GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No.234

The Effect of EU Environmental Regulation on
International Trade:
Restriction of Hazardous Substances as a Trade Barrier

Keiichiro Honda

December 2011

GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

The Effect of EU Environmental Regulation on International Trade:
Restriction of Hazardous Substances as a Trade Barrier*
Keiichiro Honda
Graduate School of Economics, Osaka University†
Absract
In 2003, an EU commission established a policy restricting the use of hazardous
substances, called the Restriction of Hazardous Substances (RoHS) directive. In thispaper, the effect of the RoHS directive on international trade in the EU is confirmed.
The RoHS directive restricts the sale of machinery, electrical equipment and electronicequipment that contain hazardous substances in EU. Foreign countries, including otherEU countries must conform to this regulation, potentially leading to decrease in value ofimports to EU countries. I employed the gravity model, using Heckman’s (1979)
procedure, as well as the extended procedure suggested by Helpman, Melitz, andRubinstein (2008) to consider the effects on the persistence of trade and the values oftrade. Estimated results show that the RoHS directive decreased the fraction of existing
trade and the trade values of affected goods in the EU. However, to distinguish between
intraregional trade in the EU and importation from outside the EU, I find that the RoHS
directive as harmonized standard promotes international trade. Additionally, this policy
has the potential to decrease trade value during the transition periods of regulation. The
RoHS directive actually works as a protective policy, acting as trade barrier toexportation to the EU market from outside the EU. In EU countries, there is most likely
going to be decreases in existing trade, but an increased trade value caused by the
harmonized standard.

Keywords
The RoHS directive, harmonized standards, gravity model, intensive and extensive
margin, sample selection and firm heterogeneity

JEL Classification: F13, F18, Q56.
*

I am grateful to Kanemi Ban, Kenzo Abe, Tsunehiro Otsuki, Yoshitaka Kawagoshi and the participants at the 2010
Japan Economic Policy Association Spring Meeting for their useful comments. I am also grateful to the Global COE
program of Osaka University for their financial supports.
†
E-mail address: kgp802hk@mail2.econ.osaka-u.ac.jp

11. Introduction
As import tariffs and other trade policies are reduced or eliminated according tonegotiations within the WTO and the increase in FTAs concluded, the importance ofother policies, such as non-tariff barriers, to trade is escalating. Pro-environmental
policies inadvertently change the structure of trade and hinder international trade. In
2003, an EU commission established restrictions on the use of hazardous substances,
the RoHS directive. The RoHS directive restricts the sale of machinery, electricalequipment, and electronic equipment that contain hazardous substances in the EU. Bothforeign and EU countries must conform to this regulation, so there is the potential for adecrease in the value of imports from foreign countries to EU countries. In this study, I
attempt to confirm the effect of the RoHS directive on international trade in the EU.
In empirical research, the gravity model is widely used to analyze international
trade flows. Moreover, many researchers consider including zero trade flows to obtain
unbiased estimates. In this study, I employ the sample selection model and the extended
procedure suggested by Helpman, Melitz, and Rubinstein (2008, the HMR model), toaddress the effect on “trade persistence” and “trade value”.
The rest of this paper is organized as follows. Section 2 describes the trade flows
of machinery, electrical equipment, and electronic equipment as well as environmental
regulation in the EU, and it reviews some previous studies of environmental standards.
Section 3 describes the gravity model for international trade and considers theobservation of zero trade flows. Section 4 describes the estimation procedures of thisanalysis and dataset. Section 5 presents and discusses the estimation results. Section 6summarizes the empirical results of this study.

2. Conformation of Trade, Policy, and Previous Studies
2.1. Trade Flows and Environmental Regulation in the EU
Figure 1 plots the imports of machinery, electrical equipment, and electronic equipmentin the EU. The bars represent the aggregate trade value of these goods, while the solidline indicates the percent of country pairs and goods that actually trade. The trade value
obviously increases from 2004 to 2007, before the global recession in 2008. Conversely,
fraction of the existing trade of machinery, electrical equipment, and electronicequipment rises until 2003 and then drops sharply in 2004. As is evident from the2

Figure 1. EU imports of machinery, electrical equipment, and electronic equipment
0.3
0.295

Billions of Euro

400

0.29
0.285

350

0.28
0.275

300

0.27
0.265

250

0.26
0.255

200

0.25
2010q3
2010q1
2009q3
2009q1
2008q3
2008q1
2007q3
2007q1
2006q3
2006q1
2005q3
2005q1
2004q3
2004q1
2003q3
2003q1
2002q3
2002q1
2001q3
2001q1
2000q3
2000q1

Trade value

Percentage of country pairs and goods

450

Percent of existing trade

Note: Constructed from data on HS84-HS92 commodities imported into EU27 countries from 199 export
countries.

figure, the trade value and the fraction of existing trade do not exhibit the same behavior.
Policies including environmental regulations have different impacts on these twoquantities; these impacts should thus each be analyzed. Moreover, the fraction ofexisting trade is lower than 0.3 after 2000. Therefore, in an empirical analysis, the zero
trade flows that dominate a large fraction of the country pairs and goods should beconsidered.
In figure 2, total trade value of machinery, electrical equipment, and electronicequipment imports as well as the share of these goods in total imports is shown. The EU
total import value increases as does the values of machinery, electrical equipment, andelectronic equipment, but more steeply. Reflecting this situation, the share of thesegoods declines.
The declines in the fraction of existing trade after 2004 and the share ofmachinery, machinery, electrical equipment, and electronic equipment are thought to beaftereffects of strengthening the environmental regulations in the EU. In recent years,
the EU has attached great importance to environmental problems, in particular, waste
reduction. The collection and recycling of container and packaging waste were defined
3

1200

0.44

1000

0.42
0.4

800

0.38

600
0.36

400

Share

Billions of Euro

Figure 2. Share of machinery, electrical equipment, and electronic equipment in the EU

0.34

200

0.32

0

0.3

2010q3
2010q1
2009q3
2009q1
2008q3
2008q1
2007q3
2007q1
2006q3
2006q1
2005q3
2005q1
2004q3
2004q1
2003q3
2003q1
2002q3
2002q1
2001q3
2001q1
2000q3
2000q1

Trade value of EU total imports

Trade value

Share

Note: Constructed from data on EU27 import countries and 199 export countries

and made into law in each country. Similarly, recycling vehicle waste is also mandated
by the End-of-Life Vehicles (ELV) directive. The most stringent regulation is the RoHS
directive, which restricts the sale of goods that contain hazardous substances in the EU.1
This directive also requires that waste from machinery, electrical equipment, andelectronic equipment be recycled. This directive is also valid for goods imported in theEU; in other words, exports from foreign countries to the EU may face theenvironmental regulations as a trade barrier. The RoHS directive was established on
February 13, 2003, and enforcement began on July 1, 2006. Deceleration of theincreasing trade of machinery, electrical equipment, and electronic equipment relative tototal imports and enforcement of this policy ware observed to occur simultaneously.
While there are many other causes, it is conceivable that the RoHS directive hindered
the import of machinery, electrical equipment, and electronic equipment in the EU.

Specifically, lead, cadmium, mercury, hexavalent chromium, polybrominated biphenyl, andpolybrominated biphenyl ether are subjects.
142.2. Previous Studies on Standards and Regulations
There has been considerable research on the relationship between standards andregulations, including the environmental regulation and international trade. In suchresearch, the important factor is whether the focused standards and regulations areharmonized among trading partners. Theoretical predictions suggest that if the standardis harmonized between exporter and importer, this policy would promote international
trade. Conversely, if the standard is country-specific, it would inhibit international trade.
Some of the research attempted to confirm this theoretical prediction. Moenius (2004)
found that the country-specific standards inhibit international trade in the
non-manufacturing sector but promote it in the manufacturing sector. Fontagne et al.
(2005) found mixed evidence across industries. Swann et al. (1996) focused the impact
of standards on trade performance in the UK context. They found that, althoughinternationally equivalent (harmonized) standards promote international trade,
idiosyncratic UK standards promote exports but interrupt imports.
These previous studies intensely focus on the collective effect of standards andregulation. Although some related studies exist, there is no research that addresses thedetailed effects of a specific policy, in particular, the effect of the RoHS directive oninternational trade in the EU. Thus, this study contributes a confirmation of the detailed
effect of a specific policy.

3. The Gravity Model and Zero Trade Flows
In this analysis, I employ the gravity model suggested by Tinbergen (1969), which is theempirical specification used to explain trade flows and FDI flows. In the basic model,
the dependent variable is the bilateral trade flows, and independent variables are theeconomic scales of each country and the distance between exporter and importer. The
gravity model has the advantage that the estimation results tend to have a highgoodness-of-fit. However, this model is criticized for its lack of theoretical background.
Thus, Anderson and van Wincoop (2003) suggested the modified gravity equation,
obtained from the monopolistic competition model of Dixit and Stigliz (1977).
With respect to international trade flows, much recent research attempts toconsider many trade observations that contain zero trade values. For example, Disdier,
Fontagne, and Mimouni (2008) estimated the gravity equation with log(1 + trade) as the5dependent variable.2 Instead of adjusting the trade flow, Eaton and Tamura (2004)
recommended adopting the Tobit model. Silva and Tenreyro (2006, 2010, 2011)
suggested Poisson Pseudo Maximum Likelihood estimator for estimating the gravity
equation. This method is used to obtain the consistent estimates in the log-linear formand can consider the zero values of a dependent variable to assume a Poisson
distribution.
In this study, as already noted in figures 1 and 2, I employ the sample selection
model and the extended procedure suggested by HMR.

3.1. Sample Selection
The sample selection model has two types of estimation methods: the maximum
likelihood method and the OLS method with an inverse Mills ratio used by Heckman(1979). The gravity model with a two-step estimation procedure measures a different
effect during each stage. The effect on persistence of trade is measured in the first stage
estimation, and the effect on values of trade is measured in the second stage estimation.
The former corresponds to an extensive margin of trade, and the latter an intensive
margin. With this procedure, Czubala, Shepherd, and Wilson (2009), for example, use
this procedure to measure the impact of harmonized standards on African exports.

3.2. Correction of Sample Selection and Firm Heterogeneity
The two-stage sample selection model can estimate the impact of every variable on the
trade flow. However, HMR observes that firms vary in productivity and that if the firm
heterogeneity correlates with the decision making of the firm’s export behavior, it is not
sufficient to correct only the sample selection bias. Moreover, they suggest theappropriate estimation procedure for that case. I employ not only the sample selection
model but also their procedure to estimate the gravity model with an environmental
regulation as a trade barrier.
The first stage estimation equation of the HMR procedure is same as the firstone of sample selection. However, in the second stage estimation, the consistentestimator is obtained by adding firm heterogeneity correction terms and a sample

2

See also Kellenberg (2009). He regressed the Heckscher-Ohlin model with log(1 + trade).

6selection correction term. The firm heterogeneity correction term is a monotonic
function of the predicted value of first stage probit. HMR found no noticeable changes
when expanding beyond a cubic polynomial. Therefore, I use the cubic polynomial formin the predicted value of first stage probit in place of the firm heterogeneity correction
term.
To estimate the effect of the environmental regulation, specifically the RoHS
directive, on the presence of trade and trade value, I apply the standard Heckman’s
correction and the HMR correction for sample selection and firm heterogeneity toestimate the gravity model.

4. Estimation Procedures and Data
As described above, I employ two estimation methods, Heckman’s sample selection
model and HMR’s sample selection and firm heterogeneity correction model. Bothmethods require the same first stage probit selection equation:
𝜌𝑖𝑗𝑘 = Pr(𝑇𝑖𝑗𝑘 = 1|observed variables)′′= Φ(𝛼0 + 𝛼𝐸𝑋 + 𝛼𝐼𝑀 + 𝛼𝐻𝑆 + 𝛼𝑄𝑇 + 𝐱 𝑖𝑗𝑘
𝛂 + 𝐱 2𝑖𝑗𝑘
𝛂𝟐 )

(1)

where 𝜌𝑖𝑗𝑘 is the probability that country 𝑗 exports goods 𝑘 to country 𝑖, conditionalon the observed variables, and where the indicator variable 𝑇𝑖𝑗𝑘 is equal to 1 whencountry 𝑗 exports goods 𝑘 to country 𝑖 and 0 otherwise. This equation includes the
fixed effects of exporter, importer, goods, and quarterly time: 𝛼𝐸𝑋 , 𝛼𝐼𝑀 , 𝛼𝐻𝑆 and 𝛼𝑄𝑇respectively. The variables 𝐱 and 𝐱 𝟐 are the vector of explanatory variables; note that𝐱 𝟐 is only included in the first stage probit. The parameters 𝛂 and 𝛂𝟐 are vectors ofcoefficient of explanatory variables. The estimated results from equation (1) imply an
extensive margin of each variable.
The second stage regression equation using Heckman’s (1979) estimator, theHeckit estimator, is as follows:′
𝑚𝑖𝑗𝑘 = 𝛽0 + 𝛽𝐸𝑋 + 𝛽𝐼𝑀 + 𝛽𝐻𝑆 + 𝛽𝑄𝑇 + 𝐱 𝑖𝑗𝑘
𝛃 + 𝛽𝜆 𝜆̂ + 𝜀𝑖𝑗𝑘

(2)where 𝑚𝑖𝑗𝑘 is the log of import value. The parameters 𝛽𝐸𝑋 , 𝛽𝐼𝑀 , 𝛽𝐻𝑆 and 𝛽𝑄𝑇 are
the fixed effects of exporter, importer, goods, and quarterly time, respectively. The
parameter 𝜆 is the inverse Mills ratio from the first stage probit, and 𝛽𝜆 is its
7parameter. The error term 𝜀𝑖𝑗𝑘 is i.i.d. satisfying E(𝜀𝑖𝑗𝑘 |𝑇𝑖𝑗𝑘 = 1) = 0.
Alternatively, the HMR estimator is as follows:′
𝑚𝑖𝑗𝑘 = 𝛽0 + 𝛽𝐸𝑋 + 𝛽𝐼𝑀 + 𝛽𝐻𝑆 + 𝛽𝑄𝑇 + 𝐱 𝑖𝑗𝑘
𝛃
2

3

∗
∗
∗
∗
+ 𝛿1 𝑧̂𝑖𝑗𝑘
+ 𝛿2 (𝑧̂𝑖𝑗𝑘
) + 𝛿3 (𝑧̂𝑖𝑗𝑘
) + 𝛽𝜂 𝜂̂ 𝑖𝑗𝑘
+ 𝑒𝑖𝑗

2

(3)

3

∗
∗
∗
where 𝑧̂𝑖𝑗𝑘
, (𝑧̂𝑖𝑗𝑘) and (𝑧̂𝑖𝑗𝑘
) are the firm heterogeneity correction terms in cubic∗∗∗∗
polynomial form, 𝑧̂𝑖𝑗𝑘≡ Φ−1 (𝜌̂𝑖𝑗𝑘 ) . The variable 𝜂̂ 𝑖𝑗𝑘= 𝜙(𝑧̂𝑖𝑗𝑘)⁄Φ(𝑧̂𝑖𝑗𝑘
) is theinverse Mills ratio, and 𝛽𝜂 is the parameter of the inverse Mills ratio. The error term

𝑒𝑖𝑗𝑘 is i.i.d. satisfying E(𝑒𝑖𝑗𝑘 |𝑇𝑖𝑗𝑘 = 1) = 0.3 The estimated results of equations (2)and (3) show an intensive margin.
Because the RoHS directive covers the subset of goods taken to the EU market,
this study considers only the imports of EU countries and the manufacturing industries
HS 2-digit commodities, 84-85 (machinery and electrical), 86-89(transportation) and
90-92(miscellaneous related to machinery excluding arms). Furthermore, the estimation
periods from the first quarter of 2000 to the fourth quarter of 2010 are used to forcompare the before and after of this policy. For the convenience of data availability, theimporters are 19 countries and the exporters are 54 countries, including the EU.4
The dataset used in this study includes the bilateral trade value, the basic variables of
the gravity equation, other control variables, and information about the RoHS directive.
The bilateral trade value is obtained from Eurostat. Real GDP is obtained fromInternational Financial Statistics. The distance between exporter and importer, language,
border, colonial relationship and landlocked status are obtained from CEPII. For
estimation, the bilateral trade value, GDP, and distance data are used in logarithmic
form. The common language dummy variable is equal to 1 if both the exporter andimporter countries use a common language and 0 otherwise. The common borderdummy variable is equal to 1 if the exporter and importer have a common border and 0
otherwise. The colonial relationship dummy variable is equal to 1 if the exporter andimporter have a previous colonial relationship and 0 otherwise. The landlocked dummy
variable is equal to 1 if both the exporter and importer lack a coastline or direct sea
access and 0 otherwise.
Both methods need to identify the first stage probit selection equation (1). These3
4

For more details on how this estimation equation was obtained, see Appendix II.
For more details concerning the considered countries, see Appendix I.

8Table 1. Descriptive Statistics
Variabletradeln(trade)
Tijk
GDPexpln(GDPexp)
GDPimpln(GDPimp)
distanceln(distance)
EU dummy
common language
common bordercolonial relationshiplandlockcost
proc&days
RoHS dummy
RoHS transition period

Unitmillion in Euro

million in Euro
million in Euro
km

Obs.
Mean
Std. Dev.
Min
Max
40629632.548170.8370.000
5720.330
276092-0.4793.849 -13.8155
8.652
406296
0.680
0.467
0
1
361791 416262.200 1592002.000 360.009 13096258.514
361791
10.8931.808
5.886
16.388381024 148969.000 171530.200 4161.994
607043.900
38102411.235
1.202
8.334
13.3164062965827.042
4563.115 141.446
19537.120
406296
8.210
1.085
4.952
9.880
406296
0.256
0.436
0
1
406296
0.055
0.227
0
1
406296
0.049
0.215
0
1
406296
0.076
0.265
0
1
406296
0.012
0.108
0
1
406296
0.146
0.353
0
1
406296
0.171
0.376
0
1
406296
0.227
0.419
0
1
406296
0.177
0.381
0
1

variables affect fixed trade costs but do not affect variable trade costs. I use thecountry-level data on the regulation costs of firm entry, collected by Djankov et al.
(2002) according to the procedure in HMR. Specifically, I use the number of days, thenumber of legal procedures, and the relative cost as a percentage of GDP per capita foran entrepreneur to begin operation. In a manner similar to that of HMR, I use these datato construct two indicator variables for a high fixed cost, consisting of country pairs inwhich both the importing and exporting countries have entry regulation measures above
the cross-country median. One of the regulation cost variables is constructed from thesum of the number of days and procedures, and the other is constructed from sum of therelative costs.
The RoHS directive is represented as an indicator variable. According to thepolicy schedule, the RoHS dummy variable is equal to 1 when it correspond to HS84,
85, 90, 91, or 92, which include the target goods of the RoHS directive in the third
quarter of 2006 and after, and 0 otherwise.5 In addition, I construct an indicator variable
The goods represented by each code are as follows:
HS84: Nuclear reactors, boilers, machinery and mechanical appliances, and computers
HS85: Electrical machinery, equipment and parts, telecommunications equipment, sound recorders,
and television recorders
HS90: Optical, photographic, cinematographic, measuring, cheking, precision and medical or surgicalinstruments and accessories
59

to represent the transition period between the establishment and the enforcement of theregulation to examine the firm behavior in these periods. The RoHS transition period
dummy variable is equal to 1 when the goods are targets of the RoHS directive from thefirst quarter of 2003 to the second quarter of 2006 and 0 otherwise.
Additionally, I use an EU dummy variable that is equal to 1 if both the exporterand importer countries are members of the EU and 0 otherwise. The descriptive
statistics used in this study are shown in Table 1.

5. Estimation Results
5.1. Baseline Results
Table 2 shows the baseline estimated results. As a benchmark, I show the estimation
results of equation (1) without entering for the identification variables and the basic
gravity model using OLS, separately. In the first stage probit, all variables are
significant at a 1% level. As we can imagine, the distance between exporter andimporter has a negative effect on the probability of persistence of trade, whereas acommon language, common border, colonial relationship, and whether the country islandlocked have positive effects. If both countries become members of the EU, thebilateral trade between these countries disappears. The RoHS dummy variable has anegative effect, implying that the probability of persistence of trade of machinery,electrical equipment and electronic equipment decline in response to the RoHS directive.
Estimation of the traditional gravity equation using OLS uses data on country pairs that
trade in at least one direction for each good. Note that the OLS estimator does notcontain zero trade values and is econometrically problematic. The GDPs of the exporterand importer have a positive effect and are statistically significant. Distance, common
language, common border, colonial relationship, and landlocked status have the same
effect in direction as in the first stage probit. Unlike the results of the first stage, thecoefficient of joining the EU is positive and statistically significant. In addition, theRoHS dummy variable is significantly negative in the first stage. These results of thebenchmark estimation show that the RoHS directive decreases the fraction of existing
the trade and trade values of the intended goods.
Next, the Heckit estimator of the second stage estimation, equation (2), is shown
HS91: Clocks, watches, and parts
HS92: Musical instruments, parts, and accessories

10Table 2. Baseline results
ProbitRoHS-0.00632***(0.00216)
GDPexp-0.00746***(0.00280)
GDPimp-0.0196***(0.00569)
distance-0.0852***(0.00227)
EU dummy-0.0119***(0.00271)
common language 0.0284***(0.00271)
common border
0.0650***(0.00218)
colonial relationship 0.0388***(0.00202)
landlock0.0402***(0.00483)
cost
proc&days

1st stage

2nd stage
Heckit
HMR-0.0684*** -0.0836*** -0.0912***(0.0185)(0.0184)(0.0210)0.0568*0.0493*0.0400(0.0297)(0.0292)(0.0314)0.664***
0.659***
0.629***(0.0505)(0.0503)(0.0593)-0.855*** -0.907*** -0.986***(0.0143)(0.0144)(0.143)0.373***
0.337***
0.353***(0.0188)(0.0188)(0.0267)
0.358***
0.362***
0.402***(0.0223)(0.0225)(0.0602)0.244***
0.224***
0.523***(0.0203)(0.0206)(0.173)0.510***
0.581***
0.609***(0.0203)(0.0203)(0.0825)
0.194***
0.277***
0.174*(0.0428)(0.0422)(0.0952)
OLS-0.00634***(0.00216)-0.00740***(0.00280)-0.0196***(0.00569)-0.0851***(0.00226)-0.0120***(0.00271)
0.0292***(0.00271)
0.0651***(0.00217)
0.0386***(0.00203)
0.0396***(0.00490)-0.00866***(0.00245)
0.00198(0.00221)

inverse mills ratio

0.966***(0.0223)

𝜂̂ ∗

𝑧̂ ∗
𝑧̂ ∗ 2𝑧̂ ∗ 3
constantObs.
R-squared

339,0390.581

0.00166(0.836)239,3220.735

339,039
0.581

0.537
(0.829)239,3220.738

3.048***(0.250)2.932***(0.259)-0.641***(0.0107)0.0371***(0.000795)
4.711(3.799)239,3220.750

Note: The marginal effect of sample means and pseudo 𝑅 2 reported for probit and first stage. The robust standard
errors are in parentheses. *, **, and *** denote significance at 10%, 5%, and 1%, respectively.

11

in Table 2. For the two-step sample selection procedure, two fixed cost variables areadded in the first stage probit. Only relative cost has a significant effect at 1%, implyingthat a fixed entry cost decreases the probability of persistence of trade. Based on thesefirst stage results, the second stage result using the Heckit estimator is obtained with theinverse Mills ratio. The coefficient of the inverse Mills ratio is statistically significant at
a 1% level, implying that the Heckit estimator is better than the OLS because acorrelation exists between the error terms in the first and second stage. The RoHS
dummy variable continues to have a significantly negative effect, but the magnitude ofthe estimated parameter is approximately 1.22 times that of the OLS.
Alternatively, the HMR estimator is obtained using the same first stage∗
estimated results. The correction term for the sample selection, 𝜂̂ 𝑖𝑗𝑘, and firm
2

3

∗
∗
∗
heterogeneity correction terms 𝑧̂𝑖𝑗𝑘
, (𝑧̂𝑖𝑗𝑘) and (𝑧̂𝑖𝑗𝑘
) are statistically significant at

a 1% level, implying that both firm heterogeneity and sample selection require
correction. The coefficient of the RoHS dummy variable is -0.0912, and statisticallysignificant. This value is larger than the results using the OLS and Heckit estimator andis approximately 1.33 times that of the OLS.
These results show that the RoHS directive decreases the fraction of existing
trade and the trade values of intended goods. Additionally, in an appropriate approach,the magnitude of the extensive margin caused by the RoHS directive is 0.6%, and thatof the intensive margin is 0.0912%.

5.2. Intraregional trade in the EU versus import from outside the EU
Next, to consider the effect of whether the same regulation exists for both the exporterand importer, I split the sample used in the estimation. One subsample includes only EU
countries as exporter (i.e., intraregional trade in the EU), and the other subsample
includes only imports from outside the EU.
In EU intraregional trade, the effect of the RoHS dummy variable in the firststage probit is significantly negative, as it is in the baseline results. However, thecoefficients of RoHS using the Heckit estimator and the HMR estimator are
significantly positive at a 1% level. According to the significance of each correction
term, the result of the HMR estimator, 0.143, is more appropriate and larger than that of
the Heckit estimator (approximately 1.82 times). However, for an importer from outside
the EU, the coefficients of the RoHS dummy variable using both methods are12

Table 3. Estimated results using subsample
Intraregional trade in EU
2nd stage
1st stage
Heckit
HMR
RoHS-0.000219**
0.0785*** 0.143***(9.85e-05)(0.0223)(0.0221)GDPexp-0.000269
0.111
0.165**(0.000218)(0.0690)(0.0681)
GDPimp-0.000231
0.638*** 0.682***(0.000217)(0.0634)(0.0623)
distance-0.00214***-1.112*** -0.578***(0.000321)(0.0156)(0.0266)
EU dummy-0.000211***0.510*** 0.570***(6.87e-05)(0.0210)(0.0207)
common language -0.000823*
0.116*** 0.232***(0.000421)(0.0290)(0.0288)
common border 0.000829***0.0573*** -0.219***(0.000137)(0.0214)(0.0234)
colonial relationship 0.000730***0.370***
0.0414(0.000119)(0.0252)(0.0285)
landlock
2.14e-05-0.0900*-0.118**(0.000198)(0.0469)(0.0460)
cost-0.000176(0.000117)
proc&days
0.000496***(9.03e-05)
inverse mills ratio-0.491***(0.0388)𝜂̂ ∗0.647***(0.0670)∗𝑧̂
2.248***(0.0766)𝑧̂ ∗ 2-0.254***(0.0134)∗3𝑧̂
0.0114***(0.000887)
constant-0.523-9.336***(1.115)(1.129)Obs.
118,728
105,040
105,040
R-squared
0.541
0.797
0.805

Import from outside of EU
2nd stage
1st stage
Heckit
HMR-0.0107**-0.216*** -0.338***(0.00510)(0.0271) (0.0325)-0.0122*-0.131*** -0.260***(0.00638)(0.0347) (0.0402)-0.0505***
0.553*** -0.00436(0.0140)(0.0715)(0.112)-0.185***-1.307*** -3.279***(0.00723)(0.0353)(0.323)0.0786***(0.00682)
0.190***(0.0103)
0.0838***(0.00591)
0.111***(0.0148)-0.00653(0.00566)-0.0127**(0.00556)

0.292***(0.0319)0.618***(0.0596)0.590***(0.0306)
0.334***(0.0749)

1.246***(0.148)4.152***(0.506)1.570***(0.162)
1.665***(0.242)

1.467***(0.0275)

220,3110.564

6.742***(0.537)0.609(0.540)-0.925***(0.0184)
0.0598***(0.00147)-8.695*** 31.50***(0.743)(4.896)134,282
134,282
0.694
0.707

Note: The marginal effect of sample means and pseudo 𝑅2 reported for the first stage. The robust standard errors arein parentheses. *, **, and *** denote significance at 10%, 5%, and 1%, respectively.

13significantly negative at a 1% level, as they are in the baseline results. Although one of∗correction terms, 𝑧̂𝑖𝑗𝑘
, is not significant, the HMR estimator is more appropriate thanthe Heckit estimator; the estimate value is -0.338.
These results imply that the intensive effects of the RoHS directive are different
between for intraregional trade in the EU and for imports from outside the EU. In EU
intraregional, all EU countries conform to the same RoHS directive, and exporting firms
in EU countries must pay the additional cost induced by the RoHS directive but can
avoid the additional cost of compliance of an importer country. Conversely, exporting
firms outside the EU must pay the additional cost for compliance in the EU. Thus, thisincreased cost of production caused by the RoHS directive can potentially cause firms
to exit the exporting market regardless of harmonization. However, an exporter
regulated by harmonized standards has an advantage over an exporter regulated by
non-harmonized standards and can increase the export value.

5.3. Effect during Transitional Period
I also consider the transition periods between when the restrictive policy is establishedand when it is first enforced. In these periods, some firms attempt to adapt the products
to forward new regulations without being regulated at that time. These firms may incur
the additional cost associated with both identifying alternative inputs as substitutes for
restricted materials and purchasing such inputs, which are expected to be more
expensive than restricted materials. Thus, the probability and value of trade have thepotential to decrease because of the predicted restrictive policy.
In Table 3, I repeat the estimation with the RoHS transition period dummy
variable. With the full sample, the RoHS directive has a significantly negative effect on
the extensive and intensive margins. All of the correction terms are significant at a 1%
level; the results of the HMR estimator remain more appropriate than those of the
Heckit estimator. The transition period dummy variable considered in this section also
has a significantly negative effect on the probability of the persistence of trade and tradevalues. Using the same subsample as in section 5.2, the RoHS dummy variable differs
little from the results in section 5.2 in sign and magnitude. However, in EU intraregional
trade, the transition period dummy variable has a significantly negative effect with the
Heckit estimator but an insignificantly negative effect with the HMR estimator.
Although we cannot reject the possibility of no effect, the RoHS directive would likely
decrease the trade value in the transition period.
14

Table 3. The effect during the transition period
Full sample
2nd stage
1st stage
Heckit
HMR
RoHS-0.0125*** -0.149*** -0.168***(0.00276)(0.0229)(0.0305)trans RoHS
-0.0106*** -0.116*** -0.135***(0.00284)(0.0242)(0.0293)
inverse mills ratio
1.060***(0.0258)𝜂̂ ∗3.047***(0.250)∗𝑧̂
2.931***(0.260)𝑧̂ ∗ 2-0.641***(0.0107)∗3𝑧̂
0.0371***(0.000795)
Obs.
339,039
239,322
239,322
R-squared
0.581
0.738
0.750

Intraregional trade in EU
2nd stage
1st stage
HeckitHMR-0.000371***0.03700.135***(0.000136)(0.0277)(0.0275)-0.000246*-0.0747***-0.0149(0.000131)(0.0289)(0.0286)-0.418***(0.0449)
0.649***(0.0670)
2.251***(0.0766)-0.255***(0.0134)
0.0114***(0.000887)
118,728
105,040
105,040
0.541
0.797
0.805

Import from outside of EU
2nd stage
1st stage
Heckit
HMR-0.0241*** -0.314*** -0.588***(0.00642)(0.0340) (0.0534)-0.0234*** -0.170*** -0.434***(0.00662)(0.0359) (0.0533)
1.523***(0.0318)
6.758***(0.538)0.592(0.541)-0.925***(0.0184)
0.0599***(0.00147)
220,311
134,282
134,282
0.564
0.694
0.707

Note: The marginal effect of sample means and pseudo 𝑅2 reported for the first stage. The robust standard errors are in parentheses. *, **, and *** denote significance at 10%, 5%,
and 1%, respectively.

15

These results imply that exporting firms paid an additional cost for compliance
before the RoHS directive was first enforced. After this enforcement, the exporter
regulated harmonized standards has the potential to increase trade values. However,they also have potential to decrease trade value because exporters may seek alternative
inputs and pay for other expensive materials during the transition period of regulation.

6. Conclusion
This study estimates the effect of the RoHS directive that restricts the sale of machinery,electrical equipment, and electronic equipment that contain hazardous substances oninternational trade in the EU. In empirical research, the gravity model is widely used toanalyze international trade flows. In this study, I employ the sample selection model andthe extended procedure suggested by HMR to consider the effect on persistence of tradeand values of trade. Estimated results show that the RoHS directive decreases thefraction of existing trade and trade values of intended goods in the EU. However, todistinguish between intraregional trade in the EU and imports from outside the EU, I
find that the RoHS directive as a harmonized standard promotes international trade.
Additionally, this policy has the potential to decrease trade value during the transition
period of regulation. The RoHS directive is inadvertently a protective policy, that is, a
trade barrier to exporting to the EU market from outside the EU. In EU countries, theprobability of existing trade decreases but trade value increases because of theharmonized standard. This effect represents the potential of specialized and divisional
production.

16

References
Anderson, J. E. and E. van Wincoop (2003) “Gravity with Gravitas: A Solution to theBorder Puzzle,” American Economic Review 93, pp.170-192.
Czubala, W., B. Shepherd, and J. S. Wilson (2009) “Help of Hindrance? The Impact ofHarmonised Standards on African Exports,” Journal of African Economics 18,
pp.711-744.
Disdier, A. C., L. Fontagne, and M. Mimouni (2008) “The Impact of Regulations on
Agricultural Trade: Evidence from the SPS and TBT Agreements,” American
Journal of Agricultural Economics 90, pp.336-350.
Dixit and Stiglitz (1977) “Monopolistic Competition and Optimum Product Diversity,”
American Economic Review 67, pp.297-308.
Djankov, S., R. La Porta, F. Lopez-de-Silanes, and Andrei Shleifer (2002) “TheRegulation of Entry,” Quarterly Journal of Economics 117, pp.1-37.
Fontagne, L., M. Mimouni, and J-M. Pasteels (2005) “Estimating the Impact ofEnvironmental SPS and TBT on International Trade,” Integration and Trade
Journal 22, pp.7-37.
Heckman, J. J. (1979) “Sample Selection Bias as a Specification Error,” Econometrica
47, pp.153-161.
Helpman, E., M. J. Melitz, and Y. Rubinstein (2008) “Estimating Trade Flows: Trading
partners and Trading Volumes,” Quarterly Journal of Economics 123,
pp.441-487.
Kellenberg, D. (2009) “An Empirical Investigation of the Pollution Haven Effect with
Strategic Environment and Trade Policy,” Journal of International Economics
78, pp.242-255.
Melitz, M. J. (2003) “The Impact of Trade on Intra-industry Reallocations andAggregate Industry Productivity,” Econometrica 71, pp.1695-1725.
Moenius, J. (2004) “Information Versus Product Adaptation: The Role of Standards inTrade,” International Business and Markets Research Center Working Paper,Northwestern University.
Silva, J. M. C. S. and S. Tenreyro (2006) “The Log of Gravity,” Review of Economics
and Statistics 88, pp.641-658.
Silva, J. M. C. S. and S. Tenreyro (2010) “On the Existence of the Maximum
Likelihood Estimates in Poisson Regression,” Economics Letters 107,
pp.310-312.
Silva, J. M. C. S. and S. Tenreyro (2011) “Further Simulation Evidence on the17Performance of the Poisson Pseudo-Maximum Likelihood Estimator,”
Economics Letters 112, pp.220-222.
Swann, P., P. Temple, and M. Shurmer (1996) “Standards and Trade Performance: the
UK Experience,” Economic Journal 106, pp.1297-1313.
Tinbergen, J. (1962) Shaping the World Economy, New York: Twentieth Century Fund.

18

Appendix I
List of countries is shown in Table A1.
Table A1. Countries in the dataset

ExporterArgentina
Australia
Austria
Belgium
Bolivia
BrazilBulgaria
Cambodia
CanadaChile
China
Colombia
Costa Rica
Czech Republic
Denmark
EcuadorEgypt
El Salvador
FinlandFranceGermany
GreeceGuatemalaHong Kong SAR, China
Hungary
India
Indonesia
Iran, Islamic Rep.

IrelandIsraelItaly
JamaicaJapan
JordanKorea, Rep.
Malaysia
MexicoMoroccoNetherlands
New Zealand
NorwayPeru
Philippines
PolandPotrugal
Romania
Singapore
Spain
SwedenSwitzerland
ThailandTunisia
TurkeyUnited Kingdom
United States

19

Importer
Austria
Belgium
Bulgaria
Czech Republic
Denmark
FinlandFranceGermany
GreeceHungary
Ireland
Italy
Netherlands
PolandPotrugal
Romania
Spain
SwedenUnited Kingdom

Appendix II
I explain the theoretical model of Helpman, Melitz, and Rubinstein (2008) to obtain theestimation equation.
Consider that country 𝑗’s consumer has constant elasticity of substitution (CES)
preference for differentiated varieties of product. Under budget constraint, standard
demand function is obtained; depend on a given price index and income. Firm incountry 𝑗 produces one unit of output with a cost-minimizing combination of inputs
that cost 𝑐𝑗 𝑎, where 𝑐𝑗 reflect differences across countries in factor prices and this iscountry specific, whereas 𝑎 is firm specific amount of inputs, reflecting productivity
differences across firms in the same country. The inverse of 𝑎 represents the firm’s
productivity level. HMR assume that a cumulative distribution function 𝐺(𝑎) withsupport [𝑎𝐿 , 𝑎𝐻 ] describes the distribution of a across firms, where 𝑎𝐻 > 𝑎𝐿 > 0,
based on Melitz (2003).6 This distribution function is the same across all countries.
Country 𝑗’s producer have two additional cost to seek to sell its product in country 𝑖: a
fixed cost of entering the market in country 𝑖, which equals to 𝑐𝑗 𝑓𝑖𝑗 , and a transportcost. As is customary, they adopt the “iceberg” specification that 𝜏𝑖𝑗 units of a producthave to be shipped for one unit to arrive. A cost of entry is needed only if firm try to go
into foreign market, i.e. 𝑓𝑗𝑗 = 0 for all 𝑗. And, assume that there is no transport cost in
home country, i.e. 𝜏𝑗𝑗 = 1 for all 𝑗.
There is monopolistic competition in final products. Every producer set price of
product with profit maximization. Using demand function and profit maximizing price,
the operating profit of firm in country 𝑗 selling to country 𝑖 is depend on the firm’s
productivity level as following:𝜏𝑖𝑗 𝑐𝑗 𝑎 1−𝜀𝜋𝑖𝑗 (𝑎) = (1 − 𝛼) () 𝑌𝑖 − 𝑐𝑗 𝑓𝑖𝑗
𝛼𝑃𝑖(A. 1)

where 𝑃𝑖 is price index, 𝑌𝑖 is the income of country 𝑖. 𝛼 is the parameter that
determine the elasticity of substitution across product, which is 𝜀 = 1⁄(1 − 𝛼). The
operating profits are positive for sales in the domestic market because 𝑓𝑗𝑗 = 0 .
Therefore all producer, 𝑁𝑗 , sell in country 𝑗. On the other hand, sales in foreign country
are profitable only if 𝑎 ≤ 𝑎𝑖𝑗 , where 𝑎𝑖𝑗 is defined by 𝜋𝑖𝑗 (𝑎𝑖𝑗 ) = 0, or:
HMR assume that firm’s productivity 1⁄𝑎 is Patero distributed, truncated to the support [𝑎𝐿 , 𝑎𝐻 ].
𝑘Then, they assume 𝐺(𝑎) = (𝑎𝑘 − 𝑎𝐿𝑘 )⁄(𝑎𝐻− 𝑎𝐿𝑘 ) , 𝑘 > (𝜀 − 1), and so suggest nonlinear estimates but I
do not adopt this.
620

(1 − 𝛼) (𝜏𝑖𝑗 𝑐𝑗 𝑎𝑖𝑗 1−𝜀) 𝑌𝑖 = 𝑐𝑗 𝑓𝑖𝑗
𝛼𝑃𝑖(A. 2)

Using the demand function and pricing equation, the value of country 𝑖’s imports from𝑗 is obtained as following:

𝑀𝑖𝑗 = (

𝑐𝑗 𝜏𝑖𝑗 1−𝜀) 𝑌𝑖 𝑁𝑗 𝑉𝑖𝑗
𝛼𝑃𝑖(A. 3)

where𝑎𝑖𝑗∫ 𝑎1−𝜀 𝑑𝐺(𝑎)𝑉𝑖𝑗 = { 𝑎𝐿0for 𝑎𝑖𝑗 ≥ 𝑎𝐿

(A. 4)

otherwiseThis bilateral trade value is equals zero when 𝑎𝑖𝑗 ≤ 𝑎𝐿 , because 𝑉𝑖𝑗 = 0. If 𝑎𝑖𝑗 > 𝑎𝐿 ,𝑉𝑖𝑗 is increasing in 𝑎𝑖𝑗 . Therefore, 𝑉𝑖𝑗 is depend on the cutoff value of 𝑎𝑖𝑗 , which is
implicitly defined by zero profit condition (A.2). Let define a related latent variables
𝑍𝑖𝑗 as follow:

𝑍𝑖𝑗 ≡(1 − 𝛼) (𝑃𝑖𝛼 𝜀−1
𝑌𝑖 𝑎1−𝜀𝐿𝑐𝑗 𝜏𝑖𝑗 )𝑐𝑗 𝑓𝑖𝑗(A. 5)

This is the ratio of variable export profits for the most productive firm to the fixedexport costs for exports from 𝑗 to 𝑖. If 𝑍𝑖𝑗 becomes equal to 1, there is no exporting
firm by zero profit condition (A.2). If 𝑍𝑖𝑗 becomes greater than 1, the gap between 𝑎𝐿
and 𝑎𝑖𝑗 is increasing in 𝑍𝑖𝑗 . In fact, more firms export to foreign market. Positive
exports are observed if and only if 𝑍𝑖𝑗 > 1. In this case, 𝑉𝑖𝑗 is monotonic function of
𝑍𝑖𝑗 . Moreover, the value of 𝑍𝑖𝑗 affects the export value 𝑀𝑖𝑗 .
For obtaining parameterized estimation equation, I specify some variables. 𝜏𝑖𝑗
captures variable trade costs that affect the volume of firm-level exports. Assume thatthese costs are stochastic due to i.i.d. unmeasured trade friction 𝑢𝑖𝑗 , which are𝛾𝜀−1country-pair specific. In particular, let 𝜏𝑖𝑗≡ 𝐷𝑖𝑗 𝑒 −𝑢𝑖𝑗 , where 𝐷𝑖𝑗 represents the
distance between 𝑖 and 𝑗 , and 𝑢𝑖𝑗 ~𝑁(0, 𝜍𝑢2 ) . In addition, let 𝑓𝑖𝑗 ≡ exp(𝜙𝐸𝑋,𝑗 +21

𝜙𝐼𝑀,𝑖 + 𝜅𝜙𝑖𝑗 − 𝜈𝑖𝑗 ), where 𝜈𝑖𝑗 ~𝑁(0, 𝜍𝜈2 ). The export value from 𝑗 to 𝑖 and latentvariable can be expressed in log-linear form as follows:
𝑚𝑖𝑗 = 𝛽0 + 𝜆𝑗 + 𝜒𝑖 − 𝛾𝑑𝑖𝑗 + 𝑣𝑖𝑗 + 𝑢𝑖𝑗(A. 6)

where 𝜆𝑗 = −(𝜀 − 1) ln 𝑐𝑗 + 𝑛𝑗 is a fixed effect of the exporting country and𝜒𝑖 = (𝜀 − 1)𝑝𝑖 + 𝑦𝑖 is a fixed effect of the importing country, and:𝑧𝑖𝑗 = 𝛾0 + 𝜁𝑗 + 𝜉𝑖 − 𝛾𝑑𝑖𝑗 − 𝜅𝜙𝑖𝑗 + 𝜂𝑖𝑗(A. 7)

where 𝜁𝑗 = −𝜀 ln 𝑐𝑗 − 𝜙𝐸𝑋,𝑗 is a fixed effect of the exporting country and 𝜉𝑖 =(𝜀 − 1)𝑝𝑖 + 𝑦𝑖 − 𝜙𝐼𝑀,𝑖 is a fixed effect of the importing country.
Now, since 𝑉𝑖𝑗 is function of 𝑍𝑖𝑗 , assume 𝑣𝑖𝑗 ≡ 𝜐(𝑧𝑖𝑗 ) that is arbitrary increasing
function of 𝑧𝑖𝑗 . Although 𝑧𝑖𝑗 is unobserved, we can observe the presence of tradeflows. Therefore 𝑧𝑖𝑗 > 0 when 𝑗 exports to 𝑖, and 𝑧𝑖𝑗 = 0 when does it not. Define
the indicator variable 𝑇𝑖𝑗 to equal when country 𝑗 exports to 𝑖, and 0 otherwise. Let
𝜌𝑖𝑗 be the probability that 𝑗 exports to 𝑖, conditional on the observed variables. I
divide (A.7) by the standard deviation 𝜍𝜂 , 𝜍𝜂2 ≡ 𝜍𝑢2 + 𝜍𝜈2 , and specify the Probit
equation to estimate the equation (A.7) as following:7
𝜌𝑖𝑗 = Pr(𝑇𝑖𝑗 = 1|observed variables)= Φ(𝛾0∗ + 𝜁𝑗∗ + 𝜉𝑖∗ − 𝛾 ∗ 𝑑𝑖𝑗 − 𝜅 ∗ 𝜙𝑖𝑗 )(A. 8)

where Φ is the cumulative distribution function of the standard normal distribution,and every stared parameter represents the original coefficient divided by 𝜍𝜂 . Using∗
predicted probability of exports from 𝑗 to 𝑖, 𝜌̂𝑖𝑗 , let 𝑧̂𝑖𝑗= Φ−1 (𝜌̂𝑖𝑗 ) be the predicted∗value of the latent variable 𝑧𝑖𝑗≡ 𝑧𝑖𝑗 ⁄𝜍𝜂 .

Consistent estimation of (A.7) requires controls for both the endogenous number∗of exporters, 𝑣𝑖𝑗 , as well as the sample selection. Using 𝑧̂𝑖𝑗, the inverse Mills ratio
∗
∗
∗
𝜂̂ 𝑖𝑗
= 𝜙(𝑧̂𝑖𝑗)⁄Φ(𝑧̂𝑖𝑗
) is obtained to correct the sample selection bias. For firm∗
heterogeneity, assume that 𝜐(𝑧𝑖𝑗 ) is approximately a polynomial in 𝑧𝑖𝑗. HMR found∗no noticeable changes from expanding 𝜐(𝑧𝑖𝑗 ) beyond a cubic polynomial. Therefore, I∗
use cubic polynomial form in 𝑧𝑖𝑗
in place of 𝑣𝑖𝑗 . Using these correction terms,

estimation equation for value of imports is as follow:
7

HMR employs dividing the equation (A.7) to avoid imposing 𝜍𝜂2 ≡ 𝜍𝑢2 + 𝜍𝜈2 = 1.

22

𝑚𝑖𝑗 = 𝛽0 + 𝜆𝑗 + 𝜒𝑖 − 𝛾𝑑𝑖𝑗
2

3

∗
∗
∗
∗
+ 𝛽𝜂 𝜂̂ 𝑖𝑗+ 𝛿1 𝑧̂𝑖𝑗
+ 𝛿2 (𝑧̂𝑖𝑗) + 𝛿3 (𝑧̂𝑖𝑗) + 𝑒𝑖𝑗(A. 9)

where 𝛽𝜂 ≡ corr(𝑢𝑖𝑗 , 𝜂𝑖𝑗 )(𝜍𝑢 ⁄𝜍𝜂 ) is parameter of inverse Mills ratio, 𝛿 is parameterof the term to correct the firm heterogeneity and 𝑒𝑖𝑗 is an i.i.d. error term. I expand thedimension of these estimation equations by the addition of the variety of goods, index 𝑘,
but this keeps unchanged the structure of the model in essentials.

23