthat the policy interventions for short term should be prioritized in the areas of
introduction of accounting system, credit access and international quality certification to
manufacturing firms. Whereas, the infrastructure development and increasing market
access are considered as the medium and long-term policy recommendation. The
experience of Japan shows that economic growth was driven by manufacturing sector.
With panel data, the sources of productivity growth for Japanese firms can be
decomposed and analyzed. It reveals that although firms made technological progress
boosting productivity but they were less efficient over time during the study periods,
which implies that such technologies were underutilized. The case study of Japan
indicates that the availability of statistics is essential to improve research on
productivity analysis of Lao economy and industries in the future. Therefore, thestatistics on Lao industries needs to be urgently improved especially Economic Census
Survey.
Key words: Lao economy, Japanese economy, productivity, efficiency and scaleefficiency
Acknowledgement: This work is carried out under the Nikkei Asia Scholarship aided
by Nikkei Inc. and Japan Center for Economic Research (JCER). The author would liketo thank all participants at the consultation workshop at JCER held on 12th September
2017 for their feedback and comments. Any views expressed in this paper are those of
the author only.

11. Introduction
Over last two decades, the economies of low-income countries in Asia had grown going
rapidly and did so their income per capita allowing them to lift up their positions fromthe list of low-income countries to higher income countries. As highlighted by the Asia
Development Bank Outlook 2017, many developing economies in Asia such as China,
India, Thailand and Indonesia have moved to higher income level from low to middle
thanked to high economic growth. In terms of population, more than 90 percent ofpeople in such developing economies, who used to be low-income group in 1991, now
become middle-income. The success of many developing countries in Asia is the main
contributor to the global success since they take up 80 percent of all middle-income
economies. The high record of economic growth during the last two decades was
predominantly attributed by capital investment and productivity growth (APO, p.77,
2016). Nevertheless, the economic development is without challenges in years ahead.
Firstly, many developing countries in Asia may not be able to use the same growthmodel that heavily depends on capital investment for their transition to higher income
level (ADB, 2017). Secondly, the sustainable economic growth has been threaten by theslowdown of productivity growth recently not only in advanced economies but also in
emerging and low-income economies due to the recent global financial crisis (IMF,2017 and OECD, 2016). Therefore, recovering and boosting the productivity growth are
critical to sustain economic growth for developing countries.
Laos is with no exemption. Even though Laos has enjoyed high economic growth in thelast two decades, which allows the income per capita reaching 1,730 US dollars in 2015
up from 210 US dollars in 1991; the growth has been slowdown in recent years.
However, the good news is that Laos has already transited from low-income to lowmiddle country in 2015, along with other new members of ASEAN such as Cambodia,
Myanmar and Vietnam, according to the World Bank. However, to maintain theposition and transit to higher position are the challenges for Laos in the future. During
the last two decades, high record of Lao economic growth was strongly influenced by alarge inflow of foreign direct investment (FDI) especially in resource sector (mining andhydropower) since early 2000s. Since a large capital investment had increased throughthe channel of FDI, therefore; the growth of Laos was not doubtfully driven by capital
investment mainly from resource sector. As a result, resource sector has a largest shareof total export and highest rate in labor productivity. Its labor productivity is as high as
for 12 times of non-resource sector. However, the employment in resource sector is lessthan 1 percent of total employment. This clearly shows the unsustainable income
distribution among resource and non-resource sectors. At the same time, resource sector
has a limited stock and is vulnerable to the commodity prices. In addition, the growth ofresource sector has a drawback on non-resource sector through the channel of exchange
rate called Dutch disease. With these reasons, heavily depending on resource industries
leads to the unsustainability of Lao economy for long term. Hence, developing the nonresource sector especially manufacturing sector should be the alternative. In parallel,
Lao government has an ambitious vision to upgrade the status of Laos to higher income
level from low middle to high middle-income by 2030. To end this, the income per2capita must be raised up by 4 times or from 1,730 US dollars in 2015 up to 6,920 US
dollars in 20301.
Taking together, Laos needs higher quality/sustainable economic growth to accomplish
the government’s target. Therefore, it will be more sustainable if Lao economic growthand income per capital are driven by manufacturing sector rather than resource sector.
This leads to the motivation of this study that aims to find ways to improve the
manufacturing sector in Laos through productivity channel. To end this, three main
objectives are set. The first objective is to understand the productivity of Lao economy
and industries based on macro data. The second one is to find ways to improve theproductivity of manufacturing sector in Laos based on the data from enterprise surveys
and economic census survey. The application of Data Envelopment Analysis (DEA) as
the quantitative analysis is used to assess the productivity growth, efficiency, scaleefficiency and factors behind the efficiency. The final objective is to draw the lessons
from the success of Japan in developing her manufacturing sector. Since Japan has been
through several stage of development, lessons learnt from Japan is equally matter forthis research.
This paper proceeds as follows. The background and identified problems are presented
in the first section. Overviews of Lao and Japanese economies are summarized in thesection 2 and 3 to capture the broad picture of their economic growth and productivity
during the last two-three decades at the macro level. Visiting previous literature andtheir limitations are highlighted in section 4. Section 5 describes theoretical concept andmethodology including data source for quantitative analysis. Econometric results for thecase of Laos and Japan are reported and discussed in section 6. The last section is theconclusion.
2. Lao Economy
2.1. Economic Reform and Source of Growth
The Lao PDR is a land-locked and one of the least developed economies (LDCs)
located in the Southeast Asian region. It population is around 6.5 million people withthe growth rate of 1.45 annually over the last 10 years, according to the latest population
census 2015. Although Laos has enjoyed high economic growth over the last two
decades around 7 percent (Figure 1), but income per capita is among the lowest in theregion. In 2015, income per capita of Laos is 1,704 US dollars lower than Vietnam andThailand (1,990 and 5,720 US dollars respectively). The poverty rate as well as theinequality rate of income distribution is high among countries in the region as well.
According to ADB basic statistics 2017, the poverty rate of Laos is 23.2 percent and the
GINI coefficient indicator is 37.9 while the poverty and GINI in Cambodia, Thailand
and Vietnam are 14 percent, 7 percent, 10.5 percent, 30.8, 37.6 and 37.9 respectively.

1This requires the real GDP growth at least 7.5 percent annually under the assumption of fixed exchange
rate and GDP deflator growth rate at 8,300 Kip per US dollar and 5 percent per year. The population
growth rate is also assumed to be less than 2 percent or between 1.2-1.8 percent.

3

FIGURE 1
Real GDP Growth of Laos

Source: Lao Bureau Statistics (LSB), Laos

The economic reform in Laos began in 1986 where the central planning system was
replaced by the market-oriented system under the president of Kaysone Phomevihane.
With the new system, Lao economy has gradually exposed to the global economy
through several economic reforms such as trade, investment liberalization, agriculturalreform, privatization, liberalization of price and exchange rate. As a result, tariffstructure was gradually simplified and creation of foreign investment law wasestablished in 1988. Later on, Laos has gained the Generalized System of Preference
scheme (GSP) granted by developed countries especially European countries since
1990s. In 1997, Laos was accepted as a member of the Association of South East Asian
Nations (ASEAN) and joined the ASEAN free trade agreement (AFTA) in 1998.
Recently, another achievement of trade liberalization occurred when Laos has gained
WTO membership on February 2013. WTO is expected to allows the Lao export sector
to access a larger market along with other 158 members in terms of tariff rates (MFN),
transparency and non-tariff barriers particularly in developing markets (Haddad et al.
2006).
Since early 1990s, Laos has enjoyed high economic growth on average of 7 percent
annually although it faced slightly setbacks during 1997-1998 Asian Financial Crisis(AFC) and 2008-2009 Global Financial Crisis (GFC). Resource and manufacturingsectors are among the fastest sectors driving Lao economic growth since 2000s. This
leads to a change of economic structure dramatically. For instance, the share of resource
to GDP increased from 4.3 percent during 1998－2000 to 11.4 percent during 2013－15.
Although manufacturing sector raised its share steadily from 8.0 percent to 10.1 percent
respectively (Table 1) but it is still low when comparing with neighboring countries2.
The share of service sector is dominant, however; most of them are small and medium
enterprises. Despite the share of agriculture declined sharply, the majority of Lao
2

Based on ADB key indicator 2017, the share of manufacturing to GDP at constant price in Laos was 8.5
percent in 2015 whereas the shares in Cambodia, Indonesia, Malaysia, Myanmar, Philippines, Thailand
and Vietnam were relatively higher with 23 percent, 21.5 percent, 23 percent, 22.1 percent, 23.2 percent,28.1 percent and 15.4 percent respectively.

4

population (66 percent) is still remained in such sector. Unlike, less than 1 percent of
total employment works in resource industries, according to the Lao Expenditure andConsumption Survey (LECS 5).
TABLE 1
Change of Economic Structure (%)
Sub-sector
1998－2000 2013－2015
Agriculture
45.4
24.7
Resource
4.3
11.4
Manufacturing
8.0
10.1
Construction
5.8
7.1
Service36.6
46.6
Real GDP
100
100
Source: Lao Bureau Statistics (LSB)

Table 2 shows that service sector such as wholesale and retail in particular covered amajor part of economic growth. At the same time, the contribution from agriculture wasgetting smaller since the late of 1990s. Resource (mining and electricity) andconstruction sectors played an important part to overall growth though their growths
were highly fluctuated during the study periods. This is because the construction sector
is strongly engaged with resource projects such as hydropower projects. In contrast,
manufacturing sector had played an increasing role steadily. In particular, there was anincrease of foreign direct investment (FDI) in special economic zones (SEZs) since theearly of 2010s. For instance, some investment of well-known international firms could
be observed in the zones such as Nikkon (components of camera), Toyota Corporation(components of automobile) and Essilor (sunglasses). Up to the end of 2016, Laogovernment has established 12 special economic zones (SEZs) and there are 295 firms
in total operating in the Zones (Phonvisay, Thipphavong and Manolom, 2017). For thisreason, the prosperity of manufacturing sector can be expected in the future, which
would contribute strongly to the economic development of Laos in terms of income
generation consequently. Still, however, it believes that resource sector will contribute
powerfully to the economic growth of Laos in the near future as several constructions ofhydropower plants are under the pipeline. By 2020, Laos will have more than 90 power
plants in total with an additional new 52 plants3.
TABLE 2
Sources of Growth by Sector (%)
Sub-sector
Agriculture
MiningManufacturing
Construction
3

2001－2005 2006－2010 2011－2015
0.71
1.34
0.83
0.77
1.16
0.38
0.70
0.86
0.93
0.11
0.47
0.99

https://laotiantimes.com/2017/01/10/laos-latest-electricity-facts/5Electricity and water
Wholesale and retail trade
Hotel and restaurant
Finance and insurance
Real estateTransportation andcommunication
Service activities
OthersReal GDP

0.02
2.32
0.02
0.13
0.12

0.25
1.50
0.05
0.62
0.11

0.62
1.56
0.05
0.42
0.30

0.46
0.40
0.52
6.27

0.38
0.44
0.71
7.90

0.51
0.45
0.89
7.93

Source: Author based on data from Lao Bureau Statistics (LSB)

Looking at another dimension of growth analysis through input growth, Table 3 foundthat the capital investment had been the main source of growth since 1991; its
contribution was more than 70 percent on average or subsidizing 4.8 percent points tototal economic growth. In addition, its trend had slightly increased in recent years from
3.63 percent points in the first period to 5.9 percent points. The inflow of foreign direct
investment (FDI) in resource sector was considered as the main source. During
2001－2015, electricity generation and mining sectors had attracted the largest
investment from FDI, more than 60 percent of total approved investment or around 5.6
billion US dollars4. The capital inflow is expected to be crucial for Lao economic
growth in the near future because of several large investment projects from FDI are
under the pipeline. The contribution from productivity (TFP) seems to be indifferent
from labor factor at around 1.13 percent points since 2001. Probably, the decline ofproductivity growth from 1.47 to 0.85 is more interesting that might pose a challenge
for Lao economic growth in the future.
TABLE 3
Sources of Growth by Production Factor (%)
FactorCapital Growth
Labor Growth
TFP Growth
Real GDP Growth

2001－2005 2006－2010 2011－2014
3.63
5.91
5.93
1.24
1.04
1.08
1.47
1.02
0.85
6.33
7.98
7.87

Source: Author based on data from Penn World Table version 9.0, Feenstra, Robert &Marcel (2015).

2.2. Productivity of Lao Economy
Laos performs well among CLMV countries in terms of labor productivity as shown in
Figure 2. In 2014, the labor productivity of Laos was at 9,000 US dollars per worker
which was slightly higher than Vietnam (8,900 US dollars), Myanmar (8,400 US4

http://www.investlaos.gov.la/images/Statistics/rpt_Invest_Summary_Sector1A_2011-2015.pdf

6dollars) and Cambodia (5,400 US dollars) respectively. However, labor productivity
growth of Myanmar was among the fastest along with China, 8.9 percent and 8.7
percent annually during 2000－2014. For Laos, labor productivity grew at 4.6 percent
annually slightly higher than Vietnam and Cambodia. When comparing to old ASEAN
members such as Singapore, Malaysia, Thailand, Philippines and Indonesia, Laos ismuch lack behind. For instance, according to TDRI (2015, p. 25), Lao worker is lesscompetitive in manufacturing, agriculture and service for 3.6, 6.5 and 3.5 times
compared to Thailand.
FIGURE 2
Per-worker Labor Productivity Levels-GDP at Constant Basic Prices per Worker, using
2011PPP, Reference Year 2014

Unit: US dollars
Source: APO Productivity Database 2016

When comparing labor productivity by sub-sectors within Lao economy and their
growths in three different years reported in Table 4, it is clearly seen that laborproductivity of resource sector was extremely high and much higher than other sectors.
Its labor productivity was more than 49 times of the labor productivity of agriculture
sector, and 12 times of service and manufacturing sectors. When accounting thisevidence, the national labor productivity as mentioned earlier might not reflect therealities of Lao economy where is substantially influenced by resource sector. In termsof growth, the national labor productivity grew relatively at high level with around
5.8－6.3 percent annually. However, labor productivity in service sector was likely on
the downtrend as the same as agriculture. Whereas the growth of labor productivity inresource and construction sectors was sharply high during 2008－2013. The laborproductivity of manufacturing sector increased steadily from 4 percent to 10 percent
between two periods.
TABLE 4
Labor Productivity by Sector in Laos and Growth Rate (%)

SectorResource
Manufacturing

Labor productivity(Million Kip)
2003 20082013
164.1 152.0
238.3
9.9
11.9
17.8

7

Annual Growth rate (%)
2003－2008 2008－2013
-1.5
11.4
4.0
10.0

Agriculture
Construction
Service
Real GDP

3.9
12.0
16.7
7.3

4.6
10.9
22.0
9.6

4.8
22.6
25.1
12.3

3.3
-1.9
6.3
6.3

1.1
21.6
2.8
5.8

Note: Productivity = Real value added / Number of employ (person).
Source: Author based on data from LEC 3&5 for employment statistics. Real value added by
sector is from Lao Bureau Statistics (LSB)

3. Japanese Economy
The success of Japanese high economic growth after World War II is well known inliterature. As a result, Japan became a position of high-income country by taking a shorttime period. For instance, according to World Bank Database, income per capita
increased for more than 45 times within 30 years or jumped up from 610 US dollars in
1962 to 30,190 US dollars in 1992. Later on, the Japanese growth model had passed tothe other East Asian economies (Ito, 1996). Many countries in Southeast Asian
including Thailand, Malaysia, Indonesia, Vietnam, Cambodia and Laos have also
followed Japanese growth model. Even though Japan is now not as used to be, but its
past success is worthy to learn. This leads to the main purpose of this section that
reviews Japan during the peak time especially the post-period of World War II in orderto draw out the lesson learnt as well as the drawback for Laos.
FIGURE 3
Real GDP Growth of Japan (%)

Source: Statistics Bureau, Statistics Japan, Ministry of Internal Affair and Communication.
http://www.stat.go.jp/english/data/chouki/03.htm

Japan had enjoyed high economic growth since late 1950s until 1970s with 9 percent
annually (Figure 3). The economy of Japan was led by manufacturing sector as the
productivity growth of this sector in terms of labor productivity was as high as 6.5
percent during 1960s－92, higher than any other sectors. During this period, theeconomic structure of Japan had been changed from basic manufacturing (textiles andtoys) to advanced manufacturing (electronics, steel, and ships) in the 1970s; and moreadvanced manufacturing industries (automobiles and semiconductors) in the 1980s (Ito,8

1996, p.227). Accordingly, the share of manufacturing sector in GDP became larger
from 28 percent in 1955 to 38 percent in 1975. In addition, it had contributed about 2.5
percent points to total economic growth of Japanese economy during 1956－1973. The
robust of manufacturing industries had also enabled the other sectors such as
distribution service, finance, energy, transportation and construction to grow as well
(Takada, 1999). Therefore, it is fairly to conclude that the manufacturing sector isfundamentally central for economic growth of Japan.
The success of Japanese economy especially manufacturing was conditional to anumber of favorable conditions. One of them is Korean War where the exports of Japan
had expanded rapidly during Korean War in mid of 1950s to facilitate the US military(Lucien, 2004). This had encouraged the growth of Japanese manufacturing industries
during this time. The exports included ships, tanks, jeeps, aircraft, textile, metal,
chemical, transportation, machinery and electricity (Sugita, 2003, p.99). Secondly, thecompetition policy rather than industrial policy was believed to strengthen the
productivity of domestic industries especially the automobile and electronics (Hatta,
2016). The competition policies, the establishment of Fair Trade Commission in 1947
and trade liberalization in 1960s for instance, had shaped the ways of doing business ofprivate companies to be more competitive internationally. The internal development ofindustries such as lifetime employment and main bank system were regarded as main
factors for that shape and the expansion of industries (Ito, 1996). During the period of
economic boom, for example, Japanese firms formed a long-term relationship with theguarantee of a job for life and rewards in the forms of return and position to theirworkers. Therefore, workers were willing to work harder. Because of hard working, theproductivity of the firms had improved gradually. Similar to the main bank system
where the banks were not just being the lenders but played a role on monitoring theperformance of firms as a part of investors. The successful industries include electronics,
automobile, electrical equipment and textiles. Whereas industrial policies provided by
the ministry of International Trade and Industry (MITI) such as selective industries andsubsidies is doubtful because some targeted industries such as coal, petrochemical, oil
refining and aluminum had failed to grow as expected (Ito, 1996, p.226). Other
favorable conditions such as high saving rate, education and monetary policy are also
considered but less significant. Therefore, the details for these favorable conditions are
not investigated.
Based on the availability of data since 1970s, Table 5 indicates that capital investment
and productivity are the main two sources of Japanese economic growth. After the crisisof oil’s price in 1973, the economy had been slowdown and did so the productivity. The
economy got even worse after the bubble economy in early 1990s. The demand shock
was claimed as one of main factors that leads to the slowdown of productivity growth ofJapan after 1991, which consequently resulted to labor hoarding, idling of capital stock,
and a decline of return rate on capital (Fukao, 2013). Later on, the outward of Japanese
investment or shifting their production sites to oversee such as China, Thailand andother countries in Southeast Asia region was also believed to back up the decline ofJapanese productivity (Saito, 2015). As a result, the spillovers of R&D from large firms
to smaller firms got smaller in Japan since not only their supply chains and factories
were relocated but also the R&D activities. Fortunately, the productivity and quality of9labor had strongly kept the economic growth in recent years thanks to the productivity
improvement made by large firms.
TABLE 5
Sources of Growth by Factors (%)
FactorMan-hours Growth
Labor Quality Growth
Capital Quantity Growth
Capital Quality Growth
TFP Growth
Real GDP Growth

1970－80 1980－90 1990－2000 2000－2012
0.3
0.4
-0.6
-0.5
0.9
0.6
0.5
0.5
2.2
1.6
1.0
0.1
-0.5
0.4
0.1
0.1
1.7
1.3
0.0
0.5
4.6
4.3
1.1
0.7

Source: The Japan Industrial Productivity Database 2015 (JIP Database 2015), Research Instituteof Economy, Trade and Industry (RIETI), Japan.
http://www.rieti.go.jp/en/database/JIP2015/index.html.

4. Literature Review
This section devotes to the previous empirical studies on productivity analysis in Lao
and Japanese literature. In particular, the studies related to productivity/total factor
productivity grounded on the quantitative analysis are the focus. Since there are several
studies of Japanese literatures were carried out in previous years, only some studies are
selected for review.
4.1. Lao Literature
Honestly, it is relatively hard to find the previous studies on productivity of Lao
manufacturing industry based on quantitative method such as DEA or stochastic
production frontier analysis. Several studies can be found in the case of agriculture butvery rare in the case of manufacturing industry. To my best knowledge, one research isonly found, it is the research by Vixathep (2011). Under his research, Data envelopment
analysis (DEA) is used to analyze the growth of productivity (TFP) for 33 garment
firms during 2004-2005. The key finding reveals that the productivity (TFP) of
sampling garment firms decreased by －40 percent during the studied periods. Suchlarge decline is mainly from a decrease of technical progress because of the demand
shock as claimed by the author. Moreover, the levels of technical efficiency are variedamong firms but the gap is spread out over studied periods because of greater
competition in the global market. In addition, the study finds an improvement oftechnical efficiency and TFP at the individual firms but worse for the whole sample.
The author made a further analysis on efficiency model. The model includes factors
such as age of firms, ownership, capital intensity and the ratio of managerial employee.
As shown by the result, only the variable of staff share is in effect on the efficiency
indicating there is a shortage of managerial employees in the garment firms. Therefore,raising a ratio of managerial employee is necessary for enhancing efficiency.
Other studies have slightly touched on the productivity analysis related to Lao economy.
For instance, Kao (2013) estimates the total factor productivity (TFP) as an indicator of

10

national productivity for 10 ASEAN countries during 1999－2001. For Laos, the study
shows that total factor productivity (TFP) contributed around 1.024 percent points per
year, 4.5 percent points from capital growth and 1.32 percent points from labor growth.
Unfortunately, no lesson learns can be drawn from this study since it is lack of detailed
analysis. Unlike, Nolintha and Yee (2015) use time series data of Lao economy during
1991－2010 based on the Ordinary Least Square (OLS) estimation. Based on theirresult, the growth of Lao economy is predominantly influenced by the capital input
rather than labor input. TFP is claimed to contribute to output growth by 0.03 percent.
In the other hand, the study indicates that there is a positive relationship between output
per labor (labor productivity) and capital per labor (capital intensity). Finally, the study
found the impact of FDI in resource sector on output growth.
These previous studies gave some insights on analysis of economic growth andproductivity in Laos. However, the provision of policy recommendation is likely to belimited since a few numbers of policy variables are included in the model. In addition,
the sample size is also small. Therefore, this study tries to overcome the gap of previous
studies by applying a larger sample size and recruiting more policy variables.
As mentioned, there are more studies on agriculture applying the frontier andinefficiency model, which can be found on Boundeth, Naseki&Takeuchi (2012) andViengpasith, Yabe & Sato (2012) in case of maize; Supaporn (2015) in the case ofsugacane; Soukkhamthat & Wong (2016) in the case of cassava; Inthavong (2005) andPhetsamone (2012) in the case of rice production. Since this study aims only on the
manufacturing industry, they are not in review.
4.2. Japanese Literature
Since the focus on this research is the productivity analysis on Lao industries, a few
previous studies in Japan are selected for review. It is well-understood that many studies
on raising industrial and firm productivity were carried in the past and some are
ongoing especially the research program5 initiated by Research Institute of Economy,
Trade and Industry (RIETI). One of the studies that is directly related to this research isthe study of ‘Explaining Japan’s unproductive two decades’ by Fukao (2013). This
study tried to unveil the reasons behind the slowdown of productivity of Japanese
economy particularly in manufacturing and non-manufacturing sectors in the last two
decades. The result found that the productivity (TFP) growth of both sectors had
declined after 1991 due to demand shocks leading to labor hoarding and idling of
capital stock, a decline of return rate on capital. This is because that capital ratio to GDP
had accelerated but the return rate of capital had dropped at the same time. The author
also claims that United States had enjoyed higher growth of return rate of capital anddid so the productivity mainly because the acceleration of ICT investment which iscontrasted to Japanese counterpart. Another interesting analysis is that large firms had
improved in productivity in recent years whereas small and medium firms were still far
behind. Therefore, the gap between two was getting wider. This incidence was
The ongoing research program on raising industrial and firm productivity is during FY 2016 －2019.
The competed research program was during FY 2011 －2015. There are several research topics under the
research program. More details can be found on http://www.rieti.go.jp/en/projects/program/pg-05/.
511

explained by the different accumulation of ICT and intangible investment between two
groups and less spillovers of R&D from large firms to smaller firms since many large
firms have expanded and relocated their supply chains, factories and their R&D
activities to oversee. To raise the productivity in manufacturing sector, the author
suggests that easing the regional logistics, enhancing tree trade agreement and reducing
corporate tax especially for new Japanese and multilateral businesses are essential.
Similarly, Saito (2015) assesses the current state of the productivity of Japanesemanufacturing industry. He found that although the productivity of Japanesemanufacturing sector (labor productivity) is still among top countries in the front stage
comparing to other OECD countries based on the recent report in 2012 but the position
is not as it used to be in 1990s. He highlights three causes for the decline of laborproductivity in manufacturing sector, namely; (1) outward investment or shifting
production sites abroad, (2) high cost of non-manufacturing sector, (3) low usage of ICT
capital and (4) low turnover rate of unproductive firms or low rate of introduction of
new business – poor indicator of doing business especially starting business.
Fortunately, Japanese manufacturing industry is still more productive in terms of TFP
when compared to Korea and China although Korean firms have improved theirproductivity dramatically during the study periods (Fukao, at el 2008). It is because
Korean firms especially new entries of small and medium firms had over invested in thetangible fixed assets and R&D resulting in low return rate to capital and R&D (Kim and
Keiko 2013). When comparing Japanese labor productivity with the United States,
Fukao (2007) found that the productivity of manufacturing industry in Japan took only
one fifth of the United States. Low productivity in the labor-intensive industries such as
textile was claimed as one of the main reasons attributed to such low labor productivity.
Although the studies reviewed above are informative on Japanese productivity analysis,
however; the sources of productivity growth is unclearly identified. Unlike, this study
attempts to explicitly decompose the sources of productivity growth for Japanesemanufacturing sector into three sources, namely; (1) technological change, (2)efficiency change and (3) scale efficiency change based on the panel data compiled
from the database of EALC 20106.
5. Theoretical Concept, Methodology and Data Source
5.1. Theoretical Concept
The aim of this section is to understand the concept of productivity and sources of itschange. To do so, this section is principally built on the literature of Coelli, et al (2005)
and Balk (2001). Accordingly, productivity is measured by output per unit of input suchas output per worker or working hour or per hectare. However, such measurement has
limitations since not only a unit of labour or single input is used to produce the output
but, in realities, it also involves factors of capital and materials (Coelli, et al, 2005, p.62).
Therefore, output per single unit could lead to misinterpretation of productivity analysis.
Therefore, multifactor productivity (MFP) or total factor productivity (TFP) are moredesirable in use as an indicator for productivity analysis. TFP or MFP is where all
6More details on the database is discussed in 5.3. Data Source

12inputs are taken into account in measuring productivity. According to Coelli, et al (2005,p.62), total factor productivity (TFP) is defined by “a ratio of aggregate output produced
relative to aggregate input used”. For instance, a simple calculation of TFP is via
profitability ratio of a firm by a ratio of revenue (real term) to input cost (real term).
Other methods include Hicks-Moorsteen TFP (HM TFP) Index and growth accounting
where the former is to compare output growth to input growth. Therefore, when output
growth is higher than input growth or HMTFP index is greater than 1 would signal theevidence of productivity improvement. The later method is popularly and widely used
to separate the sources of output growth normally into three sources that are from labour
growth, capital growth and TFP growth. Nevertheless, these calculations are without
limitations when a further analysis of sourcing the productivity or TFP growth over time
is made.
Fortunately, following to Balk (2001), the change of productivity between two periods
can be breakdown into four sources. These sources are (1) technological change (TC),(2) efficiency change (EC), (3) scale efficiency change (SEC) and (4) output mixed
effect (OME) or input mixed effect (IMO). Since the fourth has to dial with the prices ofmultiple output and inputs, only former three are considered as seen in equation 1.
TFP Change = Technical Change ∗ Efficiency Change∗ Scale Efficiency Change(1)Where,
Technological change (TC) refers to the shift of production technology between
two periods.
Efficiency change (EC) exists when a firm is able to improve production
efficiency between two periods or the ability to use available technology or more
efficient use of inputs closer to the technology frontier.
Scale efficiency change (SEC) refers to the improvement (scale efficiency) inthe scale of operations of the firm and its move towards technologically
optimum scale of operation between two periods.
Note that the change productivity and its component is in the form of Malmquist
index.

The implicit form of mathematic formula can be found as:
𝑑𝑜𝑡 (𝑥𝑠 , 𝑞𝑠 ) 𝑑𝑜𝑡 (𝑥𝑡 , 𝑞𝑡 ) 0.5
𝑑𝑜𝑡 (𝑥𝑡 , 𝑞𝑡 )𝑇𝐹𝑃𝐶 𝑠,𝑡 (𝑥𝑠 , 𝑥𝑡 , 𝑞𝑠 , 𝑞𝑡 ) = [ 𝑠
× 𝑠
]
×[ 𝑠
]
𝑑𝑜 (𝑥𝑠 , 𝑞𝑠 ) 𝑑𝑜 (𝑥𝑡 , 𝑞𝑡 )𝑑𝑜 (𝑥𝑠 , 𝑞𝑠 )× [𝑆𝐸𝐶𝑜𝑠 (𝑥𝑠 , 𝑥𝑡 , 𝑞𝑠 )𝑆𝐸𝐶𝑜𝑡 (𝑥𝑠 , 𝑥𝑡 , 𝑞𝑠 )] 0.5
Where

𝑇𝐹𝑃𝐶 𝑠,𝑡 (𝑥𝑠 , 𝑥𝑡 , 𝑞𝑠 , 𝑞𝑡 ) is TFP change between period S and T

13

(2)



𝑑𝑡 (𝑥 ,𝑞 )𝑑𝑡 (𝑥 ,𝑞 )[𝑑𝑜𝑠 (𝑥𝑠 ,𝑞𝑠 ) × 𝑑𝑜𝑠 (𝑥𝑡,𝑞𝑡)] 0.5 is technological change or technological progress7
𝑜

𝑠

𝑠

𝑡

𝑜

𝑡

between period S and T

𝑑𝑡 (𝑥 ,𝑞 )[𝑑𝑜𝑠 (𝑥𝑡 ,𝑞𝑡 )] is efficiency change between period S and T𝑜𝑠𝑠[𝑆𝐸𝐶𝑜𝑠 (𝑥𝑠 , 𝑥𝑡 , 𝑞𝑠 )𝑆𝐸𝐶𝑜𝑡 (𝑥𝑠 , 𝑥𝑡 , 𝑞𝑠 )] 0.5 refers to the scale efficiency change
between period S and T

The explanation of a change for the each component is given in Figure 4. Figure 4
explains the production function of a firm as an example in two periods (S and T) wherethe vertical axis presents output (q) and the horizontal axis refers to the input (x). F1 isdefined as the production frontier or production technology for the first period (S) andF2 refers to the production frontier for the second period (T).
FIGURE 4
Measuring Productivity Change𝑞
F2 in period t

𝑞𝑐

C

𝑞𝑡

E
B

𝑞𝑏

F

F1 in period S

A
𝑞𝑎
𝑞𝑠

D
0

𝑥𝑡

𝑥𝑠

𝑥

Source: edited Coelli, et al (2005, p.71) and Balk (2001, p.169)

At first, a firm produces an actual output of 𝑞𝑠 in the first period (S) at point D by using
an input of 𝑥𝑠 with technology (F1). Similarly, in the second period (T), an actual output
of 𝑞𝑡 is produced at point E using an input of 𝑥𝑡 with technology (F2). Since the actual
outputs produced at both periods by the firm are below the potential outputs on
production frontiers (F1 and F2), output losses due to inefficiency occur in both periods.
Then, the efficiency can be measured in period S and T for the firm. The efficiency level𝑞𝐷denoted by 𝑑𝑜𝑠 (𝑥𝑠 , 𝑞𝑠 ) in period S is (𝑞 𝑠 ) 𝑜𝑟 (𝐴 ) and the efficiency level denoted by𝑑𝑜𝑡 (𝑥𝑡 , 𝑞𝑡 ) in period T is

𝑞𝑡

𝐸

𝑎

(𝑞 ) 𝑜𝑟 (𝐶 ). Note that the efficiency lies between 0 and 1𝑐where 1 refers full efficiency. Thus, the efficiency change (EC) is the ratio of efficiency
𝑞𝑡⁄𝑞𝑐

level in period T to period S or (𝑞𝑠

⁄𝑞𝑎

).

7

Note that the notation of technical change is used to refer the technological change in some literature. In
this study, they are used interchangeable.

14

Next, the change of technological progress can be identified as the change of output due𝑞
to any changes in technology is (𝑞𝑏) under the choice of using input 𝑥𝑠 . If the choice of𝑎using input 𝑥𝑡 is selected, then productivity gain due to the technological change can be𝑞𝑞calculated as (𝑞𝑐 ). The technological change (𝑞𝑏) under the choice of input 𝑥𝑠 is𝑎𝑏𝑑𝑡 (𝑥 ,𝑞 )𝑞denoted as [𝑑𝑜𝑠 (𝑥𝑠 ,𝑞𝑠 )] and the technological change (𝑞 𝑐 ) under the choice of input 𝑥𝑡 is𝑏𝑜 𝑠 𝑠
𝑑𝑜𝑡 (𝑥𝑡 ,𝑞𝑡 )denoted as [𝑑𝑠 (𝑥 ,𝑞 )]. Since it is difficult to select the choice of using inputs, an average𝑜𝑡𝑡of these two choices is considered. Therefore, the technological change is defined as
𝑑𝑜𝑡 (𝑥𝑠 , 𝑞𝑠 ) 𝑑𝑜𝑡 (𝑥𝑡 , 𝑞𝑡 ) 0.5
𝑞𝑏
𝑞𝑐
[ 𝑠
× 𝑠
]
𝑜𝑟 [( ) × ( )] 0.5
𝑑𝑜 (𝑥𝑠 , 𝑞𝑠 ) 𝑑𝑜 (𝑥𝑡 , 𝑞𝑡 )𝑞𝑎
𝑞𝑏
For the scale efficiency change, according to Coelli et al., (2003, p.77), the concept isthat “the scale efficiency of a given firm is then measured using the output distance ofthe observed input-output vectors relative to the variable returns-to-scale (VRS)
frontier and from the cone technology or the constant returns-to-scale (CRS) technologythat is generated from the observed VRS technology”. Under the CRS technology, afirm has fully scale efficiency or equal to 1.
The scale level of efficiency change 𝑆𝐸𝐶0𝑡 based on the frontier 2 as a reference definesas
𝑆𝐸𝐶0𝑡 (𝑥𝑡 , 𝑥𝑥 , 𝑞) =𝑆𝐸0𝑡 =

𝑆𝐸0𝑡 (𝑥𝑡 , 𝑞)
𝑆𝐸0𝑡 (𝑥𝑠 , 𝑞)

𝐸𝑡∗ (𝑥, 𝑞)
𝐸𝑡 (𝑥, 𝑞)

The scale level of efficiency 𝑆𝐸𝐶𝑜𝑠 based on the frontier 1 as a reference defines as
𝑆𝐸𝐶0𝑠 (𝑥𝑡 , 𝑥𝑠 , 𝑞) =𝑆𝐸0𝑠 =

𝑆𝐸0𝑠 (𝑥𝑡 , 𝑞)𝑆𝐸0𝑠 (𝑥𝑠 , 𝑞)

𝐸𝑡∗ (𝑥, 𝑞)
𝐸𝑡 (𝑥, 𝑞)

Where
𝑆𝐸 is scale efficiency index
𝐸 ∗ is efficiency level under the VRS whereas 𝐸 is efficiency level under CRS production
function.
Then, the change of scale efficiency is the average of changes for two methods as below.
[𝑆𝐸𝐶𝑜𝑠 (𝑥𝑠 , 𝑥𝑡 , 𝑞𝑠 )𝑆𝐸𝐶𝑜𝑡 (𝑥𝑠 , 𝑥𝑡 , 𝑞𝑠 )] 0.5

15

5.2. Methodology
Two approaches are widely applied to estimate the production frontier and components
of total factor productivity (TFP) namely Data Envelopment Analysis (DEA) andStochastic Frontiers Analysis (SFA). While DEA uses the linear programming, thestochastic frontier is dependent on the econometric methodology. In this study, DEA
method is used for the analysis since it provides no requirement of econometricestimation, no need to specify a functional from for the production function, and noneed to conduct conventional test of hypotheses for the distance function or frontier(Coelli et al., 2003). Moreover, the estimation can be simply carried out by DEAP
version 2.1. The details of DEA method can be referred to the paper by Coelli (1996) as
well as the discussion of differences between two methods on Coelli et al., (2005).
For the purpose of analysis, two steps are carried out where the first step is theestimation of production frontier that involves the traditional inputs and outputs in orderto estimate the efficiency scores (TE) for individual firms. Note that TE scores are
between 0－1 where 1 means 100 percent or full potential output on frontier. Then thesecond step is proceeded for efficiency effect model based on the econometrics via
Tobit estimate. Tobit method is used rather than OLS is because it can compromise thetruncated data (McCarty and Yaisawarng, 1993). The efficiency model is aimed toinvestigate the factors behind different efficiency across firms. The factors are related tofirm’s specifics such as managerial and organizational structure, and external
environments such as infrastructure, exchange rate, subsidies, other related the export
and investment. The implicit efficiency model is specified as following:𝑇𝐸𝑖 = 𝛿0 + ∑ 𝛿𝑗 𝑧𝑖𝑗 + 𝜀𝑖

(3)𝑗Where,• 𝑇𝐸𝑖 is the efficiency score (TE) for firm i• 𝛿0 and 𝛿𝑗 are unknown parameters• 𝑧𝑖𝑗 are firm-specifics (managerial and organizational structure) andenvironmental conditions such as infrastructure, exchange rate, subsidies, otherrelated the export and investment promotion.
• 𝜀𝑖 is error term
5.3. Data Source-

Laos Case Study

This study used two types of datasets for the analysis of productivity and efficiency in
Laos. Both are cross sectional data. Note that cross sectional data refers to when theseveral firms can be observed at one point in time only. The first type of dataset is theenterprise survey conducted by the World Bank whereas the second type of dataset isthe economic census survey by Lao Statistics Bureau (LSB). Enterprise surveys werecarried out in 2006, 2009, 2012 and 20168. These surveys, except the enterprise survey
2006, use the stratified random sampling design to capture the different subdivisions of
8Note that the data collections on firms’ information such as sale, capital and labor were recorded in 2004,
2008, 2011 and 2015 for the enterprise survey 2006, 2009, 2012 and 2016 respectively.

16

population and covers the diversity of establishments. Industry, size of employment andregion are used to identify the level of stratification for sampling firms. International
Standard Industrial Classification (ISIC) is used for the definition of industries. Total
samples for each survey are 303, 360, 379 and 368 firms in service and manufacturingsectors within 4－6 provinces including Vientiane capital city of Laos. Only enterprises
with 5 employees and above were selected for the survey. Since manufacturing industry
is the focus of this study, only firms in manufacturing sector is used. Unfortunately, due
to large missing data of interested information for the enterprise survey in 2012, only
enterprise surveys of 2006, 2009 and 2016 are utilized. In addition, the weights cannot
be incorporated for the efficiency analysis as well because the enterprise survey of 2006
was conducted without using the survey design. Furthermore, missing values for key
variables are the major concerns leading to the reduction of sample size in use. The
statistics of variables for production frontier and efficiency model is presented in Table
6.
TABLE 6
Statistics Summary of Enterprise Survey
Variable

Obs

Sale (Thousand Kip)
Capital (Thousand Kip)
Material (Thousand Kip)
Labor (Person)

382
382
382
382

Time
Location
Size
Foreign ownership
Export Intensity
Age of Firm (log)
Capital Intensity (log)International Certificate

382
382
382
382
382
382
382
382

Mean
Std. Dev.
Production Frontier
4,280,000
18,000,000
7,460,000
43,300,000
970,000
4,900,000
74.81
165
Efficiency Model
2009
4.24
0.46340.49931.70940.7646
0.11780.32280.63090.4832
4.99452.658117.3531
1.33780.07070.2566MinMax

13,000474
474
5281,000,000
674,000,000
66,100,000
1,400

2006
0.00001.00000.00000.00000.000011.4591
0.00002016
1.00003.00001.00001.00007.602922.42171.0000Note: international certificate of recognized quality includes ISO 9000-1 or ISO 2008
Source: Enterprise survey, World Bank

In addition, the dataset from economic census survey in 2012/13 is also used. Economic
census 2012/13 is the second survey in Laos undertaken by Lao Statistics Bureau (LSB)
during 10-30 May 2013 while the first survey was back in 20069. It is the nationwide
survey including all establishment regardless whether they are registered. The
establishments include profit and non-profit institutions in economic related sectors.
Therefore, institutions such as military, police and family business are excluded.
Regarding to profit institution, there are 126,913 establishments in total. In which,
9

The data on key variables such as sale was collected in a range interval between maximum andminimum levels. Therefore, the first economic census can not be utilized.

17

105,874 establishments were in service sector which shares the largest (85 percent of
total establishments). There are 15,573 establishments in manufacturing sector runningup as the second largest (12 percent in share). The remains are agricultural, construction,
electricity and mining sectors. With respected to establishments in manufacturing sector,
about 85.9 percent of establishments are micro enterprises. Micro-enterprise refers to a
firm who employs labor less than 5 persons. For consistency with the enterprise survey
for analysis, the establishments with employees of less than 5 persons are removed.
Similar to the enterprise survey, International Standard Industrial Classification (ISIC)
is applied to classify the definition of industries. Again, the statistics of variables issummarized in Table 7. Note that since the data collection is different from theenterprise survey, some different variables for efficiency model are observed.
TABLE 7
Statistics Summary for Economic Census Survey
Variable

Obs

Mean

Std. Dev.

Min

Max

Production Frontier
Sale (Thousand Kip)

974

34,90041,200200

200,000

Capital (Thousand Kip)

974

6,060,000

137,000,000

150

4,240,000,000

Material (Thousand Kip)

974

2,140,000

36,700,000

136

1,000,000,000

Labor (Person)

974

14.68
Efficiency Model

26.33

5

456

Location974

0.27210.44530.00001.0000Size

974

1.43430.52221.00003.0000Capital Intensity (log)974

16.12071.7252

10.1779

23.8716

Age of Firm (log)789

1.59550.87160.00003.6109Age of Manager (log)854

3.76450.2431

2.94444.3694

Education of Manager (log)934

1.57830.41300.00001.9459Foreign ownership974

0.0041

0.06400.00001.0000Join-venture ownership

974

0.04310.20320.00001.0000Using accounting system974

0.32650.46920.00001.0000Access to credit

974

0.28640.45230.00001.0000Using TI

971

0.20190.40160.00001.0000Source: Economic Census Survey, Lao Statistics Bureau (LSB), Laos

- Japanese Case Study
The study applies the panel data of Japan manufacturing firms during 1985－2007
constructed from the East Asian Listed Companies (EALC) Database 2010, called
EALC 2010. Note that panel data is when several firms (identical firms) excluding theexist and new entries are observed over time. EALC 2010 is the updated version
constructed by a study group comprised of Japan center for economic research (JCER),
Hitotsubashi university center for economic institutions (CEI), CENU center for China
and Asian Studies, and the center for corporate competitiveness of Seoul National

18

University. The first version is the EALC 2007. The database of EALC 2010 includes
Japanese, Chinese, South Korean and Taiwanese firms listed on the stock exchanges ineach individual country covers different industries including manufacturing. It contains
the annual of capital stock, labor cost, intermediate input and production in both realand nominal term of local currency (Japanese Yen). Note that the real term value isadjusted by price index of 2000. Also, labor input in man-hours is incorporated. This
database is purposely used to compute the productivity database for international
comparison. Fukao, at el (2008) is one of international comparative studies that used
EALC 2007 to construct and compare TFP levels at the industrial levels for Japan,
South Korea and China from 1985－2005.
Based on the aim of this study, data on manufacturing industries10 from EALC 2010 for
Japan is only considered for productivity analysis. The panel data is constructed for 871
firms over 23 years or during 1985 to 2007. So that, there are 20,033 observations in
total. Unluckily, efficiency model can not be executed due to the unavailable
information related to firm’s characteristics and environmental conditions as the samecase of Laos. The statistics on key variables is summarized in Table 8.
TABLE 8
Statistics Summary for EALC 2010
VariableObs
Mean
Std. Dev.
Min
Max
Output (million Yen)
20,033
158,000,000
479,000,000
132,455
12,400,000,000
Capital (million Yen)
20,03364,400,000
188,000,000
11,4433,060,000,000
Material (million Yen)
20,033127,000,000
395,000,000
294,618
9,910,000,000
Labor (man-hours)
20,033
4,972,257
12,000,000
11,052
165,000,000
Source: East Asian Listed Companies (EALC) Database 2010, the Japan Center for Economic Research,
the Hitotsubashi University Center for Economic Institutions, the CENU Center for China and AsianStudies, and the Center for Corporate Competitiveness of Seoul National University

10

Industrial codes of 6 to 25 are only considered. The list of industrial codes can be found in Table 15 inAppendix.

19

6. Empirical Results
6.1. Case Study of Lao Manufacturing industry
Due to the limitations of data and the capacity of DEAP version 2.111, only theefficiency and scale efficiency are analyzed for samples in enterprise and economic
census surveys. Firstly, the result for enterprise survey is presented as following.
-Enterprise Survey

Generally, the efficiency level of firms in surveys is relatively low with 20－40 percentof the frontier. At the same time, the scale efficiency is below full-scale optimization.
This should suggests that there are more spaces to progress the efficiency of firms aswell as scale efficiency. The result also shows both means of technical and scaleefficiency for firms are varied across times which indicates that, for instance, the changeof the mean efficiency is seen to be improved slightly over the study period (Table 9).
However, the efficiency in recent years had declined from 44.6 percent in 2008 to 35.3
percent. For the change of scale efficiency, the mean of scale efficiency index is on the
decline from 0.733 in 2004 to 0.702 in 2015. Such decline of both indicators could bepartially affected by the external or demand shocks such as the global financial crisis in
2008. Nonetheless, there is a need for caution on the analysis of changes in efficiency
and scale efficiency during the study periods since the datasets are cross sectional dataand sample sizes for each survey are small which might not be convincible to represent
manufacturing industry.
TABLE 9Efficiency and Scale Efficiency (Mean) for Enterprise Survey
Year
2004
2008
2015

Scale Efficiency
Index
0.733
0.761
0.702

Efficiency Level
0.258
0.446
0.353

No.
Sample191
85
109

Note that the suffix of ES refers Enterprise Survey whereas EC refers to Economic Census
Survey.
Source: Author’s estimation

To find the ways to improve the efficiency of firms, efficiency model is used shown in
Table 10. Overall, the model is significant at 1 percent level and the value of log
likelihood is also high indicating that the models are good enough to represent thedataset. The finding indicates that firms in small and medium size are less efficient thanlarge firms by 75 percent and 61.2 percent respectively. This implies that the size offirm affects the operational efficiency, so that firms can be more efficient if their size is
11

DEAP 2.1 can apply the Malmquist DEA method to only the panel data to estimate the indices of totalfactor productivity (TFP) change, technological change, efficiency change and scale efficiency change.
Therefore, with the cross sectional data, the change of productivity and technology can not be estimatedby DEAP 2.1. However, it is possible to estimate the technological and productivity change for crosssectional datasets in two different periods if the econometric method was used.

20

larger. Secondly, firms who has the international certificate of recognized quality suchas ISO 9000-1 or ISO 2008 are more efficient than who has not by 42.1 percent.
Because the international recognized quality certification requires firms to improve their
managerial and operating system by themselves in order to meet the requirement ofmarkets12, therefore; firms with international recognition performs better than firms
without. The reverse relationship between capital intensity and efficiency might
indicates that the return from capital per worker is declining across firms. This shouldreflect an over-supply of capital for a worker resulting in less efficient or it means
workers use capital input not efficiently. Another possible reason is that the capital
investment is under-utilized because of the declining orders or decrease of demand(Vixathep, 2011, p. 104). For this reason, it is not recommendable for firms to increase
the ratio of capital to labor to prevent a further loss of efficiency unless the skills oflabors are enhanced to exploit the new investment efficiently. This result is similar tothe previous study for the case of garment industry. However, it should not be the mainconcern since the coefficient is relatively small.
Although the coefficients for other firm’s specifics such as foreign ownership, export
intensity and age of firm have expected signs, they are insignificant to influence thevariation of efficiency. Hence, firms are not different in efficiency under these firm’s
characteristics. For instance, foreign firms are indifferently efficient compared todomestic firms because many of foreign firms are sub-contact and spend less on R&D
or innovation activities. The typical example is the garment industry where many
garment firms are medium with sub-contact production. The process of their production
is simply regarded as Cut-Make-Trim (CMT) for the orders overseas. Therefore, foreign
firms invest little in R&D and human development as the same as domestic firms(Nolintha and Jajri, 2014). Similarly, the location effect shows no sign of influence on
efficiency. The model also includes the time trend, which shows that the time trends are
positive in both years compared to the year 2004.
TABLE 10
Efficiency Model for Enterprise Survey
Independent Variable
Time2009
Time2016
Location
Small Size
Medium Size
Foreign Ownership
Export Intensity
Age of Firm (log)
Capital Intensity (log)International Certificate
Constant term
12

Coefficient
1.270**
1.111**-0.119-0.750***-0.612***
0.070
0.141
0.108
-0.067*0.421**-1.129Std. Err.
0.555
0.546
0.103
0.155
0.154
0.162
0.164
0.098
0.038
0.213
0.999

t-statistics
2.290
2.040
-1.160-4.840-3.980
0.430
0.860
1.100
-1.7701.980-1.130More details on international certification can be found on https://www.iso.org/home.html

21

Log likelihood
Prob > chi2
Number of observation-529.80.000
382

Note: ***, **, and * denote significance at the 1%, 5% and 10% level respectively.
Source: Author’s estimation-

Economic Census Survey

The efficiency mean of sample in census survey is 22.5 percent, which is slightly lower
than the ones in enterprise surveys. The efficiency means across firms in sub-industries
are varied. Table 11 shows that the efficiency levels of firms in electrical and garment
industries are higher than firms in other industries. Whereas, firms in basic metal,
rubber plastic, food, beverage, tobacco and wood processing industries are among least
efficient comparatively. Note that the efficiency levels for firms in motor vehicles,
electronic and leather industries should be inconvincible for analysis since the standard
deviations are relatively high. Although there is a need to be careful for interpretation
because of small samples in some sub-industries, the analysis gives some hinds on
different efficiency among different manufacturing industries at least. Such differencesin efficiency across firms are due to different conditions such as firm’s characteristics
and environmental conditions that are latter investigated in efficiency model.
TABLE 11
Efficiency Level (Mean) for Firms in Sub-sector in 2012
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

Sub-sector
Motor vehicles and trailers
Electrical equipment
Leather and related productTextileChemical and pharmacies
Computer and electronics
Wearing apparels
Coke and refined petro
Other non-metallic miner
Printing
Basic metal
Furniture and other manufacturing
Wood and processing
Food, beverage and tobacco
Rubber and plastic product
Fabricated metal productNo. sample
2
2
3
2
13
11
126
13
6
22
63
234
24
70
381
2

Efficiency Score (TE)0.62500.55350.45100.40220.37740.37130.35370.32980.27670.25360.25250.22280.20410.19410.16390.1182

Source: Author’s estimation

Factors behind the efficiency across firms in economic census survey is reported inTable 12. It finds some similar and contrast results with the previous model for theenterprise survey. Firstly, it similarly finds that small and medium firms are less22

Std. Err.
0.26530.01520.2256
0.06930.08420.12050.02750.07680.07550.04890.02890.01310.04590.02700.00900.0207

efficient than larger one. The values of their coefficients between two models are also
closed. Secondly, the foreign firms are also insignificant along with the age of firms on
efficiency. However, the location effect and capital intensity are plausible. Both of theircoefficients are significantly positive. This is because that the economic census survey
covers samples in more locations, where the development of infrastructure in noncapital cities are much different from the capital city. Such differences in infrastructuredevelopment appear to influence the efficiency across firms. These results interpret theimportance of infrastructure development and the availability of capital to workers for
production efficiency. However, the capital intensity should again not be the mainconcern for firms since its coefficient is relatively small.
More interesting results that can be drawn are that firms who uses accounting system
and access to credit are more efficient than who doesn’t by 31.6 percent and 18.8
percent respectively. Surprisingly, the report of economic census survey 2015 showsthat more than 80 percent of all manufacturing establishments operates without
accounting system (Table 5.1, p.71). If so, following the result of efficiency model, one
way that could enhance the production efficiency of firms is to introduce the standard
accounting system into firm’s management; assuming other conditions constant.
Similarly, the efficiency can be enriched if firms could access to credit. Unfortunately,
human capital measured by age and education of managers shows no evidence of effecton efficiency.
TABLE 12
Efficiency Model for Economic Census Survey
Independent variable
Location
Small Size
Medium Size
Capital Intensity (log)Age of Firm (log)Age of Manager (log)
Education of Manager (log)
Foreign ownership
Join-venture ownership
Using accounting system
Access to credit
Using TI
Constant term
Log likelihood
Prob > chi2
Number of obs

Coefficient
0.155*-0.806**-0.568*0.062**
0.018
0.132-0.1210.519
0.203
0.316***
0.188**
0.148-2.838***-874.150.000
649

Std. Err.
0.086
0.340
0.335
0.024
0.044
0.164
0.102
0.664
0.201
0.095
0.081
0.120
0.812

t-statistics
1.800-2.370-1.700
2.570
0.410
0.810
-1.190
0.780
1.010
3.350
2.310
1.240
-3.500Note: ***, **, and * denote significance at the 1%, 5% and 10% level respectively.
Source: Author’s estimation

23

6.2. Case Study of Japanese Manufacturing Industry
With richer data of panel, the change of productivity and its components at firm levelcan be easily estimated for the sample. However, because firm’s specifics are notobserved in the database, the efficiency model can not be constructed like in the case of
Laos. Table 13 shows the result on the growth of productivity, technology, efficiency
and scale efficiency. In general, the total factor productivity of firms had improved by
1.1 percent annually during the study periods. Such improvement was mainly from thecontribution of technological progress whereas the technical efficiency brought the
productivity downturn and not much contribution from scale efficiency. In particular,
the contribution from technology was extraordinarily high during the period of
2001－2007 with more than 5 percent points. This should imply that firms had invested
more on new technologies or innovation such as information communication oftechnology (ICT) or R&D during this period. Following to Inoue and Koguchi (2017),
and Fukao (2013), Japanese manufacturing sector especially large firms had achieved
high productivity during 1995－2015 due to the investment of ICT capital andintangible investment-innovative property, software and database.
TABLE 13Growth of Productivity and its Components (Mean) in Sub-periods
Sub-period

Technologies

Efficiency

1986－1990
1991－1995
1996－2000
2001－2007
Mean

0.1%
1.1%
1.0%
6.2%
2.4%

0.5%
-0.8%
-0.3%
-3.6%
-1.3%

Scale
Efficiency
0.2%
-0.2%
0.4%
-0.2%
0.0%

Total Factor
Productivity (TFP)
0.8%
0.1%
1.2%
2.0%
1.1%

Note that the annual change is reported in Table 16 in Appendix
Source: Author’s estimation

In contrast, firms did poorly in utilizing the technologies, which leads to decline ofcontribution from efficiency component to the productivity growth. Such decline wasdue low investment on human development and organizational structure (Fukao, 2013).
This prevents firms to catch up to the space of technological progress. It suggests that toutilize the technologies efficiently, there is a call for firms to invest more on humandevelopment and organizational structure. Figure 5 displays that the efficiency level had
declined explicitly from 80 percent in late of 1980 to 68 percent in 2000s. The mostdecline in efficiency during the study periods were firms in industries of wood product,
leather, paper and allied, textile mill products, printing and publishing products, primary
metal, and instruments.

24

FIGURE 5
Efficiency Level (Mean) during 1985-2007

Source: Author’s estimate

Since the decline of technical efficiency is the drawback for productivity growth, further
information on efficiency for firms in sub-sectors should be useful to what extension.
Table 14 delivers the report of efficiency level by ranking for firms in sub-industries
during 2003－2007. Interesting to know that firms in the industries related to electricaland electronic, miscellaneous, non-electrical machinery, petroleum and coal, instrument,
printing and publishing, food, furniture and wood, and transport equipment were among
top ten in efficiency. In contrast, firms in the industries of textile, metal, leather, stone,
and glass were the least efficient firms. In fact, firms in electrical and electronic,
miscellaneous, non-electrical machinery industries are used to be at the bottom ten of
the least in efficiency during the late of 1980s, which implies that firms in theseindustries had improved their managerial and organizational efficiency over time.
TABLE 14
Efficiency Level (Mean) by Firms in Sub-sector during 2003－2007
No.

Sub-sector

1
2
3
4
5
6
7
8
9
10
11
12

Electrical and electronic machinery
Miscellaneous manufacturing
Non-electrical machinery
Petroleum and coal products
Instruments
Printing, publishing, and allied products
Food and kindred products
Furniture and fixtures
Transportation equipment and ordnanceLumber and wood products
ApparelChemicals
25

No.
Sample
78
19
26
4
5
17
9
130
7
1
43
74

Efficiency
Score (TE)
0.817
0.706
0.705
0.698
0.668
0.653
0.649
0.633
0.624
0.622
0.621
0.616

Std. Err.
0.004
0.018
0.004
0.041
0.009
0.015
0.006
0.011
0.008
0.021
0.010
0.004

13
14
15
16
17
18
19
20

Fabricated metal
Motor vehicles
Rubber and miscellaneous plastics
Leather
Stone, clay, and glass products
Textile mill products
Paper and allied products
Primary metal

45
133
124
68
19
27
31
11

0.614
0.614
0.612
0.611
0.599
0.590
0.550
0.541

0.007
0.006
0.006
0.027
0.006
0.015
0.009
0.006

Source: Author’s estimation

7. Conclusion and Implication
Laos is one of developing countries with high economic growth. However, such highgrowth is heavily weighted with physical capital mainly from resource sector. As aresult, labor productivity of resource sector is surprisingly extremely high distorting themeasurement of national labor productivity as well as income per capita. Also, under
the resource led growth, Lao economy is unsustainable in terms of income distribution,
limited stock of resource and easily exposed to external shocks such as commodities’
prices. In parallel, Lao government has an ambitious vision to upgrade the income of
Lao people to another level by 2030. This means that Lao economy needs a strong
growth at least 7.5 percent annually. Therefore, it is a big challenge for Laos to sustain
the economic growth and achieve the government’s target in the future. Developing
manufacturing industry through productivity is an alternative option to improve
economic growth, income level and income distribution in the sustainable way. The
current knowledge and understanding on productivity of Lao economy andmanufacturing industry are limited since only few literatures were carried. Therefore,
this study aims to add an additional value into the existing literature by investigating the
productivity of Lao economy and manufacturing industry based on qualitative andquantitative analysis.
The study finds that there is a space for Laos to develop its manufacturing industry
since the share of manufacturing in GDP is small and mainly labor intensive industry.
Based on the Data Envelopment Analysis (DEA) for enterprise survey, the efficiency aswell as scale efficiency seems to be declining in recent years. Overall, including thesample in economic census survey, the efficiency level was low around 20-40 percent offrontier. With this evidence, there is a room to improve the efficiency. To do that, thisstudy suggests that the areas of infrastructure development, firm’s size, introduction of
accounting system, credit access and international quality certificate recognition shouldbe the focus. To what extent, promoting the use of accounting system, credit access andinternational certificate should be prioritized in the short term whereas the infrastructuredevelopment and increasing the size of firm through larger market access are considered
for the long term priorities. Finally, it is unfortunate to inform that the technologicalprogress can’t be investigated due to limited data and the capacity of statistical software.
The experience of Japan shows manufacturing industry had been the leading driver of
economic growth since early 1950s. Economic reforms such as competition policy andtrade openness were believed to strengthen the manufacturing industry to become more

26

productive. With panel data, the sources of productivity growth for Japanese firms canbe decomposed and analyzed. The result indicates that the slowdown of productivity
growth was mainly from the deterioration of efficiency meaning that Japanese firms
were less efficient since late 1980s. However, there was strong contribution fromtechnological progress made by firms. The most efficient firms during 2003-2007 werein the industries of electrical and electronic, miscellaneous, non-electrical machinery,
petroleum and coal, instrument, printing and publishing, food, furniture and wood, andtransport equipment. Case study of Japan has an implication for Laos, as to improveresearch on productivity analysis of Lao economy and industries in the future, thestatistics needs to be improved especially Economic Census Survey. For instance, thepanel data should be constructed and more quantitative information on investment in
hardware such as machinery, software such as information communication oftechnology (ICT), human resource such as skills of labor, and research & development(R&D) should be captured in the survey questionnaire.

27

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30

9. Appendix
TABLE 15
List of Industries on EALC Database 2010
Indus Code
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33

Industry Name
Agriculture
Coal mining
Metal and nonmetallic mining
Oil and gas extraction
Construction
Food and kindred products
Textile mill products
ApparelLumber and wood products
Furniture and fixtures
Paper and allied products
Printing, publishing and allied products
ChemicalsPetroleum and coal products
Leather
Stone, clay and glass products
Primary metal
Fabricated metal
Non-electrical machinery
Electrical and electronic machinery
Motor vehicles
Transportation equipment and ordnanceInstruments
Rubber and miscellaneous plastics
Miscellaneous manufacturing
Transportation
Communications
Electrical utilities
Gas utilities
Trade
Finance, insurance, and real estate
Other private services
Public service

Source: East Asian Listed Companies (EALC) Database 2010, the Japan Center for
Economic Research, the Hitotsubashi University Center for Economic Institutions,the CENU Center for China and Asian Studies, and the Center for Corporate
Competitiveness of Seoul National University

31

TABLE 16
Technology, Efficiency, Scale Efficiency and Productivity Change (Mean) during 19862007
Year
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Mean

Technological
Change (TC)
-0.7%
-3.1%
0.1%
1.9%
2.2%
0.7%
1.1%
5.9%
-0.5%
-1.6%
-0.7%
3.3%
4.9%
-1.1%
-1.2%
6.0%
8.6%
-1.1%
9.1%
5.0%
6.6%
9.0%
2.4%

Efficiency Change
(EC)
-0.4%
2.0%
1.8%
0.0%
-1.1%
-0.2%
-2.1%
-4.9%
0.3%
3.0%
1.8%
-1.5%
-6.5%
2.0%
2.9%
-7.3%
-5.6%
5.5%
-4.1%
-2.8%
-4.2%
-6.8%
-1.3%

Source: Author’s estimation

32

Scale Efficiency
Change (SEC)
-1.0%
2.1%
0.5%
-0.2%
-0.2%
-0.4%
-0.7%
-1.4%
1.5%
0.1%
0.8%
-0.9%
-0.1%
1.3%
1.1%
0.4%
0.1%
-0.1%
-1.5%
0.2%
-1.0%
0.5%
0.0%

TFP Change(TFPC)
-2.1%
1.0%
2.4%
1.7%
0.8%
0.1%
-1.7%
-0.6%
1.3%
1.5%
1.9%
0.9%
-1.9%
2.2%
2.9%
-1.3%
2.7%
4.2%
3.2%
2.3%
1.1%
2.1%
1.1%