Trading Wastes Derek Kellenberg*

University of Montana

January 12, 2010

Abstract: The international trade of waste products has grown substantially in the past decade. Yet despite the fact that the physical volume of waste traded internationally is more than 4 times greater than the physical weight of passenger automobiles, the business of waste trade has gone largely unexplored in the trade literature. While a sizeable literature has flourished around the notion of international pollution havens (the movement of production with polluting by-products to low environmental regulation countries), this paper explores the possibility of international waste havens (the physical movement of waste, rather than production, to low environmental regulation countries). Specifically, the hypothesis that cross-country differences in environmental regulations are a significant determinant of waste trade is examined. Using bilateral waste trade data on 92 countries, robust evidence is found that waste imports increase for a country whose environmental regulations deteriorate vis-à-vis it’s trading partner, and that there is substantially less waste traded when both the importing and exporting country have ratified the Basel Convention on hazardous waste trade. This last result suggests important policy implications for large waste trading countries, such as the U.S. and Hong Kong, who have not ratified the Basel Convention.

JEL Codes: F18, F13, Q53, Q56

Keywords: International Trade, Waste, Gravity Model, Pollution Havens, Environmental Regulations, Recycling

                                                            * Email: Derek.Kellenberg@mso.umt.edu

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I. Introduction

In 2007, the world traded more than 191 million tons of waste1. To put this figure in

context, it has been estimated that the volume of debris removed from the World Trade Center

site after the September 11, 2001 disaster was approximately 1.6 million tons (Barry, 2002).

Which means that the volume of waste traded internationally in 2007 alone was greater in

magnitude than the debris generated by 119 World Trade Center disasters. What is more

exceptional is the fact that the annual physical weight of waste traded in international markets

grew by 67% in five short years, from 114 million tons in 2002 to 191 million tons in 20072.

The physical weight of waste traded is substantial when compared with the physical weight of

other large finished traded goods. In 2007, the physical weight of passenger automobiles3

shipped worldwide was 41 million tons; less than 22% of the physical weight of waste traded.

While much of the waste shipped is sent to foreign markets for the purposes of recycling and

recovery, mounting anecdotal evidence suggests that waste is increasingly exported to countries

with lax environmental regulations, suggesting the possibility of international waste haven

effects.

This paper takes a unique look at the determinants of waste trade by focusing on data

from waste and scrap categories in the six digit Harmonized System (HS) and combining it with

a new index on environmental regulation. Specifically, the hypothesis that relative levels of

environmental regulation across countries are an important determinant of waste trade is

explicitly tested. An empirical strategy similar to the Poisson pseudo-maximum-likelihood

estimator proposed by Santos Silva and Tenreyro [2006] is employed on a cross sectional dataset

                                                            1 Based on UNComtrade data on 62 HS-6 categories of waste & scrap material. The 62 6-digit HS categories are listed in Table A1 of the Appendix. 2 Again, based on UNComtrade data on the 62 6-digit HS categories listed in Table A1 of the Appendix. 3 Defined as the four digit HS code 8703 for motor vehicles principally designed for transport of passengers. Data from the UNComtrade database. 

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on bilateral waste trade between 92 developed and developing countries. Robust evidence is

found that relative levels of environmental regulation across countries are a significant

determinant of waste trade. All else equal, for every 1% that a home country’s environmental

regulations deteriorate vis-á-vis a foreign bilateral trading partner, the home country will

experience a 0.32% increase in waste imports from the foreign trading partner. When one

considers that the average developing country in the sample has an environmental regulation

index that is 39% lower than the average developed country, this implies a substantial effect for

environmental regulation differences on waste trade for many bilateral country pairs.

The findings of this paper make a new contribution to the trade and environment

literature by empirically analyzing an overlooked component of international trade that has been

missed in the popular pollution haven debate. The pollution haven literature4, which has broadly

examined the effects of differences in environmental standards across countries as a determinant

of the location of pollution intensive dirty industry, has found mixed results for pollution haven

effects, with most recent papers (e.g. Ederington and Minier [2002], Ederington, Levinson, and

Minier [2005], Chintrakam and Millimet [2006], and Kellenberg [2009(a)]) finding significant,

albeit modest overall effects of environmental regulation on foreign direct investment and trade

flows. While the effect of environmental regulations on industry location has been extensively

explored, the pollution haven literature (and the trade literature more generally), has missed a far

more direct way for countries to ‘export’ pollution. They quite simply export it. For many

countries dealing with physical waste from both production and consumption activities, the                                                             4 The pollution haven literature has broadly centered around those papers looking at the effects of differences in environmental regulation on trade and foreign direct investment flows (such as Keller and Levinson [2002], Fredriksson, List, and Millimet [2003], Eskeland, and Harrison [2003], Ederington and Minier [2003], Javorcik and Wei [2004], Ederington, Levinson, and Minier [2004], Kahn and Yutaka Yoshino [2004], Ederington, Levinson, and Minier [2005], Chintrakarn and Millimet [2006], McAusland [2008], and Kellenberg [2009(b)]) and those that look at the effects of trade and foreign direct investment on environmental quality (such as Copeland and Taylor [1999], Antweiler, Copeland, and Taylor [2001], Dean [2002], Cole and Elliott [2003], Cole [2004], Frankel and Rose [2005], and Kellenberg [2008]).

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marginal cost of exporting waste may be far less expensive than the fixed cost of having to build

new waste facilities at home or to relocate entire production facilities to a foreign country.

Indeed, Ederington, Levinson, and Miner [2005] and Kellenberg [2009] find evidence that

capital intensive industries with large fixed costs, which are typically thought to be the dirtiest

industries, are less likely to move production to foreign countries with lower environmental

regulations than less capital intensive, or ‘footloose’, industries. The intuition being that the

fixed costs of moving capital intensive production facilities are greater than the marginal benefits

of lower environmental regulations in the foreign location. The results of this paper suggest a

complimentary motive. The marginal cost of exporting waste to a lower environmental

regulation country may be cheaper than the fixed costs of moving production to a foreign

country to take advantage of the lower regulations.

There are likely also political economy effects for countries to raise regulation on waste

from consumption activities, creating incentives for countries to export waste to lower

environmental regulation locations. McAusland [2008] demonstrates that when economies are

open, regulators have an incentive to increase regulation on pollution that is a by-product of

consumption activities. This implies that if a significant proportion of waste is an externality of

consumption5, higher regulation in open economies may induce countries to export waste to

lower environmental regulation countries.

The possibility of these types of effects and the results of this paper suggest that a waste

haven effect may be a more important component of the trade and environment debate than the

more popularly studied pollution haven effect. The distinction is important because the

                                                            5 It’s not possible to tell from the data what exact percentage of waste is technically from consumption or production, but if one thinks about electronic waste, municipal waste, or scrap metal from old automobiles and appliances (particularly in high income developed countries) it is clear that waste from consumption activities is likely to be a significant component of waste generated.

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environmental effects of waste movements to countries with low environmental standards can be

severe. Waste-management practices vary widely across developed and developing nations with

formal collection, disposal, and treatment being the norm in the former, while engineered

landfills are rare and the dumping of wastes in rivers and fields is not uncommon in the latter

(Ray, 2008). Environmental non-profit groups such as the Basel Action Network (BAN) and

Toxics Link have documented cases in China and Nigeria where large quantities of toxic

electronic waste, often labeled as metal or plastic waste and scrap, is imported from developed

nations such as the U.S., Europe, and Japan (Pellow, 2007). The waste is disassembled, crushed

and melted (using fire or acid) to recover what precious metals, such as copper, gold, or lead, can

be salvaged, the rest often dumped or discarded directly into the environment (Puckett and

Smith, 2002).

The environmental consequences and subsequent human health problems can be severe

for countries that are ill-equipped to handle the recycling and recovery of materials that are often

highly toxic. For example, Guiyu, China was a group of four predominantly agricultural cities

prior to 1995. By 2002 it is estimated that up to 100,000 people were employed in the E-waste

recycling industry in Guiyu and ground water was so polluted that drinking water had to be

trucked in from a town 30 km away (Puckett and Smith, 2002). BAN took soil samples along

the Lianjiang river in Guiyu and found that lead concentrations were 212 times higher than what

would be treated as hazardous waste in the Netherlands. Other heavy metals such as barium, tin

and chromium were 10, 152, and 1,338 times the EPA threshold levels for environmental risk in

soil (Puckett and Smith, 2002). The above metals are known toxics that when ingested or

inhaled beyond certain levels can have severe impacts on the brain, nervous system, liver and

kidneys, especially in infants and young children. Waste that is exported to low environmental

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regulation countries can have serious environmental and health consequences and raises

questions about the social inequities that may be created by waste trade.

The idea that waste disposal responds to increased stringency of environmental

regulation, often through taxes or other enforcement, has been explored in other contexts in prior

work. For example, Sigman [1996] found that higher taxes on chlorinated solvent waste from

metal cleaning encouraged less waste generation and disposal in the U.S., while Sigman [1998]

found that in states where the cost of disposal and reuse of used oil was high there was

substantial substitution toward the lower cost alternative of illegal dumping. Levinson [1999(a)

& 1999(b)] demonstrated that in the U.S., states that increased hazardous waste disposal taxes

experienced decreases in hazardous waste shipments from other states.

Using self reported bilateral data on international hazardous waste shipments for Basel

Convention members, Baggs [2009] finds that countries import less hazardous waste the higher

is per capita income and import more waste the greater the country’s capital intensity. The

results support the idea that hazardous waste responds to the importing countries income level

(and thus presumed increase in environmental regulation), but finds that other factors such as

distance and capital intensity (reflecting economies of scale and comparative advantage in

recycling and disposal) are more important in determining international trade in hazardous waste.

This paper extends the work by Baggs in a number of dimensions. First, this paper

examines all trade in international waste whether it is self reported as hazardous or not. This is

important, as anecdotal evidence suggests that potentially large volumes of waste that may be

hazardous under the auspices of the Basel Convention (for example, products such as electronic

waste containing hazardous lead or mercury) is never actually reported (Grossman [2006],

Pellow [2007]). To the extent this is true, this waste trade will be captured in HS classifications

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as waste or scrap but would not be self-reported to the Basel Convention. Second, the total

quantity of self-reported waste flowing to non-OECD countries in Baggs is quite small, only

11% of the sample. When looking at the HS coded waste trade data used in this paper nearly

39% of world waste trade flows to non-OECD developing countries. Two likely explanations

for this discrepancy are the fact that the U.S., which is the world’s largest exporter of waste, is

not a ratified member of the Basel Convention and therefore is not required to self report

hazardous waste shipments. The second potential explanation is the aforementioned problem

that countries may simply underreport the true extent of self-reported hazardous waste

shipments, particularly when being shipped to low environmental regulation countries.

The second major advantage of the current paper is that an explicit measure of cross-

country bilateral differences in environmental regulation is examined. This has two profound

advantages for the estimation procedure. First, it allows for explicit estimation of an

environmental regulation effect on waste trade, independent of other proxies for environmental

regulation such as income. Second, by defining a bilateral environmental regulation measure

the model can be estimated using importer and exporter specific fixed effects to control for

unobserved importer and exporter heterogeneity; a factor demonstrated in the trade literature (see

for example Anderson and van Wincoop [2003], Santos Silva and Tenreyo [2006], or Helpman

et al. [2008]) to be important for consistent estimation of gravity models.

Another contribution of this paper is that the data allows for estimation of a Basel

Convention effect in reducing international waste trade. The Basel Convention on the transport

of hazardous waste came into effect in 1989 with 82 countries as original signatories. Today

there are approximately 170 member countries that have ratified the Basel Convention. The goal

of the convention was to reduce trade in international hazardous waste to countries that were

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unable to safely and adequately recycle or dispose of it. Under the rules of the Convention each

exporting country must announce shipments of any hazardous waste material before shipment.

Importing countries must show that they have adequate capability to handle the waste and then

approve the shipment. This paper is the first to explicitly estimate the relationship between

ratification of the Basel Convention and international waste trade across countries. It is found

that, all else equal, substantially less waste is traded between two countries when both the

importing and exporting countries have ratified the Basel Convention.

The importance of this finding is substantial from a policy perspective for large waste

exporting countries such as the U.S. The U.S. is one of the few major players in international

waste trade that is not a ratified member of the Basel Convention. In August of 2008 the United

States Government Accountability Office (USGA) made a recommendation to the chairman for

the committee on foreign affairs in the U.S. House of Representatives to strengthen enforcement

of harmful U.S. exports (USGAO [2008]). In particular, the recommendation is made for the

U.S. to ratify the Basel Convention. But any recommendation that countries ratify the

convention as a strategy to reduce exports of hazardous waste begs the question of whether any

connection exists between less waste trade and ratification of the convention. Recent work by

Rose [2004, 2007], Baier and Bergstrand [2006], Tomz, Goldsteing, and Rivers [2007] and Tang

[2005] has found mixed results on the empirical effects of organizations and agreements, such as

GATT, the WTO, NAFTA, ASEAN, which are designed to promote international trade. The

Basel Convention is quite different from most trade agreements and international trade

organizations in that it is designed to restrict, rather than promote, trade (Baggs [2009]). The

results of this paper suggest that ratification of the Basel Convention could potentially have a

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substantial affect for reducing the volume of waste exported from the United States to other

Basel Convention members.

The rest of the paper is laid out as follows. In Section II, a diagrammatic outline of the

determinants and end uses of waste and scrap products is examined. In Section III the data used

in the analysis is presented, while Section IV describes the baseline empirical model. Section V

presents the results as well as robustness tests and an alternative empirical specification; and

Section VI concludes.

II. Determinants of Waste Trade

In this section a schematic framework of the determinants of waste trade is laid out and

discussed. Figure 1 describes the flow of waste for country i. Domestic waste is generated from

consumption and production activity in the Domestic Economy via arrow (a). There are three

potential alternatives for dealing with domestic waste: it can be recycled (b), exported to other

countries (c), or disposed of domestically (d). If the waste product is sent for recycling (b), there

are two byproducts. Recycled material is sent back to the Domestic Economy to be reprocessed

or consumed via channel (e). However, few waste products are 100% recyclable. Some waste

inevitably ends up back in the domestic waste stream after the recycling phase via channel (f) to

either be recycled (b) again (possibly for different recyclable components) or to be exported (c)

or disposed of domestically (d). In most countries, disposing of waste domestically (d) will

involve a combination of landfill or incineration, but in countries with lax environmental

regulations may simply involve discarding of waste directly into the environment. Waste that is

exported (c) will be exported for two possible reasons: to be recycled for materials in the foreign

country (g) or to be disposed of in the foreign country (h). Note that like arrow (f) in the

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domestic economy, some waste that is sent for recycling in the foreign country will not be

recyclable and must be disposed of in the foreign country (i).

The pertinent question in this study is to determine if differences in environmental

regulation across countries is an important factor in determining waste trade. Of course other

factors must be controlled for. Larger countries with large industrial sectors and greater volumes

of consumption generate more waste (a) while smaller more agricultural economies that have

lower volumes of consumption are expected to generate less waste. These country specific

factors affect the volume of waste in each country through channel (a). Whether countries

decide to recycle waste material domestically (b), dispose of waste domestically (d), or export

waste (c) will be a function of a number of factors. First and foremost, countries with more

stringent environmental regulations will have higher costs of disposing of waste (d), making

exporting via (c) more attractive if foreign environmental regulations are lower if the waste

cannot be recycled. Country specific characteristics such as the density of the population,

geography, climate and culture are likely to determine the availability of landfill space, attitudes

toward pollution, or feasibility of recycling programs. Importantly, country i’s decision to

recycle domestically, dispose domestically, or export its waste will depend on the relative cost of

exporting to countries –i. These relative costs will again depend on importing country specific

effects such as the size and industrial composition of the importing country. Larger more

industrialized countries might be expected to have a larger import demand for foreign waste to

be used for recycling at home (g). Other country specific factors mentioned above such as

geography, population density, or climate may make foreign disposal (h) less expensive. Lax

environmental regulation in countries –i will make foreign recycling (g) and disposal [(i) & (h)]

less expensive and waste exports by country i more attractive. Traditional bilateral trade

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variables such as distance (as a proxy for transport costs), contiguity, former colonial

relationships, common language and whether countries are members of the same free trade area

or customs union are also expected to influence the relative costs of exporting waste (c) relative

to domestic recycling (b) or disposal (d).

The focus of this paper is on the determinants of waste exported at channel (c). It is clear

that waste exports from country i to countries –i is a function of importing and exporting country

specific characteristics, bilateral trade characteristics, and differences in the stringency of

environmental regulations and productivity of recycling sectors.

III. Data

Bilateral waste trade data was obtained from the UN Comtrade Database for 92

countries’ waste imports for the year 2004. Waste trade imports were defined as all six-digit HS

categories where waste and/or scrap was the only categorization of a product or material. This

yielded 62 six-digit HS categories of waste, the descriptions of which can be found in Table A1

of the Appendix, along with the country list for the sample in Table A2. For each of the 62

categories there are two measures of trade, the total bilateral weight (in tons) of the goods traded

and the total value traded. Total bilateral waste trade is defined as the total weight (or value) of

waste traded between countries aggregated across the 62 HS categories, yielding 8,372

observations for the sample6. It is important to note here that the data being used is all waste and

scrap traded, not just hazardous waste trade. The reason for this is twofold.

First, the coding for hazardous waste under the Basel Convention is defined according to

the hazardous characteristics of the material (e.g. toxic, ecotoxic, poisonous, corrosives, etc.),

while international trade data under the HS code classification is defined according to the

product (e.g. ferrous waste and scrap, clinical waste, sawdust & wood waste, etc.). While much                                                             6 Total observations are 92 x 92 – 92 (countries do not trade with themselves) = 8,372.

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of the ferrous waste (metal containing iron) and clinical waste will likely have hazardous

characteristics under the rules of the Basel Convention, sawdust or paperboard waste is less

likely to be hazardous (unless it is mixed with other hazardous substances such as oil). In short,

every category of waste in the trade data could have hazardous characteristics but it is impossible

to distinguish from the trade data which products actually contain hazardous characteristics. One

way to identify waste that is classified as hazardous is to use the self-reported hazardous waste

data from ratified Basel Convention countries, such as used by Baggs [2009]. The problem with

this is that it potentially misses a large amount of waste trade involving countries that are not

Basel Convention members (i.e. U.S. and Hong Kong) and therefore are not obligated to self

report, as well as waste that is hazardous but simply not self reported.

Second, regardless of whether waste is hazardous under the classification of the Basel

Convention it still must be handled and disposed of in an environmentally safe manner. For

example, much of a typical community’s municipal waste would not technically be classified as

hazardous, but one would be remiss to think that it did not need to be disposed of in an

environmentally sound manner without creating adverse environmental, aesthetic, or health

effects. As Section II and Figure 1 above make clear, where waste (classified hazardous or not)

ultimately ends up is largely a function of the relative stringency of environmental regulations.

For these reasons, the study here takes a broader view of waste by looking at all waste and scrap

regardless of its hazardous classification.

The environmental regulation index is constructed using data from the 2003-2004 Global

Competitiveness Report7. The report is based on survey responses from 7,741 company

                                                            7 A number of prior empirical papers, including Carr et al. [2001], Markusen and Maskus [2002], Blonigen et al. [2003], Yeaple [2003], Javorcik and Wei [2004], Eckholm et al. [2007], and Kellenberg [2009(a & b)], have used survey questions from the Global Competitiveness Report to control for various effects on trade and foreign direct

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executives across 102 countries (which account for 97.8 of the world’s GDP). The

environmental regulation index used in this paper is constructed using data from five of the

survey questions in the report. Company executives in each country are asked to rank the

stringency of the country’s air, water, chemical, and toxic waste regulations relative to other

countries in the world8. In addition, they are asked a question on how well the country enforces

its environmental regulations. Each of the five survey questions used for the index are displayed

in the Appendix in Table A3, with answers based on a 1-7 scale. The country level

environmental regulation index is calculated as the sum of the mean of the five answers reported

for each country. The scale of the index ranges from 0 to 35 with Germany having the highest

observed environmental regulation index of 32.5 and Guatemala and Paraguay having the lowest

ranking at 11.5.

The bilateral measure of the environmental regulation gradient between two countries is

measured as the average percentage change in environmental regulation between the importing

and exporting country9. Specifically, the environmental gradient, Eij, for trade flows from

country i to country j is calculated using the midpoint formula such that

( ) ( )( )2// jijiij EEEEE +−= . Recall that bilateral trade observations in the data are measured as

                                                                                                                                                                                                investment flows. Detailed discussion of the survey and the robustness of the data can be found in chapter 3 of the Global Competitiveness Report.  8 While inclusion of chemical and toxic waste regulations are fairly self-explanatory in this context, air and water were also included because waste products are often either incinerated (affecting air quality) or dumped in unlined landfills (affecting water quality). 9 Measuring the environmental regulation gradient as a change in levels (rather than as a percentage change) is also a plausible way to think about measuring the gradient. However, measurement in levels implies that the gradient between a bilateral pair where the exporting country has an environmental regulation index of 32 and the importing country has an index of 31 is the same as a bilateral pair where the exporting country has an environmental regulation index of 15 and the importing country has an index of 14. The percentage change formulation implies that the gradient in the former case is 3.2% while the latter is 6.9%. This makes sense as a 1 unit increase in the environmental regulation index for lower environmental regulation countries represents a larger change than for higher regulation countries…and it is the relative difference between bilateral pairs that we are attempting to measure here. The model was run (results not reported here) with the environmental gradient defined as a simple difference in levels and the results are similar to those reported for the gradient in percentage terms.

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country j imports from country i. Larger positive values of Eij imply that the exporting country

has more stringent environmental regulation than the importing country, while large negative

values imply that the importing country has more stringent environmental regulations. If higher

environmental regulation countries export waste to lower environmental regulation countries, a

positive coefficient is expected on the environmental regulation gradient variable. A cursory

look at the largest importers and exporters of waste suggests a possible link between

environmental regulations and waste trade.

Table 1(a) shows the top 10 exporters of waste worldwide. What is interesting to note is

that the top 10 exporters of waste make up nearly 76% of all waste exported in the world. Not

surprisingly, these 10 countries are among the world’s largest economies with nine of the ten

countries being high income developed countries (Russia being the lone exception)10.

Importantly, the average environmental regulation index of 27.03 for the top 10 exporters is

more than a standard deviation (6.25) above the sample average of 20.1.

Table 1(b) shows the top 10 importers of waste worldwide. Contrary to what a waste

haven story might suggest, with the exception of China and Turkey all of the top 10 importers of

waste are developed nations; with more than 43% of all waste worldwide being imported by the

eight countries other than China or Turkey. Developed nations do in fact import a substantial

share of the world’s waste. Further, the average environmental regulation index among the top

10 importers is not substantially different than the top exporters. But there are important

differences that must be pointed out. First, five of the top ten importers of waste are also among

the top ten exporters. However, of the eight developed countries in Table 1(b) all but Belgium

are net exporters of waste. More importantly, China and Turkey, ranking among the worst

                                                            10 Throughout this section the terms developed and developing will be used to describe countries of different income levels. Developed countries are those classified by the World Bank as ‘high income’ countries, while those countries classified by the World Bank as ‘middle income’ or ‘low income’ are referred to as developing countries.

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environmental regulation indexes for all countries in the sample, are the two largest importers of

waste in the world.

If over 43% of the world’s waste was imported by the eight developed countries in Table

1(b), where did the rest go (other than China and Turkey)? Did developing countries in general

receive a substantial share? In Table 1(c), the top 10 developing importers of waste are

presented. These ten developing countries imported more than 36% of the world’s waste traded.

Importantly, the top ten developing countries had an average environmental regulation index of

17.39. That’s lower than the sample mean and more than one and a half standard deviations

lower than the environmental regulation index of the top 10 largest exporters11.

When we look at all countries in the sample, the story is very similar. Table 2 reports the

share of world income, share of world waste exports, share of world waste imports, and average

environmental regulation index for developed and developing countries. In the first two columns

it is apparent that the share of world waste exports for both developed and developing countries

is almost perfectly proportioned to their world income share. That is, countries’ capacity to

supply waste to international markets is roughly proportional to their consumption share. In the

third column, developed countries import a larger overall share of world waste imports than

developing countries (61.3% vs. 38.7%); a fact that at first seems contrary to a waste haven

story. Developed countries import more of the world’s waste than developing countries.

However, developing countries import a disproportionately large share of the world’s waste

relative to their overall income share. Developing countries have world income shares and

world waste export shares of 21% but are importing nearly 39% of the world’s waste. One

potential explanation for the disparity is the large difference in the average environmental

                                                            11 Recall from above that the sample mean and standard deviation of the environmental regulation index variable are 20.1 and 6.25, respectively.

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regulation indexes between developed and developing countries observed in the fourth column of

Table 3. Again, the average developed country in the sample has an environmental regulation

index that is more than one and a half standard deviations greater than the average developing

country. On the surface, Tables 1 & 2 suggest a possible connection between lower

environmental regulations and the destination of waste in international markets.

A potentially confounding factor discussed in Section II above is that waste may flow to

countries with relatively weak environmental regulations, but if weak environmental regulation

countries are actually more productive in handling and recycling waste, then any effects on the

environmental regulation gradient could be spurious. Cross-country data on measures of

recycling productivity, especially for developing countries, are difficult to find. Two things

allow for control of potential differences in recycling and handling capabilities. First, as will be

discussed in the next section describing the empirical model, importer and exporter specific fixed

effects are employed on the bilateral waste trade observations. This means that all unobserved

country specific characteristics, such as the absolute productivity of recycling and handling

operations in the importing and exporting countries are controlled for. However, the hypothesis

being tested here is that the environmental regulation gradient between two countries is the

relevant metric in determining waste trade. Thus, we would also like to control for any gradient

differences in recycling productivities. Two measures are used to capture differences in

recycling productivity across countries: the recycling wage rate and GDP per capita.

Assuming that recycling wage rates reflect the marginal productivity of workers in the

recycling industry, countries with higher productivity recycling sectors will have higher

recycling wage rates. Data on recycling wage rates were obtained from the ILO LABORSTA

database. A recycling wage gradient analogous to the environmental regulation gradient was

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constructed such that for trade flows from country i to country j the recycling wage gradient is

( ) ( )( )2// jijiij RRRRR +−= . Unfortunately, recycling wage rates were only available for 64 of

the 92 countries in the sample. As a second measure, GDP per capita is used as a proxy for

recycling productivity across countries. GDP per capita is a helpful proxy for two reasons. First,

GDP per capita is highly correlated with recycling wage rates. Countries with high levels of

productivity in general, as reflected by high GDP/capita, typically also have high productivity in

the recycling sector. In Table 3, we see that the recycling wage gradient has a simple correlation

with the GDP/capita gradient of 0.92. Second, GDP per capita is available for all countries in the

sample which avoids having to drop a disproportionate number of low income, low

environmental regulation countries where data on recycling wage rates is not available.

Contrary to concerns that countries may be more productive at recycling despite low

environmental regulations, Table 3 shows that the environmental regulation gradient, recycling

wage gradient, and GDP/capita gradient12 are strongly positively correlated. That is, high

income countries with more stringent environmental regulations tend to also be more productive

at recycling. Given that a substantial portion of waste and scrap material flows to countries for

the purposes of recycling, we should expect that, all else equal, more productive recycling

countries will import more than less productive recycling countries. This implies the expected

coefficient on the recycling wage gradient and GDP/capita gradient variables is negative13.

Additional bilateral trade variables that have been shown to be important in the trade

literature such as distance, whether two countries are members of a free trade area or customs                                                             12 The GDP/capita gradient is defined in the same fashion as the environmental regulation gradient and the recycling wage gradient. 13 Recall that the recycling wage gradient and GDP/capita gradients are defined as

( ) ( )( )2// jijiij RRRRR +−= , where country i is the exporting country, country j is the importing country, and the unit of observation in the dataset is country j imports from country i. Larger positive values of Rij imply that the exporting country is more productive at recycling than the importing country and should therefore lead to fewer waste imports for country i from country j.

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union, share a common official language, have a colonial link, or are contiguous are also

included as control variables. Descriptive statistics for all variables and data sources are found in

the Appendix.

IV. The Empirical Model

The empirical approach to estimating waste trade is based on the gravity equation first

introduced by Tinbergen [1962], and expanded in a vast and deep trade literature14. Given N

countries, bilateral waste trade between country i and j, Tij, is assumed to be a positive function

of the size of two economies, Gi and Gj (measured in units of GDP), and negatively related to

bilateral distance, Dij. In addition, bilateral waste trade is assumed to be influenced by a

constant, α0, and K other bilateral factors, ξij, such as colonial ties, free trade agreements, or

environmental regulation gradients between countries. The basic gravity relationship is modeled

as

, (1) ∏=

=K

kijijjiij

nDGGT4

0321 ββββ ξα

where it is assumed that β1 > 0, β2 > 0, and β3 < 0. To account for unobserved country importer

and exporter heterogeneity Anderson and van Wincoop [2003] and Silva and Tenreyro [2006]

augment equation (1) by including importer and exporter fixed effects, and , implying

equation (1) can be rewritten as

Id Ed

. (2)

Ejj

Iiik dd

K

kijijij eDT ααββ ξα +

=∏=

40

3

                                                            14 A few recent examples of papers employing the gravity model in a large gravity literature include Feenstra, Markusen, and Rose [2001], Frankel and Rose [2002], Anderson and Marcouiller [2002], Anderson and van Wincoop [2003], Hanson and Xiang [2004], Rose [2004], Blomberg and Hess [2006], Hallak [2006], Balistreri and Hillberry [2007], Berthelon and Freund [2008], Helpman, Melitz, and Rubinstein [2008], and Baier and Bergstrand [2009].

19  

Controlling for unobserved importer and exporter specific heterogeneity using fixed effects

implies that only bilateral variables can be estimated. All unobserved importer and exporter

specific variables that do not change by bilateral country pair (such as GDP) are captured by the

importer and exporter specific dummy variables.

A popular approach to estimation has been to estimate a log-linearization of equation (2)

using linear regression techniques. However, Santos Silva and Tenreyro [2006] show that linear

regression techniques on a log-linearization of equation (2) are inconsistent as the log-linearized

error is dependent on the covariates and, further, log-linearization inadequately accounts for

values of zero in the dependent variable. They demonstrate that an alternative Poisson pseudo-

maximum likelihood (PPML) estimator, often used for count data applications, is a consistent

estimator of bilateral trade gravity models15. The PPML estimator is the baseline technique for

estimation of waste trade in this paper. Letting , the PPML has a conditional

mean, µ, that depends on the bilateral characteristics between countries i and j, and is given by

∏=

=K

kijijij

kD4

3 ββ ξβx

⎥⎥⎦

⎤

⎢⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛== ∏∏

==

βxx ij

N

j

Ej

N

i

Iiijijijij ddTE

11

exp)(μ , (3)

with the probability of observing a specific bilateral trade value given by

!)exp(

)Pr(ij

Tijij

ijij TT

ijμμ−=x . (4)

Parameter estimates are obtained by maximizing the log of the likelihood function,

jiT

TLN

i

N

j ij

yijij

N

i

N

jijij

ji

≠∀−

== ∏∏∏∏= == =

,!)exp(

)Pr()(1 11 1

μμμXT,β . (5)

                                                            15 Using U.S. intranational trade data, Henderson and Millimet [2008] also demonstrate that the PPML estimator is preferred to the log-linear specification.

20  

Although the PPML is a consistent estimator, in applications where there are “excess

zeros” in the data the fit of the model can be improved upon by modeling a separate process for

zero counts. The zero inflated Poisson pseudo maximum likelihood (ZIPPML) model allows for

bilateral trade to be generated by two processes, one that generates zero counts and another that

generates positive counts. The ZIPPML model has the effect of increasing the variance of the

model and increasing the predicted probability of observing zero trade values (Long, [1997]).

This process is potentially important for the waste trade data used in this paper, where 62% of

the bilateral country observations in the dataset have zero trade values. To estimate the

ZIPPML, it is assumed that there is a second process that generates zero trade counts with

probability ρ, where ρ is determined by a vector of bilateral characteristics, ωij, and ψ are a

vector of parameters to be estimated such that

)( ψω ijij F=ρ , (6)

and F(.) is the standard normal cumulative distribution function. The binary process in equation

(6) is estimated using a standard probit regression model. The probabilities in equation (4) are

then adjusted based on whether the bilateral trade observation is a zero or positive count (Long,

[1997])

)exp()1()0Pr( ijijijijijT μρρ −−+== x (7)

0for !

)exp()1()Pr( >

−−= ij

ij

Tijij

ijijij TT

Tijμμ

ρx.

The effect of the ZIPPML is to decrease the conditional mean in equation (3) by giving more

weight to zero observations such that

ijijijijijijijijijij TE μρμρμρ −=−×+×= )]1([]0[),( ωx , (8)

21  

and the conditional variance of the ZIPPML estimator becomes

)1)(1(),( ijijijijijijijTVar ρμρμ +−=ωx . (9)

To test the fit of the ZIPPML relative to the PPML a Vuong statistic (Vuong [1989]) is

calculated with large values of the Vuong statistic favoring the fit of the ZIPPML over the

PPML.

V. Results

Estimation results for the PPML and ZIPPML specifications are presented in Table 4,

with importer and exporter fixed effects included in all regressions16. Model (1) and model (2)

present the most basic specification where bilateral waste imports are a function of traditional

bilateral gravity variables and the environmental regulation gradient variable. In model (2), both

the ZIPPML and the 1st stage probit equation (Inflate) that predicts the probability of a zero

count are reported.

Recall from equation (6) that contains variables that predict the probability of a zero

between two bilateral trading countries. Naturally, most of the variables that affect the marginal

decision to trade waste, such as distance, common language, or environmental regulation

gradient will also affect the decision not to trade. Therefore, contains xij as well as an

additional exclusion variable that is likely to influence entry into export markets by firms.

ijω

ijω

In 1995 the BAN Amendment to the Basel Convention was introduced that would place

an outright ban on OECD and EU countries (as well as Liechtenstein), known in the convention

as Annex 7 countries, from exporting any hazardous waste to non-OECD, non-EU countries

(non-Annex 7 countries). The BAN Amendment has not yet become an official Amendment to                                                             16 In the sample, 8 countries had zero values for waste imports from every country. As a result, observations where these eight countries are importers were dropped as their imports (zero) were perfectly collinear with the importer fixed effect. This reduces the original sample size of 8,372 to 7,644 observations. The 8 countries were the Dominican Republic, Indonesia, Nigeria, Panama, the Philippines, Ukraine, Venezuela, and Serbia and Montenegro.

22  

the Basel Convention due to legal wrangling over the number of countries needed to ratify the

amendment for it to officially become part of the Basel Convention. However, by 2004 there

were more than 60 countries that had domestically ratified the Basel Ban. This information is

used to construct an indicator variable, A7/Ban3 to non-A7 dummy, for bilateral trading pairs

where the exporting country is an Annex 7 country that has ratified the Basel Ban and the

importing country is a non-Annex 7 country. For these bilateral country pairs, exporting

countries ratification of the Basel Ban should have a negative effect on the decision to export to

the importing country (and therefore a positive effect on the probability of a zero observation), as

it is illegal (under domestic law) in these countries to export hazardous waste to non-Annex 7

countries.

In the PPML and the ZIPPML models in models (1) and (2) of Table 4, the

environmental regulation gradient variable is positive and significant, indicating that for every

1% that a home country’s environmental regulations deteriorate vis-á-vis a foreign bilateral

trading partner, the home country will experience a 0.22% increase in waste imports from the

foreign trading partner17. Distance, contiguity, and common language are significant and of the

expected sign. The magnitude of the distance elasticity at first appears quite large when

compared with prior literature that has found estimates that range in the -0.75 to -1 range for

trade overall. However, waste exports, which are often scrap metals and alloy materials, tend to

be heavy relative to their value (especially when compared to most other traded products) and

should be more sensitive to distance and transport costs.

The first stage Inflate equation in model (2) indicates that the probability of a zero

observation in bilateral waste trade increases in distance and if the exporting country is an Annex

                                                            17 The environmental regulation gradient is reported as a percentage in the dataset. Therefore, the environmental regulation gradient can be interpreted as a semi-elasticity.

23  

7 country that has ratified the Basel Ban Amendment and the importing country is a non-Annex

7 member. The probability of a zero observation decreases when countries are contiguous, have

a common official language, or have prior colonial ties.

In models (3) & (4) a dummy variable is added that indicates if both the importing and

exporting country have ratified the Basel Convention18. The Basel ratification dummy is

negative and significant in both models. The estimate in model (4) implies that if both countries

are ratified members of the Basel Convention they trade 82% less waste relative to bilateral trade

pairs where one or more of the countries have not ratified the Basel Convention19. At first

glance, this appears to be a huge effect. But the magnitude of this effect is sensible when one

considers that three of the most heavily trafficked bilateral waste trading pairs (USA to China,

USA to Hong Kong, and Hong Kong to China) are between countries where one or more of the

trading partners is not a ratified member of the Basel Convention. It is important to caution here

that the Basel Convention dummy should not be interpreted as a causal effect, but rather a strong

empirical correlation. Determination of a causal relationship requires panel data where one

could observe changes in waste trade before and after countries ratified the convention.

Nonetheless, the significance and magnitude of the Basel dummy suggests that ratification of the

Basel Convention by a country such as the U.S. could potentially have a substantial quantitative

effect on the volume of waste exported to other Basel ratified countries.

                                                            18 In the dataset, there are four countries that have not ratified the Basel Convention and therefore are not legally obligated to report hazardous waste shipments under the auspices of the Convention. These four countries are Algeria, Hong Kong, the United States, and Zimbabwe. 19 The marginal percentage estimate of the Basel Convention dummy is calculated as , where β is the Basel dummy coefficient estimate.

100*)1( −βe

24  

Models (5) & (6) of Table 4 present the same regression as models (1) and (2) but include

the recycling wage gradient20. Recall that the recycling wage gradient captures the difference in

recycling productivity between the exporting and importing country. All else equal, the more

productive the exporting country is at recycling relative to the importing country, the less waste

expected to be imported by the importing country. This implies an expectation of a negative

coefficient on the recycle wage gradient. In models (5) and (6) this is indeed what we observe.

For every 1% that a home country’s recycling productivity deteriorates vis-á-vis a foreign

bilateral trading partner, the home country will experience a 0.013 % decrease in waste imports

from the foreign trading partner

Models (7) & (8) replicate models (5) & (6) but include the GDP/capita gradient as an

alternative proxy for recycling productivity differences. The qualitative results of models (7) and

(8) are the same as models (5) and (6). The negative coefficient on the GDP/capita gradient

indicates that the more productive the recycling sector is in the exporting country relative to the

importing country, the fewer waste and scrap imports are expected to flow to the importing

country. In models (5)-(8) the magnitude of the coefficients on the environmental regulation

gradient increase and the significance of the variables improves. This is not altogether surprising

given the positive correlation of the environmental regulation gradient, recycling wage gradient,

and GDP/capita gradient observed in Table 3. Given that the recycling gradient and

environmental regulation gradient work in opposite directions, omitting the recycling

productivity gradient measure will tend to bias the coefficient on the environmental gradient

downward. Indeed, the larger parameter estimates on the environmental regulation gradient

                                                            20 Due to the loss of observations in the recycling wage gradient variable and the fact that only four countries are not Basel Convention ratifying members, the Basel ratification dummy cannot be separately estimated in models (5) and (6) due to collinearity with the remaining importer and exporter fixed effects.

25  

variable in models (5)-(8) indicate that omitting the recycling gradient variables in models (1)-

(4) biased the environmental gradient estimates downward.

As discussed in Section IV above, the PPML model is a consistent estimator but can

potentially be more efficient with the ZIPPML when there are a large number of zeros in the

bilateral trade values. While the qualitative and quantitative magnitudes of the results in Table 4

are quite similar, the large positive Vuong test statistics indicate that the ZIPPML model

provides a better fit to the data than the PPML model.

Robustness Tests

Table 4 provides strong evidence that the environmental regulation gradient between

countries is an important determinant of international trade in waste. In Table 5, a number of

alternative specifications are run to test the robustness of the results. One concern is that the

environmental regulation index developed from the Global Competitiveness Survey is not

representative of other potential cross country indexes of environmental regulations. In model

(1) of Table 5, the model is estimated using an alternative measure of stringency of

environmental regulation; the Environmental Sustainability Indicator (ESI). The ESI is a cross-

country measure of environmental regulation that takes into account not only measures of

regulation stringency (some of which come from the Global Competitiveness Survey) but also

many components of the environmental quality, political institutions, and environmental

technology levels of the countries. The ESI gradient is calculated in the same fashion as the

environmental regulation gradient21. Using the ESI gradient we see that the elasticity of waste

imports with respect to differences in environmental regulation is even larger. Under this

specification, an importing country that sees a bilateral foreign trading partner increase its

                                                            21 Like models (5) and (6) in Table 4, in model (1) of Table 5 the loss of some country observations when using the ESI index (due to lack of ESI data for countries like Hong Kong) means that the Basel ratification dummy cannot be separately estimated from the importer and exporter fixed effects.

26  

environmental regulations by 1% vis-á-vis the importing country’s environmental regulations

will experience a 0.87% increase in waste imports from the foreign trading partner.

A second potential concern regarding the significance of the environmental regulation

gradient variable is that it might be driven by large players in the international market for waste.

In particular, the fact that China imports (and the U.S. exports) close to 19% of waste worldwide

generates a potential concern that the results might be largely driven by a China as importer (or

U.S. as exporter) effect. In models (2) and (3) of Table 5 these hypotheses are tested by

dropping observations where China is a waste importer in model (2) and dropping observations

where the U.S. is a waste exporter in model (3). The magnitude of the environmental regulation

gradient elasticity does in fact fall when China imports are excluded relative to the full sample

estimation in model (8) of Table 4, indicating that China as an importer of waste does appear to

have an influence on the environmental regulation gradient effect. However, the elasticities in

models (2) and (3) of Table 5 are still positive and significant indicating that although China and

the U.S. play a substantial role, the environmental regulation gradient effect is not entirely driven

by either a ‘China as an importer’ or ‘the U.S. as an exporter’ of waste effect.

Two additional robustness tests are run that are concerned with alternative measures of

the independent variable. While the physical volume of trade is really the relative measure of

interest in thinking about the potential effects of waste trade on environmental quality, model (4)

in Table 5 is estimated using the dollar value of waste traded. In most gravity models of trade it

is the dollar value of trade that is the independent variable. Using the dollar value of waste trade

has no discernable differences when compared to the models using the physical weight of waste

traded22.

                                                            22 This is not surprising given that the weight of waste and the dollar value of waste have a simple correlation of 0.97.

27  

The final robustness check of Table 5 tests the possible concern that the environmental

regulation gradient is picking up other unobserved bilateral characteristics that are a source of

trade in general rather than trade in waste. To test this hypothesis model (5) of Table 5 is

estimated with total net trade (in dollars) as the independent variable. Total net trade is defined

as the total dollar value of all trade between two countries minus the dollar value of waste. The

idea being that we want to test if the environmental regulation gradient has the same effect on all

other goods traded. Since there is no reason to believe that other non-waste traded goods should

flow to the lowest environmental regulation countries, we should expect that the coefficient on

the environmental regulation gradient will be insignificant. If it is not, we could be concerned

that the environmental regulation gradient is picking up some other unobserved bilateral

characteristic of trade in general (rather than a characteristic of waste trade).

In fact, the environmental regulation gradient in model (5) is small and extremely

insignificant. The Basel ratification dummy is also insignificant. Given that the Basel

Convention only concerns trade in hazardous waste, there is no reason to believe that the Basel

dummy would have any effect on trade in other products. Further, the free trade area dummy is

positive and significant and the elasticity with respect to distance is much smaller; now -0.7823.

The positive free trade area dummy, contiguity dummy, and smaller elasticity on the distance

variable are all in line with other trade gravity studies on the dollar value of trade.

Table 6 presents the results of two additional robustness checks. Baggs [2009] finds a

positive and significant effect of the capital/labor ratio for importing countries of hazardous

waste, which implies that factor endowment differences between two countries could be

important for explaining waste trade flows. If waste disposal is capital intensive, we expect that

countries with greater capital/labor ratios than their trading partners will import more waste. If                                                             23 An estimate almost identical to that found by Helpman et al. [2008] for aggregate bilateral trade.

28  

this is the case and it is excluded from the model, then environmental regulation gradient

differences could be biased upwards since capital abundant countries also tend to be countries

with more stringent environmental standards.

In column (1) of Table 6, the base ZIPPML model is repeated with the capital/labor

gradient24 between the two countries included as an additional explanatory variable. The

capital/labor ratio is positive but insignificant while the environmental regulation gradient

remains positive and significant, indicating that differences in environmental regulation have a

significant effect on waste trade flows even after accounting for differences in income and

relative factor endowment differences.

A final concern regarding the environmental gradient effect on waste trade is the

potential problem of endogeneity of environmental policy and waste trade flows. If

environmental policy is used as a form of protection for domestic industry, as is sometimes

suggested, then environmental policy in an industry may be driven more by import penetration

than the other way around. Indeed, Markusen [1975] and Barrett [1994] have shown

theoretically how the stringency of environmental regulation can be decreasing in import

penetration, while Ederington and Minier [2003] and Kellenberg [2009(b)] find empirical

evidence of endogeniety of environmental policy variables with trade and foreign direct

investment flows, respectively.

In contrast to prior papers that have found measures of environmental regulation to be

endogenous to net total trade, there are a number of reasons why the environmental regulation

gradient in the bilateral context of this paper is likely to be exogenous to waste trade flows.

First, in prior work such as Ederington and Minier [2003], where trade flows were found to be

                                                            24 The capital/labor gradient is calculated in the same fashion as the environmental regulation gradient, recycling wage gradient, and the GDP/capita gradient. The number of observations in column (1) of Table 6 is smaller due to the fact that capital stock data was not available for some countries.

29  

endogenously determined with measures of environmental regulation, the explanatory variable

has been a measure of total import intensity into the U.S. from all countries combined. Unlike

tariffs, countries cannot set different domestic environmental standards for different trading

partners. So, while industries in a particular country may be able to influence domestic

environmental regulation in response to aggregate import penetration from all trading countries

(i.e. the endogenity problem between domestic environmental policy and overall import

intensity), they cannot set different domestic environmental regulations for each of their trading

partners. In the context of the bilateral dataset used in this paper, the environmental regulation

gradient is determined for each country pair. Since countries cannot set different environmental

regulations for different trading partners and there is a great deal of variation in waste trade

across bilateral pairs, it is unlikely that the environmental gradient at the bilateral level for any

two trading pairs is being driven by trade patterns between those two country pairs alone. A

second explanation for the exogeneity of the environmental gradient in the bilateral context of

this dataset is that the environmental index used in this paper is not specific to the waste industry,

but to the overall environmental regulations in a country. It is unlikely that the survey responses

to the country’s overall environmental regulation stringency will be driven strictly by waste

industry trade.

For the sake of robustness however, we present the results of an IV-estimator. The

empirical challenge of addressing the endogeneity concern is that IV estimation of non-linear

Poisson models such as the PPML and the ZIPPML have not, to the author’s knowledge, been

established. Although SST and Henderson and Millimet [2008] have made a convincing case for

the PPML approach over the log-linear approach to the gravity model, the log-linear approach

30  

does lend itself to standard IV techniques which can be viewed as a second best effort at testing

for endogeneity concerns.

For comparison purposes, column (2) of Table 6 reports the OLS results of the log-

linearization of equation (2), while column (3) presents both the 1st and 2nd stages of a 2SLS

estimation of the log-linearization of equation (2). Since countries that are relatively capital

abundant tend to be highly correlated with countries that have higher environmental

regulations25, and capital/labor ratios do not have a statistically significant impact on waste trade

flows (as evidenced by column (1) in Table 6), the capital/labor gradient is a good candidate as

an instrument for the environmental regulation gradient. In both the OLS and the 2SLS

specifications, the environmental regulation gradient is negative and insignificant. The

parameter estimates of course should be taken with caution given that the log-linear specification

is likely an inconsistent estimator. Of interest, however, is the fact that the Durbin-Wu-Hausman

test fails to reject the null hypothesis of exogeneity of the environmental regulation gradient,

despite the appropriateness of the instrument as evidenced by the significance of the capital/labor

gradient in the 1st stage and the significant Anderson LR statistic. While this is an admittedly

less than ideal test of endogeneity (we would like to have a PPML-IV estimator), it does give

some assurance that the bilateral environmental regulation gradient can be treated as exogenous

to bilateral waste trade flows.

An Alternative Specification

An alternative specification to estimating bilateral gravity models developed by

Helpman, Melitz, and Rubinstein [2008] (from here on out referred to as HMR), and

subsequently employed by Baggs [2009], is a two stage estimation technique to try and control

for self-selection of firms into output markets. HMR show that not accounting for firm level                                                             25 The correlation in the data between the capital/labor ratio and the environmental regulation index is 0.84.

31  

heterogeneity may bias the distance variable (and potentially other bilateral resistance variables)

upward. The estimation approach is based on a monopolistic competition model where firms

enter export markets only if they have relatively high levels of productivity. A first stage probit

equation is estimated to identify the probability that firms in country j export to country i. The

first stage estimation is intended to account for the proportion of firms that enter the export

market for a bilateral trading pair. To account for this HMR argue that, in addition to bilateral

trade variables that affect marginal costs, the first stage probit should incorporate variables that

affect firms fixed costs of exporting to a country and thus their decision to enter the market.

Three variables presumed to affect a firms fixed costs of entering export markets are included in

the matrix κij in the first stage probit.

The first two variables are country level observations constructed from data in Djankov et

al. [2002] and are the same first stage exclusion variables used by HMR. The variables on

country-level regulation costs are summarized by Helpman et al. [2008, pg. 461]:

…entry costs are measured via their effects on the number of days, the number of legal procedures, and the relative cost (as a percentage of GDP per capita) for an entrepreneur to legally start operating a business. We surmise (and confirm empirically) that they also affect the costs faced by exporting firms to/from that country, and that these costs are magnified when both exporting and importing countries impose high regulatory hurdles. By their nature, these measures affect firm-level fixed rather than variable costs of trade. We therefore construct an indicator for high-fixed cost trading pairs, consisting of country pairs in which both the importing and exporting countries have entry regulation measure above the cross-country median. One variable uses the sum of the number of days and procedures above the median (for both countries whereas the other uses the sum of the relative costs above the median (again for both countries). By construction, these bilateral variables reflect regulation costs that should not depend on a firm’s volume of exports to a particular country, and therefore satisfy the requisite exclusion restrictions.

The third variable that is likely to affect the decision of firms to enter export markets is

the A7/Ban3 to non-A7 indicator variable described above. Firms in Annex 7 countries that have

32  

domestically ratified the Ban Amendment face a fixed cost of deciding to break the law by

exporting to non-Annex 7 countries. In this respect, the Ban Amendment for these bilateral pairs

is likely to affect firm entry decisions (to break the law or not) more than their marginal decision

of how much to export (they’ve already accepted the fixed costs of breaking the law).

Letting Iij be an indicator variable if country j exports to country i, Φ be the c.d.f. of the

unit normal distribution, and φ a vector of parameters to be estimated, the predicted probability

that country j exports to country i, , can be written ijz

( )ϕijijijijijij Iz κβxκx +Φ== ),Pr(ˆ . (10)

From the first stage probit equation in (10), HMR construct two variables to correct for

selection bias and firm heterogeneity to be used in the second stage estimation. The first is the

inverse mills ratio, ijη , or Heckman [1979] correction, to control for countries that do not trade

waste with one another (i.e. the zeros problem). The second variable, wij, is a proxy for the

proportion of firms in country j that export to country i and is specified as a polynomial

approximation26 using the predicted probabilities, , from the first stage probit regression ijz

( ) ( )332

2 ˆˆˆ ijzijzijzij zzzw βββ ++= . (11)

The second stage estimation model is specified as

, (12) ijijuijijij ewT +++= ηβ η ˆln βx

where eij is an additive error term and assumed to be i.i.d. Equation (12) is estimated using

standard OLS with wij given by equation (11).

                                                            26 HMR also specify a non-linear parameterization of wij and estimate the model using non-linear least squares. The non-linear parameterization was also estimated here but is not reported). Like HMR, it yielded similar results to the much simpler to implement polynomial specification. The polynomial specification has the further advantage of being adapted to a hybrid estimator to be explained later in this section.

33  

Table 7 reports the estimation results of the alternative specification. In column (1), a

standard OLS model without the Heckman correction ( ijη ) or the firm selection variable (wij) is

reported for comparison purposes. Column (2) reports the first stage probit results, while the

second stage estimation of equation (12) is reported in column (3). Like the standard OLS

regression in column (1), where only country pairs with positive waste trade are used, the second

stage polynomial results in column (3) show a negative and insignificant effect of the

environmental regulation gradient and the GDP/capita gradient. Like HMR, the parameter

estimates on distance and other right hand side variables, such as contiguity and the colonial

dummy, are smaller than under the OLS specification, an indication that the true effect of these

variables is biased upward by excluding the indirect effect of the proportion of exporting firms

(wij). In fact, the firm selection variable, wij, indicates that waste trade is increasing in the

number of firms that enter the export market (given by the positive and significant coefficient on

z_hat), albeit at a decreasing rate (a negative z_hat2 coefficient). The coefficient on the inverse

mills ratio, ijη , is positive and significant. With 62% of the bilateral waste trade observations in

the dataset containing zeros it is not surprising that the Heckman correction effect is substantial.

However, the polynomial regression in column (3) produces a counterintuitive result of a

negative and significant effect on the free trade area dummy. All else equal, countries with

fewer trade barriers should trade more with one another, not less.

Of primary concern is the difference in the magnitude and significance of the

environmental regulation gradient between the Poisson specifications in Tables 4 and 5 and the

alternative two-step specification in Table 7. There are two likely possibilities for the

discrepancy. First, the bilateral cost and bilateral regulation variables used to identify firm

selection in the first stage probit are either insignificant (Bilateral Cost) or significant but of the

34  

wrong expected sign (Bilateral Regulation), indicating that while these variables might have

explanatory power for trade overall, they may not be good instruments for the waste trade

industry. The model was run again with just the A7/Ban3 to non-A7 dummy as the excluded

variable and the second stage results (not reported) were virtually identical27.

The second possibility has to do with potential inconsistency of the second stage

estimator if eij in equation (12) are not independent of the right-hand side covariates. Indeed, the

similarity of the OLS results in column (1), which are shown by Santos Silva and Tenreyro

[2006] to be inconsistent for this reason, and the two-step polynomial results in column (3) are

suggestive. The HMR two step estimator is intended to control for both country pair selection

(via the Heckman correction) and bias due to unobserved firm selection into export markets.

However, the method still assumes independence of the errors in the second stage, precisely the

point that Santos Silva and Tenreyro [2006] show can create a problem for consistency of the

estimators if the assumption is not valid28.

The PPML model presented in this paper is a consistent non-linear estimator that controls

for the ‘zeros’ problem for bilateral trade pairs, but to this point has not controlled for the

potential bias due to unobserved firm level heterogeneity suggested by HMR. To correct for

this, it is straightforward to consider a hybrid estimation technique that combines the HMR two

stage estimator and the PPML estimator. To do so, define the conditional mean structure for the

                                                            27 Likewise, the A7/Ban3 to non-A7 dummy was excluded and only the Bilateral Cost and Bilateral Regulation variables were used in the first stage but the second stage results remained predominantly unchanged. 28 The assumption of independence of the eij from the xij for the alternative NLS parameterization in HMR (rather than the polynomial specification) likewise is “often much too strong” (Woolridge [2002, pg. 342]), particularly when the independent variable is constrained to be greater than or equal to zero.

35  

Poisson model by taking the exponent of both sides of equation (12), excluding the inverse mills

ratio and additive error terms29, to obtain

( )ijijijijijij wzTE +== βxx exp)ˆ,(μ , (13)

which is analogous to the mean structure in equation (3). To estimate the hybrid two-stage

PPML we simply run the first stage probit defined by equation (10) to obtain the estimates of ,

and then estimate the PPML in the second stage using equation (13) as the conditional mean.

This approach should correct for the ‘zeros’ problem, any bias due to unobserved firm level

heterogeneity, and any inconsistency due to correlation between the error term and the right-hand

side covariates. It also has the added advantage of using the full dataset in the second stage of

estimation (rather than the polynomial estimator in column (3) which drops 62% of the

observations in the second stage).

ijz

The results of this hybrid approach are reported in column (4) of Table 7. Like the PPML

model in column (7) of Table 4, the environmental regulation gradient is positive and significant,

while the GDP/capita gradient is negative and significant. The counter-intuitive negative and

significant result on the free trade area dummy found in the polynomial regression is now

positive but insignificant. Importantly, the coefficients on the first two polynomial terms in

column (4) are significant and of the expected sign. Waste trade is increasing (at a decreasing

rate) in the proportion of firms that enter the export market. Like HMR, including the terms

has the effect of reducing the upward bias on the estimate of the distance term; in this case,

lowering the distance estimate from -1.55 (model (7) of Table 4) to -1.43 (model (4) in Table 6).

ijz

zijˆ

                                                            29 The inverse mills ratio term is redundant since the PPML estimator controls for the ‘zeros’ problem from country pairs that do not trade and the additive error term is dropped to relax the assumption that the error is independent of the right-hand side covariates.

36  

The parameter estimate on the Basel ratification dummy is also lower in the PPML regression

when controlling for firm entry, falling to -1.88 (from -2.06 in column (7) of Table 4).

IV. Conclusions

Trade in international waste is a growing business with competing ramifications for

international environmental objectives. On one hand, trade in waste products destined for

countries that are capable of safely handling and recycling material can have positive effects on

environmental quality and efficient use of resources by reducing the demand for virgin materials.

On the other hand, trade in waste that flows to countries with low environmental regulations to

be recycled or discarded in an environmentally unsafe manner can create severe environmental

problems for those countries.

This paper tests the hypothesis that differences in environmental regulation across

countries is an important determinant of trade in international waste. It is found that for every

1% that a home country’s environmental regulations deteriorate vis-á-vis a foreign bilateral

trading partner, the home country will experience a 0.32% increase in waste imports from the

foreign trading partner. This is a substantial effect when one considers that the average

developing country has an environmental regulation index that is 39% lower than the average

developed country’s environmental regulation index. While much of the literature has focused

on the potential for pollution havens, or the movement of productive resources in dirty industries

to countries with lax environmental regulations, the sheer quantity of waste traded internationally

and the robust effect that environmental regulation differences play in these trade flows suggests

that waste havens may be a much more problematic and under researched global environmental

concern.

37  

A number of countries have joined international efforts to reduce the export of hazardous

waste to countries that are ill equipped to safely recycle or dispose of the material. The Basel

Convention on the international transport of hazardous waste has been the primary international

treaty for addressing the problem, but to date, there have been no prior studies that have

examined waste trade for bilateral trading countries that have ratified the Basel Convention

relative to those that have not. This paper is the first to explicitly estimate the effect of

ratification of the Basel Convention on the international trade of waste. The empirical results

suggest a strong correlation between decreased waste trade and those bilateral trading pairs when

both countries have ratified the Basel Convention. This has important potential policy

implications for large waste trading countries such as the U.S. or Hong Kong who are

considering strategies to reduce waste trade to developing countries but have not yet ratified the

Basel Convention.

The results here suggest that there is a strong effect of differences in environmental

regulations across countries for determining waste trade internationally. Although a substantial

portion of international waste is imported by developed countries with high environmental

regulations, developing countries with much lower regulations import a disproportionately large

share of the world’s waste. The results of this paper provide compelling evidence that

differences in environmental regulations across countries is an important determinant of waste

trade and that there exists the real possibility of international waste haven effects. Future work

in this area should explore waste trade in a panel data context to better understand the causal

mechanisms between Basel ratification and specific components of waste trade. From an

environmental perspective, there is much anecdotal evidence to suggest that developing countries

that are large waste importers experience deteriorating environmental conditions, yet this result

38  

has not been formally tested empirically and should be a subject of future research to better

understand the social welfare effects of waste trade.

39  

References

Anderson, James E., and Douglas Marcouiller, 2002, Insecurity and the Pattern of Trade: An

Empirical Investigation, Review of Economics and Statistics, 84(2), 342-52.

Anderson, J., and E. van Wincoop, 2003, Gravity with Gravitas: A Solution to the Border

Puzzle, American Economic Review, 93, 170-192.

Antweiler, Werner, Brian R. Copeland, and M. Scott Taylor, 2001, Is Free Trade Good for the

Environment?, American Economic Review, 91(4), 877-908.

Baier, Scott and Jeffrey H. Bergstrand, 2007, Do free trade agreements actually increase

members’ international trade?, Journal of International Economics, 71(1), 72—95.

Baier, Scott and Jeffrey H. Bergstrand, 2009, Bonus vetus OLS: A simple method for

approximating international trade-cost effects using the gravity equation, Journal of

International Economics, 77(1), 77-85.

Balistreri, Edward, and Russell H. Hillbery, 2007, Structural Estimation and the Border Puzzle,

Journal of International Economics, 72(2), 451-463.

Barrett, Scott, 1994, Strategic environmental policy and international trade, Journal of Public

Economics, 54, 325-38.

Barry, Dan, 2002, Sifting the last tons of Sept. 11 debris, The New York Times, May 14: pA1(N)

pA1 (L).

Berthelon, Matias and Caroline Freund, 2008, On the conservation of distance in international

trade, Journal of International Economics, 75(2), 310-20.

Blomberg, S. Brock, and Gregory D. Hess, 2006, How Much Does Violence Tax Trade?,

Review of Economics and Statistics, 88(4), 599-612.

Blonigen, Bruce A., Ronald B. Davies, and Keith Head, 2003, Estimating the Knowledge-

Capital Model of the Multinational Enterprise: Comment, American Economic Review,

93(3), 980-94.

Cameron, A. Colin and Pravin K. Trivedi, 1986, Econometric Models Based on Count Data:

Comparisons and Applications of Some Estimators and Tests, Journal of Applied

Econometrics, 1(1), 29-53.

Carr, David L., James R. Markusen, and Keith E. Maskus, 2001, Estimating The Knowledge-

Capital Model of the Multinational Enterprise, American Economic Review, 91(3), 995-

1001.

40  

Chintrakarn, Pandej, and Daniel L. Millimet, 2006, The environmental consequences of trade:

Evidence from subnational trade flows, Journal of Environmental Economics and

Management, 52(1), 430-453.

Cole, Matthew A., 2004, Trade, the Pollution Haven Hypothesis and the Environmental Kuznets

Curve: Examining the Linkages, Ecological Economics, 48(1), 71-81.

Cole, Matthew A. and Robert J.R. Elliott, 2003, Determining the Trade Environment

Composition Effect: The Role of Capital, Labor, and Environmental Regulations, Journal

of Environmental Economics and Management, 46(3), 363-383.

Copeland, Brian R. and M. Scott Taylor, 1999, Trade, Spatial Separation, and the Environment,

Journal of International Economics, 47(1), 137-168.

Dean, Judith M., 2002, Does trade liberalization harm the environment? A new test, Canadian

Journal of Economics, 35(4), 819-842.

Djankov, Simeon, Rafael La Porta, Florencio Lopez-de-Silanes, and Andrei Shleifer, 2002, The

Regulation of Entry, Quarterly Journal of Economics, 117(1), 1-37.

Ederington, Josh, Arik Levinson, and Jenny Minier, 2004, Trade Liberalization and Pollution

Havens, Advances in Economic Policy & Analysis, 4(2), Article 6.

Ederington, Josh, Arik Levinson, and Jenny Minier, 2005, Footloose and Pollution-Free, The

Review of Economics and Statistics, 87(1), 92-99.

Ederington, Josh, and Jenny Minier, 2003, Is environmental policy a secondary trade barrier? An

empirical analysis, Canadian Journal of Economics, 36(1), 137-154.

Ekholm, Karolina, Rikard Forslid, and James R. Markusen, 2007, Export Platform Foreign

Direct Investment, Journal of the European Economic Association, 5(4), 776-95.

Eskeland, Gunnar S. and Ann E. Harrison, 2003, Moving to greener pastures? Multinationals and

the pollution haven hypothesis, Journal of Development Economics, 70, 1-23.

Feenstra, R., J. Markusen, and A. Rose, 2001, Using the Gravity Equation to Differentiate among

Alternative Theories of Trade, Canadian Journal of Economics, 34, 430-447.

Frankel, J. and A. Rose, 2002, An Estimate of the Effect of Common Currencies on Trade and

Income, Quarterly Journal of Economics, 117, 409-466.

Frankel, Jeffrey A., and Andrew K. Rose, 2005, Is Trade Good or Bad for the Environment?

Sorting out the Causality, The Review of Economics and Statistics, 87(1), 85-91.

41  

Fredriksson, Per G., John A. List, and Daniel L. Millimet, 2003, Bureaucratic corruption,

environmental policy and inbound US FDI: theory and evidence, Journal of Public

Economics, 87, 1407-1430.

Grossman, Elizabeth, 2006, High Tech Trash: Digital Devices, Hidden Toxics, and Human

Health, Island Press, Washington, DC.

Hallak, J.C., 2006, Product Quality and the Direction of Trade, Journal of International

Economics 68(1), 238-65.

Hanson, Gordon H., and Chong Xiang, 2004, The Home-Market Effect and Bilateral Trade

Patterns, American Economic Review, 94(4), 1464-91.

Heckman, James J., 1979, Sample Selection Bias as a Specification Error, Econometrica, 47,

153-61.

Helpman, Elhanan, Marc Melitz, and Yona Rubinstein, 2008, Estimating Trade Flows: Trading

Partners and Trading Volumes, Quarterly Journal of Economics, 123(2), 441-87.

Henderson, D.J. and D.L. Millimet, 2008, Is Gravity Linear?, Journal of Applied Econometrics,

23, 137-172.

Javorcik, Beata Smarzynska & Shang-Jin Wei, 2004, Pollution Havens and Foreign Direct

Investment: Dirty Secret or Popular Myth? Contributions to Economic Analysis &

Policy, 3(2), Article 8.

Kahn, Matthew E., and Yutaka Yoshino, 2004, Testing for Pollution Havens Inside and Outside

of Regional Trading Blocs, Advances in Economic Analysis & Policy, 4(2), Article 4.

Kellenberg, Derek, 2008, A Reexamination of the Role of Income for the Trade and

Environment Debate, Ecological Economics, 68(1), 106-115.

Kellenberg, Derek, 2009(a), U.S. Affiliates, Infrastructure, and Growth: A Simultaneous

Investigation of Critical Mass, The Journal of International Trade and Economic

Development, 18(3), 311-45.

Kellenberg, Derek, 2009(b), An Empirical Investigation of the Pollution Haven Effect with

Strategic Environment and Trade Policy, Journal of International Economics, 78(2), 242-

255.

Keller, Wolfgang and Arik Levinson, 2002, Pollution Abatement Costs and Foreign Direct

Investment Inflows to U.S. States, The Review of Economics and Statistics, 84(4), 691-

703.

42  

Levinson, Arik, 1999(a), NIMBY taxes matter: the case of state hazardous waste disposal taxes,

Journal of Public Economics, 74(1), 31-51.

Levinson, Arik, 1999(b), State Taxes and Interstate Hazardous Waste Shipments, American

Economic Review, 89(3), 666-677.

Long, J. Scott, 1997, Regression Models for Categorical and Limited Dependent Variables, Sage

Publications, Thousand Oaks, CA.

Markusen, J.R., 1975, International externalities and optimal tax structures, Journal of

International Economics, 5, 15-29.

Markusen , J. R. and K. E. Maskus, 2002, Discriminating among Alternative Theories of the

Multinational Enterprise, Review of International Economics , 10(4) , 694–707.

McAusland, Carol, 2008, Trade, politics, and the environment: Tailpipe vs. smokestack, Journal

of Environmental Economics and Management, 55(1), 52-71.

Pellow, David Naguib, 2007, Resisting Global Toxics: Transnational Movements for

Environmental Justice, MIT Press, Cambridge, MA.

Puckett, Jim & Ted Smith, Eds., 2002, Exporting Harm: The High-Tech Trashing of Asia,

http://www.ban.org/E-waste/technotrashfinalcomp.pdf.

Puckett, Jim, Ed., 2005, The Digital Dump: Exporting Re-use and Abuse to Africa,

http://www.ban.org/BANreports/10-24-05/documents/TheDigitalDump_Print.pdf.

Ray, Amit, 2008, Waste Management in Developing Asia: Can Trade and Cooperation Help?,

The Journal of Environment and Development, 17(1): 3-25.

Rose, Andrew K., 2004, Do We Really Know That the WTO Increases Trade?, American

Economic Review, 94(1), 98-114.

Rose, Andrew K., 2007, Do We Really Know That the WTO Increases Trade? Reply, American

Economic Review, 97(5), 2019-25.

Santos Silva, J.M.C & Silvana Tenreyro, 2006, The Log of Gravity, The Review of Economics

and Statistics, 88(4): 641-658.

Sigman, Hilary, 1996, The Effects of Hazardous Waste Taxes on Waste Generation and

Disposal, Journal of Environmental Economics and Management, 30(2), 199-217.

Sigman, Hilary, 1998, Midnight dumping: public policies and illegal disposal of used oil, RAND

Journal of Economics, 29(1), 157-178.

43  

Stock, James H., and Motohiro Yogo, 2002, Testing for Weak Instruments in Linear IV

Regression, NBER Working Paper 284.

Tang, Donny, 2005, Effects of the Regional Trading Arrangements on Trade: Evidence from the

NAFTA, ANZCER and ASEAN Countries, 1989-2000, 14(2), 241-265.

The Global Competitiveness Report 2003-2004, 2004, Oxford University Press, Geneva.

Tinbergen, Jan, 1962, The World Economy. Suggestions for an International Economic Policy,

New York: Twentieth Century Fund.

Tomz, Michael, Judith L. Goldstein, and Douglas Rivers, 2007, Do We Really Know That the

WTO Increases Trade? Comment, American Economic Review, 97(5), 2005-18.

United States Government Accountability Office (USGAO), 2008, Electronic Waste: EPA

Needs to Better Control Harmful U.S. Exports through Stronger Enforcement and More

Comprehensive Regulation, GAO-08-1044.

Vuong, Quang H., 1989, Likelihood Ratio Tests for Model Selection and Non-nested

Hypotheses, Econometrica, 57(2), 307-33.

Winkelmann, Rainer, 2008, Econometric Analysis of Count Data, 5th ed., Springer, Berlin,

Germany.

Yeaple, Stephen R., 2003, The Role of Skill Endowments in the Structure of U.S. Outward

Foreign Direct Investment, Review of Economics and Statistics, 85(3), 726-34.

44  

Figure 1

Domestic Waste (Country i) 

Recycle Domestically (Country i) 

Dispose Domestically (Country i) 

Waste Exports (Country i to ∑Countries –i) 

Foreign Recycling (∑Countries ‐i) 

Foreign Disposal  (∑Countries –i) 

(a) 

(b)  (c) 

(i) 

(d) 

(e) 

(f) 

Domestic Economy (Country i) 

(g)  (h) 

45  

Largest exportersUnited States 28,000,000 18.69 27.3Germany 16,700,000 11.15 32.5Russia 11,300,000 7.54 15.8Japan 11,000,000 7.34 28.1Great Britain 10,700,000 7.14 28.2France 10,000,000 6.68 28.5Netherlands 8,910,000 5.95 30.8Canada 6,910,000 4.61 28.6Belgium 5,730,000 3.83 28.2Hong Kong 4,150,000 2.77 22.3

Total (Average) 113,400,000 75.71 (27.03)

Largest importersChina 28,800,000 19.23 17.9Turkey 11,400,000 7.61 16.2Korea 10,700,000 7.14 22.5Germany 9,830,000 6.56 32.5Spain 8,240,000 5.50 23.6Italy 8,170,000 5.45 24.7Belgium 7,990,000 5.33 28.2United States 7,090,000 4.73 27.3Netherlands 7,000,000 4.67 30.8France 6,420,000 4.29 28.5

Total (Average) 105,640,000 70.53 (25.22)

Largest importersChina 28,800,000 19.23 17.9Turkey 11,400,000 7.61 16.2Mexico 3,869,000 2.58 20India 3,520,000 2.35 17.8Thailand 2,690,000 1.80 20Malaysia 2,510,000 1.68 22.8Pakistan 472,000 0.32 14.6Vietnam 434,000 0.29 14.8Guatemala 209,000 0.14 11.5Colombia 202,000 0.13 18.3

Total (Average) 54,106,000 36.12 (17.39)

% of world waste imports

Environmental Regulation Index

Table 1(a): Top 10 Largest Exporters of Waste

Tons of Waste% of world

waste exports Environmental

Regulation Index

Table 1(c): Top 10 Developing Country Importers of Waste

Tons of Waste% of world

waste imports Environmental

Regulation Index

Table 1(b): Top 10 Largest Importers of Waste

Tons of Waste

Share of World Income

Share of World Waste Exports

Share of World Waste Imports

Environmental Reg. Index (Average)

Developed Countries 78.5% 79.2% 61.3% 27.7Developing Countries 21.5% 20.8% 38.7% 16.8

Table 2: Income, Waste Trade, and Environmental Regulation by Development Group

Environmental regulation gradient 1.00ESI gradient 0.45 1.00GDP/capita gradient 0.83 0.48 1.00Recycle wage gradient 0.81 0.50 0.92 1.00

Table 3: Correlations of Environment and Income VariablesEnvironmental

regulation gradient ESI gradientGDP/capita

gradientRecycle wage

gradient

46  

(1) (3) (5) (7)PPML ZIPPML Inflate PPML ZIPPML Inflate PPML ZIPPML Inflate PPML ZIPPML Inflate

Environmental reg. gradient 0.224* 0.272** 0.009 0.223* 0.271** 0.009 0.621*** 0.637*** -0.020 0.278** 0.322*** 0.016(0.128) (0.130) (0.026) (0.128) (0.130) (0.026) (0.236) (0.237) (0.045) (0.109) (0.111) (0.029)

Recycle wage gradient -0.013*** -0.013*** 0.001(0.005) (0.005) (0.001)

GDP/capita gradient -0.034*** -0.035*** -0.001(0.007) (0.007) (0.001)

Basel ratification dummy -2.464*** -1.736*** 0.032 -2.064** -1.640** 0.022(0.925) (0.608) (0.805) (0.903) (0.717) (0.802)

Free trade area dummy 0.081 0.106 -0.109 0.082 0.106 -0.109 0.418 0.442 -0.167 0.109 0.142 -0.111(0.227) (0.225) (0.093) (0.227) (0.225) (0.093) (0.347) (0.346) (0.137) (0.207) (0.204) (0.094)

Ln[Distance] -1.630*** -1.540*** 1.095*** -1.629*** -1.539*** 1.095*** -1.356*** -1.305*** 1.082*** -1.550*** -1.448*** 1.100***(0.135) (0.142) (0.054) (0.135) (0.142) (0.054) (0.154) (0.155) (0.079) (0.117) (0.121) (0.055)

Contiguity dummy 0.732*** 0.787*** -1.273*** 0.732*** 0.787*** -1.273*** 1.061*** 1.074*** -1.043*** 1.035*** 1.102*** -1.245***(0.252) (0.251) (0.258) (0.252) (0.251) (0.258) (0.233) (0.231) (0.382) (0.177) (0.177) (0.257)

Common language dummy 0.426** 0.413** -0.483*** 0.425** 0.413** -0.483*** 0.403* 0.401* -0.654*** 0.316* 0.296* -0.472***(0.208) (0.205) (0.082) (0.208) (0.205) (0.082) (0.231) (0.229) (0.141) (0.183) (0.178) (0.083)

Colonial dummy 0.059 0.043 -0.842*** 0.059 0.043 -0.842*** 0.135 0.127 -1.001*** 0.101 0.095 -0.846***(0.247) (0.242) (0.213) (0.247) (0.242) (0.213) (0.219) (0.216) (0.309) (0.219) (0.214) (0.213)

A7/Ban3 to non-A7 dummy 0.220** 0.220** 0.302** 0.231**(0.110) (0.110) (0.151) (0.110)

Observations 7,644 7,644 7,644 7,644 3,477 3,477 7,644 7,644log-liklihood -5.50E+07 -5.19E+07 -5.49E+07 -5.18E+07 -3.54E+07 -3.40E+07 -4.95E+07 -4.62E+07

Vuong test statistic 35.69*** 35.72*** 26.35*** 33.87***Robust standard errors in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%

Table 4: Waste Trade Regressions(2) (4) (8)(6)

47  

(5)Net Total Trade ($)

ZIPPML Inflate ZIPPML Inflate ZIPPML Inflate ZIPPML Inflate PPMLEnvironmental reg. gradient 0.173* -0.002 0.261** 0.014 0.329*** 0.003 -0.029

(0.106) (0.029) (0.118) (0.029) (0.096) (0.029) (0.142)ESI gradient 0.869** 0.369**

(0.378) (0.155)GDP/capita gradient -0.037*** -0.002 -0.015*** 0.001 -0.039*** -0.001 -0.020*** 0.000 0.025***

(0.008) (0.001) (0.006) (0.001) (0.007) (0.001) (0.006) (0.001) (0.007)Basel ratification dummy -1.245 0.053 -1.788*** 0.055 -1.313** -0.433 0.160

(1.258) (0.809) (0.565) (0.786) (0.642) (0.781) (0.199)Free trade area dummy -0.014 -0.124 0.070 -0.101 0.088 -0.121 0.305 -0.070 0.394***

(0.233) (0.103) (0.201) (0.094) (0.215) (0.094) (0.200) (0.087) (0.069)Ln[Distance] -1.548*** 1.132*** -1.411*** 1.103*** -1.342*** 1.094*** -1.137*** 1.134*** -0.788***

(0.135) (0.060) (0.116) (0.055) (0.110) (0.055) (0.103) (0.051) (0.031)Contiguity dummy 1.034*** -1.166*** 1.384*** -1.240*** 1.136*** -1.263*** 0.781*** -1.476*** 0.296***

(0.186) (0.268) (0.178) (0.255) (0.171) (0.256) (0.163) (0.253) (0.058)Common language dummy 0.141 -0.509*** 0.362** -0.470*** 0.386** -0.473*** 0.263* -0.463*** 0.192***

(0.230) (0.092) (0.178) (0.082) (0.179) (0.084) (0.158) (0.080) (0.062)Colonial dummy 0.168 -0.831*** 0.031 -0.846*** 0.362** -0.843*** 0.152 -0.855*** -0.040

(0.248) (0.222) (0.206) (0.213) (0.160) (0.213) (0.200) (0.219) (0.098)A7/Ban3 to non-A7 dummy 0.318*** 0.218** 0.235** 0.207*

(0.121) (0.110) (0.110) (0.110)Observations 6,715 7,553 7,561 7,644 7,644log-liklihood -4.10E+07 -3.96E+07 -4.10E+07 -1.64E+10 -8.70E+11Vuong test statistic 30.53*** 34.91*** 34.17*** 37.05*** -2.55Robust standard errors in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%

CHN IM Dropped USA EX Dropped Waste Trade ($)

Table 5: Waste Trade Robustness Regressions(1) (2) (3) (4)

48  

49  

(2)ZIPPML Inflate OLS 2SLS 1st Stage

Environmental reg. gradient 0.278** 0.021 -0.037 -0.372(0.127) (0.032) (0.089) (0.564)

GDP/capita gradient -0.0165** 0.000 0.011 0.013 0.006***(0.008) (0.003) (0.007) (0.010) (0.002)

Capital/labor gradient 0.687 -0.113 -0.317 0.945***(0.859) (0.228) (0.573) (0.146)

Basel ratification dummy -1.940* -0.126 0.601 0.599 -0.005(1.062) (0.795) (1.555) (1.487) (0.407)

Free trade area dummy 0.563*** -0.175 -0.008 -0.054 -0.137**(0.215) (0.114) (0.216) (0.218) (0.055)

Ln[Distance] -1.288*** 1.111*** -2.418*** -2.429*** -0.031(0.126) (0.066) (0.100) (0.101) (0.027)

Contiguity dummy 1.235*** -1.338*** 0.803*** 0.794*** -0.024(0.190) (0.336) (0.257) (0.270) (0.074)

Common language dummy 0.153 -0.511*** 0.432** 0.435** 0.009(0.184) (0.088) (0.195) (0.182) (0.050)

Colonial dummy 0.275 -0.728*** 1.035*** 1.035*** 0.002(0.237) (0.239) (0.264) (0.293) (0.080)

A7/Ban3 to non-A7 dummy -0.024(0.140)

Observations 5,544 2,206 2,206 2,206log-liklihood -3.30E+07Vuong test statistic 28.91***Durbin-Wu-Hausman test (χ2) 0.349Anderson LR stat 44.74***F-test of excluded instuments 41.98***

Robust standard errors in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%

Table 6: Capital/Labor Gradient and IV Waste Trade Robustness Regressions

(1) (3)

(1) (2) (3) (4)OLS Probit Polynomial PPML

Environmental regulation gradient -0.027 -0.005 -0.054 0.325***(0.079) (0.030) (0.077) (0.117)

GDP/capita gradient 0.002 0.000 0.001 -0.035***(0.003) (0.001) (0.003) (0.007)

Basel ratification dummy 0.643 -0.037 0.272 -1.876**(1.634) (0.557) (1.514) (0.829)

Free trade area dummy -0.487** 0.102 -0.620*** 0.125(0.196) (0.086) (0.192) (0.215)

Ln[Distance] -2.513*** -1.100*** -1.882*** -1.433***(0.092) (0.048) (0.135) (0.134)

Contiguity dummy 1.428*** 1.288*** 0.811*** 1.058***(0.208) (0.215) (0.211) (0.175)

Common language dummy 0.281 0.466*** 0.116 0.316*(0.184) (0.082) (0.183) (0.175)

Colonial dummy 1.317*** 0.837*** 0.752*** 0.018(0.225) (0.199) (0.238) (0.222)

Bilateral Regulation 0.185**(0.093)

Bilateral Cost 0.112(0.093)

A7/Ban3 to non-A7 dummy -0.213*(0.110)

eta_hat 7.053***(1.179)

z_hat 4.275*** 0.809***(0.692) (0.213)

(z_hat)2 -0.550*** -0.201**(0.151) (0.096)

(z_hat)3 0.006 0.018(0.012) (0.012)

Observations 2,861 7,644 2,861 7,644R-squared 0.61 0.63log-liklihood -4.81E+07Robust standard errors in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%

Table 7: Alternative Waste Regression Specifications

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Appendix

hscode Commodity Description hscode Commodity Description251720 Macadam of slag/dross/sim. industrial waste 520210 Yarn waste (incl. thread waste), of cotton252530 Mica waste 520299 Cotton waste other than yarn waste 261900 Slag, dross (excl. granulated slag), scalings & oth. waste from mfr. 550510 Waste (incl. noils, yarn waste & garnetted stock) of synth. fibres262110 Ash & residues from the incineration of municipal waste 550520 Waste (incl. noils, yarn waste & garnetted stock) of art. fibres271091 Waste oils cont. polychlorinated biphenyls (PCBs) 711291 Waste & scrap of gold, incl. metal clad with gold 271099 Waste oils other than those cont. polychlorinated biphenyls (PCBs) 711299 Waste & scrap of precious metal/metal clad with precious metal300680 Waste pharmaceuticals 720410 Waste & scrap of cast iron382510 Municipal waste 720421 Waste & scrap of stainless steel382530 Clinical waste 720429 Waste & scrap of alloy steel other than stainless steel382541 Halogenated waste organic solvents 720430 Waste & scrap of tinned iron/steel382549 Waste organic solvents other than halogenated waste organic solvents 720441 Ferrous turnings, shavings, chips, milling waste, sawdust, filings382550 Wastes of metal pickling liquors, hydraulic fluids, brake fluids, etc. 720449 Ferrous waste & scrap (excl. of 7204.10-7204.41)382561 Wastes from chem./allied industries, mainly cont. organic constituents 740400 Copper waste & scrap382569 Wastes from chem./allied industries, n.e.s. in Ch.38 750300 Nickel waste & scrap382590 Residual prods. of the chem./allied industries, n.e.s. in Ch.38 760200 Aluminium waste & scrap391510 Waste, parings & scrap, of polymers of ethylene 780200 Lead waste & scrap391520 Waste, parings & scrap, of polymers of styrene 790200 Zinc waste & scrap391530 Waste, parings & scrap, of polymers of vinyl chloride 800200 Tin waste & scrap391590 Waste, parings & scrap, of plastics n.e.s. in 39.15 810197 Tungsten (wolfram) waste & scrap400400 Waste, parings & scrap, of rubber (excl. hard rubber) 810297 Molybdenum waste & scrap411520 Parings & oth. waste of leather/composition leather, not suit. for mfr. 810330 Tantalum waste & scrap440130 Sawdust & wood waste & scrap 810420 Magnesium waste & scrap450190 Waste cork; crushed/granulated/ground cork 810530 Cobalt waste & scrap470710 Recovered (waste & scrap) unbleached kraft paper/paperboard 810600 Bismuth & arts. thereof , incl. waste & scrap470720 Recovered (waste & scrap) paper/paperboard mainly of bleached chem. 810730 Cadmium waste & scrap470730 Recovered (waste & scrap) paper/paperboard made mainly of mech. Pulp 810830 Titanium waste & scrap470790 Recovered (waste & scrap) paper/paperboard (excl. of 4707.10-4707.30) 810930 Zirconium waste & scrap500310 Silk waste (incl. cocoons unsuit. for reeling, yarn waste & garnetted stock) 811020 Antimony waste & scrap500390 Silk waste (incl. cocoons unsuit. for reeling, yarn waste & garnetted stock) 811213 Beryllium waste & scrap510320 Waste of wool/of fine animal hair, incl. yarn waste 811222 Chromium waste & scrap510330 Waste of coarse animal hair 854810 Waste & scrap of primary cells, primary batteries

Table A1:  Waste by HS Code

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Australia Germany Lithuania SloveniaAustria Greece Luxembourg SpainBelgium Hungary Mexico SwedenCanada Iceland Netherlands SwitzerlandCzech Rep. Ireland New Zealand TurkeyDenmark Italy Norway United KingdomEstonia Japan Poland USAFinland Korea PortugalFrance Latvia Slovakia

Algeria Guatemala Mauritius SingaporeArgentina Honduras Morocco South AfricaBangladesh Hong Kong Mozambique Sri LankaBolivia India Namibia ThailandBrazil Indonesia Nicaragua Trinidad and TobagoBulgaria* Israel Nigeria TunisiaChile Jamaica Pakistan UgandaChina Jordan Panama UkraineColombia Kenya Paraguay UruguayCosta Rica Macedonia Peru VenezuelaCroatia Madagascar Philippines Viet NamDominican Rep. Malawi Romania* ZambiaEcuador Malaysia Russia ZimbabweEl Salvador Mali SenegalEthiopia Malta Serbia and Montenegro

Table A2: Country ListAnnex 7 Countries

Non-Annex 7 Countries

*Bulgaria and Romania did not acced to the EU, and thus become Annex 7 nations, unitl 2007.

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Variable Survey Question

Chemical waste regulations

Consistency of Regulation Enforcement

Environmental regulation in your country is: (1 = not enforced orenforced erratically, 7 = enforced consistently and fairly)

The regulations concerning chemicals used in manufacturing in yourcountry are: (1 = lax when compared with those of most othercountries, 7 = among the world's most stringent)

Toxic waste disposal regulations The toxic waste disposal regulations in your country are: (1 = laxwhen compared with those of most other countries, 7 = among theworld's most stringent)

Table A3: Questions Determining Variables from the Global Competitiveness Report

The air pollution regulations in your country are: (1 = lax whencompared with those of most other countries, 7 = among the world'smost stringent)

Air pollution regulations

Water pollution regulations The water pollution regulations in your country are: (1 = lax whencompared with those of most other countries, 7 = among the world'smost stringent)

 

 

Descriptive Statistics and Data Sources

Variable Obs Mean Std. Dev. Min MaxDependant VariablesBilateral Waste Imports (tons) 7,644 19,575 213,154 0 10,200,000Bilateral Waste Import Value (mil $) 7,644 6.27 59 0 2,420Bilateral Net Total Import Value (mil $) 7,644 1,010 6,670 0 258,000Independent VariablesEnvironmental regulation gradient 7,644 -2.295 42.579 -95.455 95.455ESI gradient 6,715 -0.846 21.875 -17.415 17.415GDP/capita gradient 7,644 -1.136 29.276 -65.659 65.659Recycle wage gradient 3,477 -8.261 126.806 -198.581 198.581Capital/labor gradient 5,544 -0.044 1.293 -1.991 1.991Ln[Distance] 7,644 8.677 0.878 4.742 9.886Contiguity dummy 7,644 0.029 0.167 0 1Common language dummy 7,644 0.128 0.334 0 1Colonial dummy 7,644 0.020 0.140 0 1Free trade area dummy 7,644 0.180 0.384 0 1Both countries Basel ratification dummy 7,644 0.921 0.270 0 1A7 & Ban 3 ratified Exporter; non-A7 importer dummy 7,644 0.172 0.378 0 1Bilateral Regulation 7,644 0.301 0.459 0 1Bilateral Cost 7,644 0.283 0.450 0 1

Table A4: Descriptive Statistics

Bilateral waste trade data comes from the UN Comtrade Database. Environmental

regulation gradient variables are calculated using survey questions (found in Table A3 above)

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from the 2004 Global Competitiveness Report. GDP per capita and labor force data was

obtained from the World Development Indicators Database and geographical variables such as

distance, colonialization, common official language and contiguity were obtained from the

dist_cepii.dta data file on the CEPII website http://www.cepii.fr/anglaisgraph/bdd/distances.htm.

Data on whether a country was part of a free trade area or customs union was constructed from

information on the World Trade Organization website at www.wto.org. Data on Basel

convention membership comes from the Basel Convention website at www.basel.int, while data

on countries that have ratified the Basel Ban were obtained from the Basel Action Network

website at http://www.ban.org/country_status/country_status_chart.html. Number and days of

regulation as well as regulation cost per capita data used in Table 6 comes from Djankov et al.

[2002]. ESI data comes from The Environmental Performance Measurement Project and was

obtained at http://www.yale.edu/esi/. Capital stock data for 2004 was calculated by the

perpetual inventory method and a 9% discount rate using data from the Penn World Tables 6.2

on investment share of real GDP per capita from 1970-2004. Recycling wages were obtained

from the ILO LABORSTA database using manufacturing wages category 37 (Recycling) under

the ISIC-Rev.3 standard industrial classification.