Discussion Papers In Economics
And Business

Does ISO14001 raise firms’ awareness ofenvironmental protection?—Case from Vietnam
Bin Ni, Hanae Tamechika, Tsunehiro Otsuki and Keiichiro Honda

Discussion Paper 16-05

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANDoes ISO14001 raise firms’ awareness ofenvironmental protection?—Case from Vietnam
Bin Ni, Hanae Tamechika, Tsunehiro Otsuki and Keiichiro Honda

Discussion Paper 16-05

March 2016

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANDoes ISO14001 raise firms’ awareness ofenvironmental protection?—Case from Vietnam
Bin Ni† Hanae Tamechika‡

Tsunehiro Otsuki§ Keiichiro Honda**

March 29, 2016

Abstract
Environmental protection is an inevitable issue that developing countries all have todeal with during the process of inviting foreign direct investment (FDI). However,high correlation between FDI and pollution doesn’t necessarily indicate that foreign
firms are to blame. In this paper, we apply firm-level panel data in Vietnam andunique information on waste discharge to show that foreign firms are actually more
active to acquire ISO14001, a voluntary environmental standard. And the adoption
will in turn improve firms’ performance in waste control. It also increases firms’
welfare as well as their productivity level. This paper provides strong evidence thatfirms’ efforts towards corporate social responsibility will eventually benefit
themselves as well.

JEL classification: D22, F21, F64, Q56
Keywords: FDI, ISO14001, Vietnam, environmental protection
	
  

	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
†

Corresponding author. The Graduate School of Economics, Osaka University.

‡

The Graduate School of Economics, Nagoya City University.

§

Osaka School of International Public Policy, Osaka University.

**

Department of Administration, Prefectural University of Kumamoto.

	
  

1
	
  

Introduction

It is known to all that foreign direct investment (hereafter FDI) has been a
consider- able driving force to spur the economic growth in developing countries,
especially in newly-emerging economies. But rapid growth usually comes with aprice—pollution. Given the simultaneous rise in FDI and pollution level, it is natural tothink that a positive correlation might exist between these two. Critics have accusedforeign investors of shifting their heavily-polluting activities to countries with lax
regulations in search of “pollution-haven”, however, empirical evidence consistent withthis hypothesis is surprisingly rare (Cole, 2004). Actually foreign firms are found to be
more energy efficient compared to state-owned firms (Eskeland and Harrison, 2003; He,2006). This might be due to the advanced waste-processing technology adopted byforeign firms and their awareness to achieve corporate social responsibilities (Lyon andMaxwell, 2008). Other stories include “protecting institutional reputation, appealing to‘green consumers’, deterring lobbying and boycotts by environmental groups, avoiding
regulatory scrutiny, and preempting future regulation”1. Under certain circumstances,
we highly doubt the “pollution haven” hypothesis and propose the opposite idea that themore foreign firms invest in the host country, the more likely they become selfrestrained in the sense of environmental protection.
In this paper we would like to verify this hypothesis by evaluating firms’
participation in a voluntary environmental program—ISO14001, in the context ofVietnam. ISO14001 is considered one of the most widely recognized voluntarystandards2 for Environmental Management Systems, whose adoption is likely to be
incurred by firms’ spontaneous act3. Thus the possibility of acquiring ISO14001 is
usually positively associated with firms’ willingness to involve in the environmental
protection. By quantifying firms’ efforts before and after attending this program, we
hope to answer the following questions: are foreign firms more active to be involved in
ISO14001 than their domestic counterparts? How can ISO14001 improve firms’ overall
performance, especially their efforts in waste control?
By applying various methods to clear these doubts, this study differs from theprevious literature in several ways: First, it will be the first paper to explore how firms’
participation in voluntary program affects the pollution behavior in Vietnam, thus fill in
the research gap in the developing countries (Arimura et al., 2014); Second, the
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
1

Quoted from Bui and Kapon (2012).
The environmental protection paradigm in developing countries is moving away gradually from compulsory approach to more
flexible and voluntary ones (Tambunlertchai et al., 2013).
3
Though some have argued that the adoption of ISO14001 is motivated primarily by domestic regulatory and market pressures(Khanna and Anton, 2002; Lyon and Maxwell, 2004). http://www.iso.org/iso/about/iso member.htm Accessed on 2014/11/25.	
  
2

	
  

1

measurement will be based on multiple indexes, instead of single ones. After robustness
check, we do achieve consistent results that support our hypothesis. The coherence, on
the other hand, also leaves us the room for policy implications and future study.
This paper is organized as follows: in the next section we talk briefly about thepollution situation in Vietnam. Literature review then comes after. In section 4 we
describe the data and estimation strategy, followed by robustness check and findings.
Section 6 concludes.	
  
	
  
2
Overview
2.1

About ISO14001

The International Organization for Standardization (ISO) was founded in 1946,
which has 165 members by far, each representing a country. It is the most prominent
developer of standards in the world. In 1980s, ISO introduced ISO9000 standards for
quality manufacturing practices. Building on this system, ISO set up ISO14001
environmental standards in 19964. According to the definition by ISO, ISO14001 sets
out the criteria for an effective environmental management system so that firms canfollow, aiming at minimizing the negative impact firms’ operations might have on
environment. The criteria includes, for example, whether firms are using
environment-friendly materials for their production, whether the waste discharge has
been properly dealt with (such as chemical cleaning detergent improvement), or
whether some of the disposed wastes can be reused. By adopting ISO14001, firms can
not only improve corporate image among regulators, customers and the public, but also
reduce cost of waste management and distribution while increase savings in
consumption of energy and materials; (ISO Homepage). The procedure is based on a
Plan-Do-Check-Act cycle (for details see Martin, 1998).
Another feature of this standard is that the adoption takes a voluntary approach andthe certification is performed by third-party organizations rather than direction
application. The initial cost can be burdensome for small and medium-sized firms (itranges from USD24,000~128,000, depending on the size of the firm. Jiang and Bansal,
2003). Firms also have to spend extra cost such as training and auditing fees. Thus firms
are faced with a tradeoff between considerable cost to acquire ISO14001 and thebenefits afterwards, which leads to the uncertainty of the adoption. In this paper, we try
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
4

In recent years, ISO22000 food safety standards, ISO26000 social responsibility standards, ISO36000 risk management standards,
and ISO50001 energy management systems are also introduced.

	
  

2

to uncover the truth by focusing on the case of Vietnam.
2.2

Why Vietnam?

In this paper we would like to focus on Vietnam, a typical example that witnesses
both fast growth in foreign investment and industrial pollution at the same time.
According to Central Institute of Economic Management Report (CIEM) 2007 and
CIEM 2008, FDI in 2007, as a share of Vietnam’s GDP, has risen to more than 20%, toas much as five times that in 2000. In a single year between 2005 and 2006, about 60%of the total FDI were in the industrial sectors while 66.7% of the capital flew into heavy
industries5. Figure 1 shows the structural change of the sectors in Vietnam. This is inline with Vietnam’s national policy of rapid industrialization and transition from rural
economy.
Figure 1: Structural Change in Vietnam (percentage)50	
  Agriculture,	  forestry	
  and	  :isheries	
  

40	
  
30	
  
20	
  

Industry,	
  construction	
  

10	
  

Services	
  

0	
  
1995	
  

2000	
  

2005	
  

Source: Social-Economic Development Plan, 2006-2011
Meanwhile the pollution in Vietnam is considered to get even worse if the current
pattern of industrialization continues and no further control is activated. Take air
pollution, for instance, nearly half of Nitrogen dioxide (NO2) emission is due toindustrial development, and when it comes to Sulfur dioxide (SO2) industry becomesthe major criminal (Vietnam: Air Quality Profile 2010). These two kinds of pollutants
are detrimental to both human health and the environment. Liquid (total suspended
solids) and solid wastes (chemical and metal) also constitute a large portion of the totalindustrial discharge. Though Ministry and Departments of Natural Resources and
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
5

Review and analysis of the pollution impacts from Vietnamese manufacturing sectors (2008), a report conducted by World Bank.
Hereafter referred to as Review (2008).

	
  

3

Environment (MONRE) in Vietnam tries to record the pollution level in all aspects, theprecise data is not available. Instead we approximate the amount of pollution in
Vietnam by using the pollution intensity index constructed by World Bank’s Industrial
Pollution Projections System (IPPS). The same method has been applied in Mani andJha (2006), and Ngo (2010). From Figure 2, we can see that the total pollution has risen
by nearly 150 percent over five years. To decompose the contents, we find that most of
the increase comes from air and solid waste, suggesting a possible shift in the waste
composition6.
Figure 2: Pollution Level in Vietnam (10,000 tons)	  	
   4500	
  	
   4000	  	
  	
   3500	  	
  Total	  
	
   3000	
  	
   2500	
  
Air	
  
	
   2000	  	
  Solid	  
	
   1500	
  	
   1000	  Liquid	  	  	
   500	
  
	
  
0	
  
	
  
2004	
   2005	
   2006	
   2007	
   2008	
  
	
  
	
  
Source: Calculated by authors based on the statistics from World Bank’s “Review andanalysis of the pollution impacts from Vietnamese manufacturing sectors P89-93.”
Given the fact that FDI keeps flooding into Vietnam, it should be important andinteresting to investigate what role foreign firms play in “contributing” to this wave ofpollution. By using ISO14001 as the benchmark, we would like to measure the awareness of foreign firms’ environmental protection and the post-adoption performance,compared with the case for domestic firms. If positive result is to be achieved for theforeign firms, then we can provide more evidence to refute the “pollution havenhypothesis”, at least in the context of Vietnam.
	  3Literature Review
By far several studies have investigated the direct relationship between FDI andpollution level: Jiang et al. (2014), He (2006) as well as Eskeland and Harrison (2003)
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
6

Review (2008) provides us with extra index: the most seriously polluted areas are those that experience the fastest economic
growth, such as Ho Chi Minh City and Hanoi. They have the highest overall ranking on the National Pollution Index. As for sectors,
heavy industries are undoubtedly responsible for the major waste discharge.

	
  

4

all reach the unanimous conclusion that FDI has a negative impact on the pollution levelin the host country. Based on this stylized fact but taking a step further, we would liketo make explicit of the mechanism behind the phenomenon. We will divide the process
into two steps: to find out how FDI (or firm’s ownership in the micro level) affects
ISO14001 adoption and the impact of ISO14001 adoption on firms’ polluting behavior.
As for the first step, there are mainly two kinds of theories: convergence anddivergence (Prakash and Potoski, 2007). Convergence advocates claim that foreignsubsidiaries usually conform to global standard rather than adapting to host country
characteristics. In other words, if the subsidiaries come from a country with a highcoverage of ISO14001 adoption, it is quite likely that these firms will also acquire thecertificate in the host country. And within the convergence debate, the main stream
opinion is that foreign firms face greater scrutiny from local government, which gives
them more incentive to adopt ISO14001, or even encourage their suppliers. Thus FDI
will have a positive influence on firms’ adoption of ISO14001 in the target country.
While divergence supporters hold the opposite opinion: foreign investors choose to
locate in developing countries because they face less stringent environmental control.
And they no longer need to play by the rule as they do in the home country7. In theempirical verification, positive relationship is found between FDI and ISO14001
adoption in Thailand (Tambunlertchai et al., 2013) as well as Malaysia (Arimura et al.,2014). Both studies applied firm level data. In the macro level, similar result is achieved(Prakash and Potoski, 2006). This paper will be aimed at re-evaluating FDI’s role in the
ISO14001 adoption preference.
The second step focuses on the relationship between ISO14001 adoption and firms’
performance. There is a large theoretical literature to study the connection between
compulsory regulations and firms’ polluting behavior, but few are found to investigate
voluntary program’s impact (e.g. Lyon and Maxwell, 2008). The conclusion has been
mainly drawn on empirical evidence. Previous studies have used single pollution
measure to assess the impact of ISO14001 (Potoski and Prakash, 2005; Turk, 2009) andthey all find ISO14001 reduces pollution discharge. Meanwhile, Arimura et al. (2008)
verify ISO14001’s positive influence on reducing both solid and liquid waste in the caseof Japan. Furthermore, Arimura et al. (2011) find ISO14001 also improves firms’
supply chain management. Apart from ISO14001, other voluntary environmental
programs promote firms to curb pollution as well (Bui and Kapon, 2012; Kim and Lyon,2011).
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  

7	
   Political economists also argue that corruption plays its part in affecting environmental policy stringency to attract FDI, such as

Fredriksson et al. (2003), Cole et al. (2004).

	
  

5

Our methodology is closest to that used in Blackman et al. (2010). We will
describe the estimation strategy and data in the following section.
	
  
	
  

4

Estimation Strategy and Data

4.1

Estimation Strategy

For empirical verification we apply the firm level data in Vietnam. To account forthe potential self-selection endogeneity problem, we incorporate it into a two-stepsample selection framework.
ISOit* = δ it ⋅ Zit + Uit(1)

Yit = α it + βiso ⋅ ISOit + βi ⋅ Xit + ε it(2)

where ISO=1 if ISO*>=0 and ISO=0 if ISO*<0.
Firstly we estimate the propensity of firms’ decision to adopt ISO on a series ofexogenous variables. ISO is a dummy variable which takes value of 1 if firm i adopts
ISO14001 at time t, otherwise 0. It’s taken from the observed data. Zit in Eq. (1) is avector of derterminants which lead to the adoption. It includes both objective andsubjective firm characteristics. The former consists of firm size (number of labor), FDI(foreign capital/total capital) and capital-labor ratio while the latter includes answers
based on the firms’ self-evaluation, such as whether they follow the environmental
regulations. Uit is the error term.
Xit in Eq. (2) has similar elements as Zit. But when the Heckman-style model isapplied, exclusion restriction has to be met. Thus we include some of the subjective
variables in the selection equation (Eq. (1)), but exclude them in the outcome equation.
Basically we choose the explanatory variables consistent with Tambunlertchai et al.
(2013) and Arimura et al. (2014), but have to give up some (export status, ISO9000
certification, etc) because of data availability. Concerning dependent variable Yit, it has
two sets of indicators: waste discharge and non-environmental performance (turnover,average salary and total factor productivity). Each variable of interest will be estimatedseparately8. Year dummies and industry dummies are included in both equations. See
Table 1 for detailed description of the variables.
As for TFP, it needs extra effort. Since traditional solow residue’s approach isunable to isolate the true productivity from statistical noise, recent literature has
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
8

	
  

We use “treatreg” as our baseline estimation command and try other alternatives for confirmation.

6

exclusively proxy methods (Olley and Pakes, 1996; Levinsohn and Petrin, 2003)9.
Unfortunately, due to quality issue, neither is the ideal method to be used in our analysis,though we use them as robustness check tools. Another option is stochastic frontier
analysis (SFA). According to Kumbhakar and Lovell (2000), given the Cobb-Douglas
production function, the model for the SFA is specified as10:
ln yit = ∑ β n ⋅ ln xnit + vit − uit

(3)	
  

where xit is a vector of inputs. vit is the noise component and uit is the nonnegativetechnical inefficiency component. Our objective is to obtain estimate of the technicalefficiency which is the proxy for TFP of a particular firm.
TEit = exp{−ûit }(4)
where it is assumed that ui ~ iid N + (0, σ u2 ) . The other options might include
Normal-Exponential Model, Normal-Gamma Model. We will use them alternatively.
4.2

Data	
 

This paper uses a panel dataset, constructed from the Vietnam Enterprise Survey(hereafter VES) at firm level. The data was collected by the General Statistics Office ofVietnam (hereafter GSO) for all sectors and industries, as on March 1st annually.
Company characteristics such as ownership, labor, capital stock, turnover, assets, FDI
share, average wage rate, intermediate materials are available. Apart from the above,
GSO has taken a census of all multinational enterprises (MNEs), which are defined
firms that have foreign capital, regardless of the share. The advantage is that investment
behavior of these foreign capitalized firms can be captured over time. Census is also
taken for firms with more than 10 employees. Each firm has an exclusive enterprisecode. We use it together with province code to identify the firms.
Another uniqueness of this dataset is that it collects information on firms’
engagement in environmental protection, including the cost spent on environmental
protection, whether the firm carries out environmental management system, whether it
follows the clean manufacturing process, etc. And above all, whether the firm has
ISO14001 certification is recorded. Since it is relatively objective criteria which is freeof measurement error, we use it to create our ISO adoption dummy. Unfortunately, theISO information is only accessible from 2007 to 2009, we have to limit our analysis to
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
9

Though De Loecker (2011) has criticized their methodology for ignoring the omitted price variable bias. Since we don’t have theproduct information, we will not consider his method in this paper.
10
To distinguish from Eq.(1)-(2), we use lowercased letters.	
  

	
  

7

this time period. Finally, the detailed data on waste discharge categorized by form (air,liquid, solid) is complete. It differentiates between treated and untreated amount ofwaste as well, which is important for us to conduct the second stage estimation.
Certainly we have to admit that the survey has its drawbacks. For example, theincomplete information about export and import, missing data for materials and othervariables, inconformity of units among different years, etc. As a result, we have to deal
with an unbalanced panel data here. We remove the missing observations necessary to
calculate TFP, and delete outliers. After these arrangements, the total number ofobservations for estimation is 28274 over three years.
This paper focuses on the ISO14001 adoption by manufacturing firms, since In
VSE dataset, 85% of the firms that adopt ISO14001 are manufacturing firms. The
manufacturing sectors include manufacture of food products industry, manufacture ofbeverages industry, manufacture of tobacco products industry, manufacture of textiles,
manufacture of wearing apparel, manufacture of leather and related products,
manufacture of wood and products of wood and cork, except furniture, manufacture ofpaper and paper products, printing and reproduction of recorded media, manufacture ofcoke and refined petroleum products, manufacture of chemicals and chemical products,
manufacture of pharmaceuticals, medicinal chemical and botanical products,
manufacture of rubber and plastics products, manufacture of other non-metallic mineralproducts, manufacture of basic metals, manufacture of fabricated metal products, except
machinery and equipment, manufacture of other fabricated metal products;
metalworking service activities , manufacture of computer, electronic and opticalproducts,	
   manufacture of electrical equipment, manufacture of machinery andequipment n.e.c, manufacture of motor vehicles; trailers and semitrailers, manufactureof other transport equipment, manufacture of furniture, other manufacturing, and, repair
and installation of machinery and equipment.
Table 1 lists up all the variables used for estimation. In order to take into accountindustrial heterogeneity, we include the categories of manufacturing sectors, as in Table
2. Statistical summaries are shown in Table 3. We use capital labor ratio and the numberof labor (Labor) as a proxy for firm size, turnover as the firms’ economic performance,
firms’ total salary as the firms’ economic performance, TFP as the firms’ economic
performance. We also use ISO14001, Emsystem, Envirstandard, Wastedept,
Cleanemanufacture, and Cost_environ.	
   All data are obtained from VSE dataset.
Turnover, firms’ total salary, and total cost for environmental protection are normalizedby manufacturing gross domestic products (hereafter GDP) deflator. Manufacturing
GDP deflator is obtained from the World Bank.

	
  

8

[Table 1 is inserted here][Table 2 is inserted here][Table 3 is inserted here]
5

Results

5.1 Baseline Results
We employ treatment-effects model to analyze the determinants of ISO 14001
adoption and the effects of ISO 14001 adoption on environmental problems such as air
pollution, water pollution, and land pollution, and firm’s economic performances suchas salary, turnover and productivity. The estimation results of the estimation of baselinemodel are summarized in Table 4. The first panel presents the estimation result of theequation of the determinants of ISO 14001 adoption. Foreign direct investment ratio,
number of labor, and capital labor ratio are positively statistically significant at the 1%
level in the first stage. These results indicate that firms with foreign capital actively
adopt ISO14001. Firm size (Labor) is also the determinant of the adoption of ISO
14001. The positive sign of total labor indicates that the larger a firm is, the more likely
it is to adopt ISO14001. Since the cost of ISO 14001 adoptions is high, larger firms
have more capacity to participate in such voluntary programs. Meanwhile capital-laborratio also plays a positive role, implying that capital-intensive firms prefer ISO14001.	
  Capital-intensive firms exceed labor-intensive firms in technological capacity. This
causes that capital-intensive firms are easier to adopt ISO 14001 than labor-intensive
ones because of low cost of ISO 14001 adoptions due to relative high technologicalcapacity.
Environmental protection variables such as Emsystem, Envirstandard, Wastedept,and Cleanemanufacture are also statistically positively significant at the 1% level in thefirst stage. Firms which carry out environmental management system, firms which meetrequirements of environmental standard, and the firms which apply or carry out theclean manufacturing process are more likely to adopt ISO 14001. That is, firms whichtake measures against environmental issues tend to adopt ISO 14001. However,Cost_environ is not statistically significant. Cost_environ differ in size between firms,
and this might produce the results. VSE dataset has no data of Total Cost during 2006 to
2009, and then, we cannot use “the environmental protecting cost ratio” which is the

	
  

9

total costs of the enterprise for environmental protection divided by total costs. Fromthe point of view of industry sector dummies, a_mnf, c_mnf, d_mnf, and e_mnf arepositively statistically significant at the 1% level. Firms in these industry sectors arelikely to adopt ISO14001.
The second panel of Table 4 presents the estimation results of the effects of
ISO14001 adoptions on pollutions and on firm’s economic performances. Columns (1),(2) and (3) present the effects of ISO14001 adoptions on pollutions. ISO 14001
adoptions are positively significantly at the 1% level in all three column. These results
show that ISO 14001 adoptions increase the share of treated air or water or solid wastes,
and therefore improve air pollution, water pollution and solid pollution. As long as
waste discharge is concerned, ISO14001 adoptions increase the share of treated wastes
in all three forms. The result provides evidence that once firms acquire thisenvironmental certificate, they raise the awareness of environmental protection and try
to control their polluting behavior.
When looking at the industry sector dummies, c_mnf, d_mnf, and e_mnf arestatistically significant at the 1% level in column (1), a_mnf, b_mnf, d_mnf, and e_mnf
are statistically significant at the 1% level in column (2), and a_mnf, b_mnf, and c_mnf
are statistically significant at the 1% level in column (3). c_mnf, and d_mnf sectors
make efforts to reduce air pollution, but e_mnf sector doesn’t. a_mnf sector makes
efforts to reduce water pollution, but b_mnf, d_mnf, and e_mnf sectors do not. Finally,c_mnf, and d_mnf sectors make efforts to reduce solid pollution, but e_mnf sector
doesn’t.
Columns (4), (5) and (6) present the effects of ISO14001 adoptions on firm’s
economic performances. ISO 14001 adoptions are positively significantly at the 1%
level in all three column. These results show that ISO 14001 adoptions also improve
firm’s economic performances. The positive economic impact of ISO14001 on firms’
total salary, turnover and total factor productivity is telling us another story. After firms
adopt ISO14001, the cost of managing the waste is reduced and allows firms to havemore resources (capital and labor) to allocate. As a consequence, it benefits the firms byshifting up their economic performance. Firms’ devotion to the social responsibility canlead to a win-win situation.
[Table 4 is inserted here]
5.2

Robustness CheckTo ensure the robustness of our results, there are several issues that need further
clarification. Since the term of validity for ISO14001 is 3 years, it is likely that firms

	
  

10

might “lose status” during anytime in between 2007-2009. If they fail to renew thecertificate, then our estimation results would be biased when we count these firms as
ISO14001-adopted. In order to alleviate the concern, we limit the samples to the firmsthat did not change the ISO14001 status or newly acquired ISO14001 during 2008-2009.
The results are shown in Table 5. ISO14001 is still positively significant in all kinds ofspecifications, which supports our baseline estimation results. Furthermore, we use
waste
department,
total_cost_environment,
environment_system
andenvironment_standard alternatively as exclusive variables in the first stage, and theresults remain unchanged11. Concerning TFP calculation, Levinsohn and Petrin’ method
is also tried. We get similar and consistent results in all cases12.
[Table 5 is inserted here]
Next, we use propensity score matching (PSM) to confirm our findings. The
purpose of our estimation is to find out the average treatment effect on the treated(ATT), which in this paper is the difference of performance between ISO14001 adoptersand non-adopters. The accurate measurement needs random experimental settings,
however, the counterfactual phenomenon is usually unobserved. Rosenbaum and Rubin(1983) propose the propensity score which can be used in our analysis to match the
adopters with non-adopters. We will use the first stage equation as introduced in Section
4.1 to predict the likelihood a firm adopts ISO14001.
The challenge is that firms didn’t report in which year they acquired ISO14001.
Thus we use the information in year 2007 (the first year of the observation) to calculate
firms’ propensity score of ISO adoption. Then we match them with the firms in 2009
(the last year) that have similar propensity and compare their difference. If theperformance indicators in these two years are significantly different, then we can makethe judgement that ISO14001 has potentially promoted firms to improve. To proceed,
we further assume that by controlling the covariates, the error term will be uncorrelatedwith the decision of firms’ adopting ISO1400113.
Our treatment sample (ISO14001=1) varies from 825 in year 2007 to 1201 in year
2009. The average value of each control variable for the treated is higher than that forthe control. For example, the average TFP for the treated group is 0.56 compared to
0.49 for the control group. The estimation results are consistent with the statistical
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
11

We attempt different estimation models such as ivtreatreg. The results remain unchanged.
To save space, we did not report all the results but they are available upon request.
13
In reality this assumption can be violated. For example, a policy shock in some industry might promote firms to apply for ISO,and an opposite scenario can also be considered.	
  
12

	
  

11

intuition. In Table 6, we report results for both nearest 1-to-1 matching and kernel
matching in each model. ATT estimates are all significantly except for solid waste. This
indicates that by adopting ISO14001, firms’ overall performance will be greatly
improved.
[Table 6 is inserted here]
We conduct the balance tests (for matched) as well. The results of t test for eachcovariate does not reject the null hypothesis that the mean between treated and control
are equal, meaning the models balance the covariates quite well. Another evidence isthat the standardized bias is 4.5% for kernel matching estimator while 1.4% for nearest
1-to-1 matching estimator. Based on the discussion in Caliendo and Kopeining (2008),
standardized bias below 5% is enough to justify the balance.
Despite the strong evidence that ISO14001 improves firms’ competitiveness, andraise their awareness of environmental protection, the estimation might still suffer frombias due to data limitation, as previously explained. More accurate result can beachieved if we have more detailed information on the background of ISO14001
adoption, e.g., why firms in some industries or areas have higher tendency to acquireISO, especially in the context of Vietnam. It also leaves us room for future study onwhether ISO’s impact is temporary or not.
6

Conclusion

We use the firm level data from 2007-2009 in Vietnam to investigate the impact of
the adoption of ISO14001, a voluntary environmental standard. In the empiricalverification, two-stage selection model is applied to correct for the potential selection
bias. The results show that foreign firms are more likely to adopt ISO14001, and theadoption does affect firms’ overall performance, ranging from reduction on waste
discharge to turnover and productivity. We use propensity score matching as therobustness check and obtain consistent results. This finding is in according with most of
the existing literature14. It also provides evidence to refute the critics of “pollution
haven” and makes acknowledgeable foreign firms’ effort towards environmental
protection. In the meantime we are aware of the limitation of this study. With moredetailed information, we would like to extend our analysis to investigation into more
specific industries and regions.
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
14

	
  

Blackman et al. (2010) do not find significant impact of Clean Industry Program on average environmental performance.

12Vietnam is undergoing fast economic transition, however, it is important for
Vietnamese government to realize its current situation of industrial pollution. We hope
this paper can offer decision-makers some clue to explore the real source of pollution
and come up with more efficient ways to protect the environment.
	
  
	
  

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Economics 70: 1-23.
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[19] Per G. Fredriksson, John A. List and Daniel L. Millimet (2003) “Bureaucratic
Corruption, Environmental Policy and Inbound US FDI: Theory and Evidence.” Journalof Public Economics 87: 1407-1430.
[20] Potoski Matthew and Aseem Prakash (2005) “Covenants with Weak Swords:
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Systems.” Journal of Environmental Economics and Management 55(3):281-295.
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decision-making research: Technical report, viewed 23 August 2010, usistf.org.

Table 1: Definitions of variables
Variables
Air

Definition
Share of treated air wastes, treated air waste divided by total air
waste, (%)

LiquidShare of treated water wastes, treated liquid waste divided by totalliquid waste, (%)

Solid

Share of treated solid wastes, treated solid waste divided by total
solid waste, (%)

SalaryReal salary

TurnoverReal Turnover

TFP

Total factor productivity using stochastic frontier method.

ISO14001

Does the enterprise have ISO 14001 certification?
variable.

Emsystem

Does the enterprise carry out environmental management system?
Dummy variable.

Environstandard

Does the enterprise meet requirements of environmental standard?
Dummy variable.

Dummy

Cleanmanufacture Does the enterprise meet requirements of environmental standard?
Dummy variable.
Wastedept

Does the enterprise have an organization or department ofenvironmental protection? Dummy variable.

Cost_environ

Total costs of the enterprise for environmental protection in theyear.

Cap_labCapital labor ratio, (%)

Labor

Total number of Labor

FDI

Foreign direct investment ratio, (%)

	
  

16

Table 2 Categorization of manufacturing sectors.
Variables

Definitiona_mnf

Dummy variable: 1 if firm is manufacture of food products industry,
manufacture of beverages industry, or manufacture of tobacco
products industry; 0 otherwise.

b_mnf

Dummy variable: 1 if firm is manufacture of textiles, manufacture ofwearing apparel, or manufacture of leather and related products; 0
otherwise.

c_mnf

Dummy variable: 1 if firm is manufacture of coke and refined
petroleum products, manufacture of chemicals and chemical products,
manufacture of pharmaceuticals, medicinal chemical and botanicalproducts, or manufacture of rubber and plastics products; 0 otherwise.

d_mnf

Dummy variable: 1 if firm is manufacture of other non-metallic
mineral products, manufacture of basic metals, manufacture offabricated metal products, except machinery and equipment, or
manufacture of other fabricated metal products; metalworking service
activities; 0 otherwise.

e_mnf

Dummy variable: 1 if firm is manufacture of computer, electronic andoptical products, manufacture of electrical equipment, manufacture ofmachinery and equipment n.e.c, manufacture of motor vehicles;
trailers and semitrailers, or manufacture of other transport equipment;
0 otherwise.

f_mnf

Dummy variable: 1 if firm is manufacture of wood and products ofwood and cork except furniture, manufacture of paper and paper
products, printing and reproduction of recorded media, manufacture offurniture, other manufacturing, or repair and installation of machineryand equipment; 0 otherwise.

	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  

	
  

17

Table 3: Summary Statistics
Variables

Obs.

Mean

Std. Dev.

Min

Max

Air

131584

0.809

6.281

0

90

Liquid131584

5.302

16.430

0

100

Solid

1315846.328

16.586099.900

Salary2041682549.993

2549.993

02740906

Turnover2041684110.692 446357.1092

1.079

110604912

TFP

203168

0.487

0.145

0.000

0.850

ISO14001

22672

0.742

0.262

0

1

Emsystem

22696

0.325

0.468

0

1

Environstandard

22708

0.315

0.464

0

1

Cleanmanufacture

22762

0.403

0.491

0

1

Wastedept

22728

0.328

0.328

0

1

Cost_environ

131584139.3998615.973

01867600

Cap_lab

204168101.598

1342.982

0527071.750

Labor

20416879.960464.112

1

64751

FDI

55433

15.08735.241

0

100

	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  

	
  

18

Table 4: Baseline estimation using two-stage treatment model

1st stage

iso14001

FDI

0.00164***(0.000382)

Labor

0.000115***(1.09e-05)

emsystem

0.631***(0.0427)

envirstandard

0.681***(0.0392)

wastedept0.415***(0.0390)

cleanmanufacture

0.116***(0.0402)

totalcost4environ_6.63e-07(5.13e-07)y20.0601(0.0425)y3-0.00803(0.0391)a_mnf

0.257***(0.0569)b_mnf-0.0225(0.0688)c_mnf

0.440***(0.0599)

d_mnf

0.282***(0.0573)

e_mnf

0.665***(0.0626)

cap_lab

0.000116***(2.91e-05)

Constant-2.836***(0.0606)

Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

	
  

	
  

19	
  

Effects of ISO 14001 adoption on environmental and economic performance
(1)

(2)

(3)

(4)

(5)

(6)

nd

air

liquidsolidssalary

turnover

TFP

FDI-0.0199***

0.0747***0.0207***9.150**

266.7**

0.000410***(0.00254)(0.00530)(0.00471)(3.724)(114.3)(2.36e-05)

1.21e-05

0.000368

0.000648***

27.33***

230.1***

8.30e-06***(0.000111)(0.000231)(0.000207)(0.163)(5.004)(1.03e-06)-0.2181.075**

0.419

677.9*

14,4640.00518**(0.251)(0.525)(0.467)(368.5)(11,310)(0.00233)-0.622***-0.431-1.531***685.6**

17,993*0.0124***(0.235)(0.491)(0.436)(344.3)(10,567)(0.00218)

0.357

8.694***-5.917***739.7*

128,380***0.0261***(0.306)(0.640)(0.569)(449.6)(13,795)(0.00284)-0.347-3.298***-2.320***-1,647***-99,605***-0.0395***(0.360)(0.752)(0.669)(528.1)(16,208)(0.00334)

0.662*-0.494-2.274***

1,519***

12,7880.0482***(0.368)(0.769)(0.685)(540.8)(16,592)(0.00342)

1.919***-2.195***-0.442941.4**

41,414***0.00925***(0.294)(0.614)(0.546)(431.2)(13,232)(0.00273)-1.807***-3.623***0.253

1,370**

116,731***0.0267***(0.409)(0.855)(0.762)(601.6)(18,454)(0.00380)

0.00208***0.00186***0.000175

3.774***

255.3***

2.96e-05***(0.000263)(0.000551)(0.000490)(0.387)(11.87)(2.45e-06)

15.07***

39.10***

14.56***

15,834***722,407***0.138***(0.937)(1.919)(1.767)(1,396)(42,532)(0.00873)-7.258***-21.03***-7.789***-6,037***-266,855***-0.0623***(0.524)(1.053)(1.001)(790.7)(23,935)(0.00490)

2.683***

17.39***

28.41***-1,209***-57,790***0.462***(0.259)(0.541)(0.481)(380.1)(11,664)(0.00241)

18,14018,14018,14018,14018,14018,140

2 stage

Labor

y2

y3

a_mnf

b_mnf

c_mnf

d_mnf

e_mnf

cap_labiso14001

lambdaConstant

Observations

Note: "treatreg" model with “twostep” option is applied.
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

	
  

	
  
	
  
	
  
	
  
	
  
	
  

20

	
  

Table 5 After removing observations that have changed ISO status
(1)

(2)

(3)

(4)

(5)

(6)

1 Stage

ISO14001

ISO14001

ISO14001

ISO14001

ISO14001

ISO14001

fdi_capital

0.00180***0.00180***0.00180***0.00180***0.00180***0.00180***(0.000396)(0.000396)(0.000396)(0.000396)(0.000396)(0.000396)

0.000114***0.000114***0.000114***0.000114***0.000114***0.000114***(1.12e-05)(1.12e-05)(1.12e-05)(1.12e-05)(1.12e-05)(1.12e-05)

0.000120***0.000120***0.000120***0.000120***0.000120***0.000120***(2.95e-05)(2.95e-05)(2.95e-05)(2.95e-05)(2.95e-05)(2.95e-05)

0.637***

0.637***

0.637***

0.637***

0.637***

0.637***(0.0448)(0.0448)(0.0448)(0.0448)(0.0448)(0.0448)0.707***

0.707***

0.707***

0.707***

0.707***

0.707***(0.0412)(0.0412)(0.0412)(0.0412)(0.0412)(0.0412)0.413***

0.413***

0.413***

0.413***

0.413***

0.413***(0.0406)(0.0406)(0.0406)(0.0406)(0.0406)(0.0406)

0.0855**0.0855**0.0855**0.0855**0.0855**0.0855**(0.0418)(0.0418)(0.0418)(0.0418)(0.0418)(0.0418)

7.36e-07

7.36e-07

7.36e-07

7.36e-07

7.36e-07

7.36e-07(6.20e-07)(6.20e-07)(6.20e-07)(6.20e-07)(6.20e-07)(6.20e-07)

2nd Stageairliquidsolid

total_salaryturnover

TFP

iso14001

14.84***

39.57***

15.90***

13,836***

647,846***

0.136***(0.979)(2.016)(1.865)(1,190)(38,822)(0.00921)-0.0197***

0.0766***0.0197***10.31***

266.7***

0.000418***(0.00254)(0.00535)(0.00477)(3.038)(99.85)(2.38e-05)

2.75e-05

0.000400*

0.000615***

23.29***

194.0***

8.43e-06***(0.000112)(0.000235)(0.000211)(0.134)(4.413)(1.05e-06)

0.00204***

0.00173***

8.09e-05

3.144***

214.7***

2.91e-05***(0.000263)(0.000554)(0.000494)(0.315)(10.35)(2.46e-06)-7.218***-21.30***-8.403***-5,010***-232,699***-0.0600***(0.543)(1.098)(1.048)(669.1)(21,697)(0.00514)

Time FE

Yes

Yes

Yes

Yes

Yes

Yes

Industry dummy

Yes

Yes

Yes

Yes

Yes

Yes

Observations

17,785

17,785

17,785

17,785

17,785

17,785sttotal_labor

capital_labor ratio

environment_system

enviroment_standard

waste_department

clean_manufacturing

totalcost_environment

fdi_capital

total_labor

capital_labor ratio

lambdaNote: "treatreg" model with “twostep” option is applied.
Standard errors in parentheses.

*** p<0.01, ** p<0.05, * p<0.1

	
  
	
  
	
  
	
  

21

	
  

Table 6

Results using propensity score matching
(1)

(2)

(3)

(4)

(5)

(6)

VARIABLES

air

air

liquidliquid

solid

solid

MethodNearestKernelNearestKernelNearestKernel

ATT

2.398*

2.437**

3.572*

1.574

1.673

-0.326(1.629)(1.364)(2.307)(1.881)(2.129)(1.731)

1,935

1,935

1,935

1,935

1,935

1,935

(7)

(8)

(9)

(10)

(11)

(12)

VARIABLES

total_salary

total_salaryturnoverturnover

TFP

TFP

MethodNearestKernelNearestKernelNearestKernel

ATT12890.702**

4632.676**

197537.429**

164014.116**

0.018**

0.032***(6013.768)(2793.106)(97780.390)(81050.043)(0.009)(0.008)

1,935

1,935

1,935

1,935

1,935

1,935

Observations

Observations

Standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1 (One-tail significance test is conducted.)

	
  
	
  

	
  

22