Environmental Impacts of a North American Free Trade Agreement

NBER WORKING PAPERS SERIES

ENVIRONMENTAL IMPACTS OF A NORTH AMERICAN FREE TRADE AGREEMENT

Gene

M. Grossman

Alan B. Krueger

Working Paper No. 3914

NATIONAL BUREAU OF ECONOMIC RESEARCH

1050 Massachusetts Avenue

Cambridge, MA 02138

November 1991

This paper was prepared for the conference on

the U.S.- Mexico

Free Trade Agreement. sponsored by SECOFI.

We are grateful to

the Industrial Relations Section and International

Finance

Section of Princeton University, and the National

Science

Foundation for partial financial support. We

thank Loren Baker.

Kainan Tang, and GuillerInO Frias for research

assistance, and

Drusilla Brown, Gardener Evans, and Greg Schoepfle

for sharing

their unpublished data. Joanne Gowa. Howard

Gruenspecht. and

Jeff Mackie-Mason provided helpful comments and

discussion. This

paper is part of NBER's research program

in International

Studies. Any opinions expressed are those of

the authors and not

those of the National Bureau of Economic

Research.

NEER Working Paper #3914

November 1991

ENVIRONMENTAL

IMPACTS OF A NORTH AMERICAN FREE TRADE AGREEMENT

ABSTRACT

reduction in trade barriers generally will affect the

environment by expanding the scale of economic activity, by altering

the composition of economic activity, and by bringing about a change

in the techniques of production. We present empirical evidence to

assess the relative magnitudes of these three effects as they apply to

further trade liberalization in Mexico.

In Section 1. we use comparable measures of three air pollutants

in a cross-section of urban areas located in 42 countries to study the

relationship between air quality and economic growth. We find for two

pollutants (sulfur dioxide and smoke") that concentrations increase

with per capita GDP at low levels of national income, but decrease

with GD? growth at higher levels of income. Section 2 studies the

determinants of the industry pattern of U.S. imports from Mexico and

of value added by Mexico's maquiladora sector. We investigate whether

the size of pollution abatement costs in the U.S. industry influences

the pattern of international trade and investment. Finally, in

Section 3, we use the results from a computable general equilibrium

model to study the likely compositional effect of a NAFTA on pollution

in Mexico.

Gene H. Grossman

Alan B. Krueger

Woodrow Wilson School

Princeton University

Princeton, NJ 08544

princeton, NJ 08544

and NBER

and NEER

Environmental advocacy groups In the United States have voiced their

concerns about a potential North American Free Trade Agreement (NAFTA). Some

went so far as to oppose the Congressional. granting of fast-track negotiating

authority to the President to enable American negotiators to enter Into talks

with their Mexican counterparts.

The reservations of the lobbying groups

mirror a growing perception on the part of environmentalists worldwide that an

open world trading system may be inimical to the goal of preserving a clean,

healthy, and sustainable global commons.

The arguments linking trade liberalization with environmental

degradation have not been fully articulated.' With regard to a NAFFA, the

environmentalists have expressed a number of reasons for fearing that freer

trade and direct investment flows between the United States and Mexico may

aggravate pollution problems in Mexico and in the border region.2 At the

least discerning level, some have argued simply that any expansion of markets

and economic activity inevitably leads to more pollution and faster depletion

of scarce natural resources. A more pointed argument recognizes that

pollution already is a severe problem in Mexico and that the country's weak

regulatory infrastructure is strained to the breaking point. Under these

conditions, it is feared that any further industrialization that results from

the liberalization of trade and investment will exacerbate an already grave

situation.

Other environmentalists draw their conclusions by extrapolating the

experience of the maquiladora sector in Mexico. The maquiladoras are

See Low and Safedi (1991), who cite several examples of writings that

view

open trade as detrimental to environmental protection.

2 See, for example, Gregory (1991), Kelly and Kemp (1991). National Wildlife

Foundation (1990), Leonard and Christensen (1991), and Ortman (1991).

predominantly

foreign-owned firms

that produce largely for export to the

United States under a Mexican policy that allows duty-free imports of foreign

components for further processing and re-export. Originally, maquiladoras

were required to locate within a 20-kilometer strip along the U.S. -

Mexico

border in order to qualify for special customs treatment. The sector grew -

quite

rapidly and with little governmental oversight, and now is widely

regarded as being a major contributor to the perilous environmental and social

conditions in the border region. Environmental groups point to this sector as

a prime example of how unregulated expansion in response to trade oppor-

tunities can create risks to worker safety and public health. They argue that

investments in this sector have been encouraged by the lax enforcement of

environment and labor protection laws in t4exico and fear that any further

expansion in trade and investment flows between the United States and Mexico

will be motivated by firms' desires to avoid the high costs of meeting U.S.

regulations.

A further concern of some environmental groups is that a NAFEA may

undercut regulatory standards in the United States. Spokespersons have made

the political-economic argument that, with freer trade, industry groups in the

United States will demand less stringent pollution controls in order to

preserve their international competitiveness, so that

environmental standards

will tend toward a lowest common denominator. The environmentalists worry,

moreover, that existing environmental protection laws in the

United States may

be seen as nontariff barriers to trade in the context of a regional

trade

agreement.

While the environmental groups have raised a host of valid questions

they have so far been unable to provide convincing

and well supported answers

to these questions. Many of their arguments fail to recognize all of the

implications of trade liberalization for resource allocation and natural

resource use in each of the trade partner countries. Moreover, die empirical

claims that have been made rely mostly on anecdotal evidence and on

extrapolation of the experience in one region or industry to the entirety of

economic activity in Mexico. Indeed, relatively little is known at any level

of generality about the relationship between a country's trade regime and its

rate of environmental degradatton. or even about the relationship between a

country's stage of economic development and its output of pollution.

Theoretical investigation of these topics has been limited, and empirical

studies are virtually non-existent.

It is useful to distinguish three separate mechanisms by which a change

in trade and foreign investment policy can affect the level of pollution and

the rate of depletion of scarce environmental resources) First, there is a

scale effect, capturing the simple intuition espoused by the environmental

advocates. That is, if trade and investment liberalization causes an

expansion of economic activity, and if the nature of that activity remains

unchanged, then the total amount of pollution generated must increase. The

environmental groups point, for example, to the deleterious environmental

consequences of the combustion of fossil fuels and to the air pollution

that

is generated by the trucking industry. To the extent that economic growth

gives rise to an increased demand for energy, which then is generated by means

similar to the prevailing methods, there will be an increased output of

harmful pollutants that attends an increase in economic output. Similarly, to

A similar decomposition of the effects of economic growth on the output

of pollution has been proposed by the Task Force on the Environment

and the

Internal Market (1990).

the extent that expanded trade gives rise to an increased demand for cross-

border transportation services without there being any change in trucking

practices, increased trade will contribute to a deterioration in air quality.

Second, there is a composition effect that results from any change in

trade policy. When trade is liberalized, countries specialize to a greater

extent in the sectors in which they enjoy competitive advantage. If

competitive advantage derives largely from differences in environmental

regulation, then the composition effect of trade liberalization will be

damaging to the environment. Each country then will tend to specialize more

completely in the activities that its government does not regulate strictly.

and will shift out of production in industries where the local costs of

pollution abatement are relatively great. On the other hand, if the sources

of international comparative advantage are the more traditional ones, namely

cross-country differences in factor abundance and technology, then the

implications of the composition effect for the state of the environment are

ambiguous. Trade liberalization will lead each country to shift resources

into the sectors that make intensive use of its abundant factors, The net

effect of this on the level of pollution in each location will depend upon

whether pollution-intensive activities expand or contract in the country that

on average has the more stringent pollution controls.

Finally, there is a technicue effect. That is, output need not be

produced by exactly the same methods subsequent to a liberalization

of trade

and foreign investment as it has been prior to the change in regime. In

particular, the output of pollution per unit of economic product need not

remain the same. There are at least two reasons to believe that pollution per

unit of output might fall, especially in a less developed country. First,

foreign producers nay transfer modern technologies to the local economy when

restrictions on foreign investment are relaxed. More modern technologies

typically are cleaner than older technologies due to the growing global

awareness of the urgency of environmental concerns.

Second,

and

perhaps

importantly,

if trade liberalization generates an increase in income levels,

then the body politic may demand a cleaner environment as an expression of

their increased national wealth. Thus, more stringent pollution standards and

stricter enfoccement of existing laws may be a natural political response to

economic growth.

In this paper we explore some of the empirical evidence that bears on

the likely environmental impacts of a NAflA. In Section 1, we shed some light

on the relative magnitudes of the scale and technique effects. We use a

cross-country sample of comparable measures of pollution in various urban

areas to explore the relationship between economic growth and air quality.

After holding constant the identifiable geographic characteristics of

different cities, a common global time trend in the levels of pollution, and

the location and type of the pollution measurement device, we find that

ambient levels of both sulphur dioxide and dark matter suspended in the air

increase with per capita GD? at low levels of national income, but decrease

with per capita GD? at higher levels of income. The turning point comes

somewhere between $4,000 and $5,000, measured in 1985 U.S. dollars. For a

third measure of air quality, namely the mass of suspended particles found in

a given volune of air, the relationship between pollution and GD? is

monotonically decreasing.

Sections 2 and 3

address different aspects of the composition effect.

In section 2 we ask whether and to what extent the sectoral patterns of U.S.

foreign investment in Mexico and of Mexican exports to the United States are

affected by the laxity of environmental regulations in Mexico as compared to

the stricter enforcement of controls in the United States. We relate the

sectoral pattern of maquiladora activity, of U.S. imports from Mexico under

the offshore assembly provisions of the US. tariff codes, and of total US.

imports from Mexico to industry factor intensities, U.S. tariff rates, and the

size of pollution abatement costs in the U.S. industry. We find that the

traditional determinants of trade and investment patterns are significant

here, but that the alleged competitive advantages created by lax pollution

controls in Mexico play no substantial role in motivating trade and investment

flows.

Finally, in Section 3, we begin with the premise that resource

allocations in the United States, Mexico, and Canada have been guided by

competitive advantages generated by differences in factor endowments. We

borrow from Brown, Deardorff and Stern (1991) their estimates of the change in

resource allocation that might result from a NAFTA, and discuss the

implications of these predicted changes in the structure of production for

levels

of pollution in each country.

1 Economic Growth and Urban Air Pollution

As we

noted

in the introduction, economic growth has offsetting

implications

for the anthropogenic generation of air pollution. On the one

hand, some pollutants are &

natural

byproduct of economic activities such as

electricity generation and the operation of motor vehicles. As

economic

activity expands, emissions of these pollutants tend to grow.

On the other

hand, firms and households can control their pollution to some degree

by their

choice of technology. Cleaner technologies produce less pollution per unit of

output. As a society becomes richer its members may intensify their demands

for a more healthy and sustainable environment, in which case the government

may be called upon to impose more stringent environmental

controls,

Little is known about the empirical relationship between national income

and concentrations of various poLlutants. Investigation of this issue has

been hampered by the paucity of data on air pollution that is available on a

comparable basis for a representative sample of countries. However, since

1976 the World Health Organization (WHO) has collaborated with the United

Nations Environment Programme in operating the Global Environmental Monitoring

System (GEMS). The goal of this project has been to monitor closely

the

concentrations of several pollutants in a cross-section of urban areas using

standardized methods of measurement. This data set, which to our knowledge

has not previously been analyzed by economists, provides us with an

opportunity to examine how air quality varies with economic growth.4

In the next subsection we describe the GEMS project, the types of

pollution that it monitors, and the data that it has generated.

Section 1.2

gives the details of the statistical analysis that we

have performed. Our

findings are presented in Section 1.3 and the implications

for Mexico are

discussed in Section 1,4.

The GEMS data have

been statistically analyzed by some

enviroTuhlental

scientists (see

World Health Organization (1984]), but they have neglected

to use

any economic variables in their exclusively

bivariate analyses.

1.1 The GEMS Data3

The GEMS monitors air quality in urban areas throughout the world.

Daily (or, in some cases, weekly or less frequent) measurements are taken of

concentrations of sulphur dioxide (SO) and suspended particulate matter

Data on particulates, which are gases and liquids suspended in the air, are

collected by different methods (described further below) that alternatively

measure the mass of materials in a given volume of air and the concentration

of finer, darker matter, sometimes referred to as "smoke".

Sulfur dioxide is a corrosive gas that has been linked to respiratory

disease and other health problems.' It is emitted naturally by volcanoes,

decaying organic matter, and sea spray. The major anthropogenic sources of

SO are the burning of fossil fuels in electricity generation and domestic

heating, and the smelting of non-ferrous ores (World Resource Institute,

1988). other sources in some countries include automobile exhaust and the

chemicals industry (Kormondy, 1989). Sulfur dioxide emissions can be

controlled by the installation of flue gas desulfurization equipment

(scrubbers) on polluting facilities, and by switching electricity-generating

and home-heating capacity to lower sulfur grades of coal or away from coal

altogether.

Particulates arise from dust, sea spray, forest fires, and volcanoes.

Most of these naturally produced particles are relatively large. Finer

The GEMS data for 1977.1984 are published by the World Health Organization

in the series Mr quality in Selected Urban Areas. Unpublished data

for 1985-

1988 have been kindly provided to us by Gardener Evans of the U.S. EPA.

6 Lave and Seskin (1970) find for example, that variation in SO2 and

population density together explain two-thirds of the variation

in death from

bronchitis in a sample of U.S. cities-

particles are emitted by industry and from domestic fuel combustion (World

Resources Institute, 1988). Larger particles reduce visibility but have

relatively minor health impact, whereas the finer particles can cause

eye and

lung damage and can aggravate existing respiratory conditions (U.S. EPA,

1982). Particulate emissions from anthropogenic processes can be reduced via

the installation of control equipment and by switching to fuels that, when

burned, emit fewer particles.

The GEMS sample of cities has been changing over time. Sulfur dioxide

was monitored in 47 cities spread over 28 different countries in 1977, 52

cities in 32 countries in 1982, and 27 cities in 14 countries in 1988.

Measurements of suspended particles were taken in 21 cities in 11 countries in

1977, 36 cities in 17 countries in 1982, and 26 cities in 13 countries in

1998, while data for darker matter (smoke) are available for 18 cities in 13

countries for 1977. 13 cities in nine countries for 1982, and seven cities in

four countries for 1988. In all, there are 42 countries represented in our

sample for 502, 19 countries in our sample for dark matter, and 29 countries

our sample for suspended particles. The participating cities are located

in a variety of developing and developed countries and have been chosen to be

fairly

representative of the geographic conditions that exist in different

regions of the world (Betmett et at., 1985). In most of the cities included

in the project, air quality measurements are taken at two or three different

sites, which are classified either as center city or suburban, and as

commercial, industrial, or residential. Multiple sites in the same city are

monitored in recognition of the fact that pollutant concentrations can vary

dramatically with local conditions that depend in part upon land use.

Observations at most sites are made on a daily basis and the data set includes

measures of the mean, median, 80th, 95th, and 98th percentile of daily

observations in a given site for a given year.

Sulfur dioxide concentrations have been determined by a number of well

accepted methods (see WHO, 1984). The reliability of these methods has been

checked in independent studies, and an intercomparison exercise was performed

using one particular method as a reference point (Bennett et al., 1985). It

was concluded that the measurements by alternative methods are roughly

comparable, although particular meteorological conditions can affect the

various methods differently. With these results in mind, we have chosen to

pool

our sample of observations of 502

concentration,

but to allow for a dummy

variable to

reflect the method of measurement at each site.

Suspended

particles are measured by two main

methods. High volume

gravimetric sampling determines the mass of particulates in a given volume of

air while the smoke-shade method assesses the reflectance of the stain left on

a filter paper that ambient air has been drawn through. The former method

measures the total weight of suspended particles while the latter is

predominantly an indication of dark material in the air. Since the two

methods yield incomparable measures that capture different aspects of

particulate

air pollution, we treat the data generated by gravimetric and

smoke-shade methods separately in our analysis.7

Table I provides the mean,

median

and standard deviation

for the 50th

and

95th percentiles of daily observations in our sample of cities for each of

A few sites used nephelometric methods to measure suspended particles;

i.e., they measured the light

loss due to scattering when a light beam is passed

through

a sample of particle-laden air. This method gauges the mass of suspended

particles,

much as does the high volume gravimetric method. Since the estimates

are comparable in many cases, we pooled the observations from these two

types of

instruments, but included a dummy variable

to allow for device-specific

measurement differences.

the three types of pollution. Figure 1 displays the

corresponding histograms.

The median

daily observations on SO2 range from a minimum of zero to a

maximum of 291 micrograms per cubic meter (pg n13) of air, whereas the

95th

percentile of daily measures range from zero to 1022 pg m3.8 These numbers

can be compared with the World Health Organization recommendation that

annual

average SO2 concentrations ought not to exceed 40-60 pg m and that 98th

percentile concentrations ought not to exceed 100-150 pg m3. The median of

daily observations for suspended particles varied from zero to 715 pg nC3

while that for the 95th percentile observation ranged from [5 to 1580

The WHO guidelines for suspended particles list 60-90 pg nC3 as the safe limit

for the annual mean and 150-230 pg

as the safe limit for the 98th

percentile. Finally, the median of daily observations of dark matter (or

smoke) in the sample of sites varied from zero to 312 pg m3, while the 95th

percentile observation varied from two to 582 pg m3. The WHO recommends that

dark matter not exceed 50-60 pg & in annual average and 100-150

pg nC3 in the

98th percentile of daily observations.

1.2 Estimation

Concentrations of pollutants in the air depend upon the amounts that are

emitted by natural and anthropogenic sources and on the ability of the

atmosphere to absorb and disburse the gases or particles. Thus, our analysis

of the relationship between growth and air quality must allow for an influence

of city and site characteristics on the observed concentrations of the various

pollutants in addition to the dependence on national product.

a Actlly, SO concentrations are never

literaLly zero, but the machines

are unable to detect very low levels of the gas.

We have sought to explain the median and 95th percentile of daily

observations for SO2 suspended particles (gravimecric and nephelomecric

methods) and dark matter (smoke-shade method). As explanatory variables, we

have included functions of per capita GOP in the country where the site is

located, characteristics of the site and city, and a time trend. We used the

Summers and 1-leston (1991) data for per capita CD?, which attempt to measure

output in relation to a common set of international prices. Initially, we

allowed the coefficient on per capita CD? to vary across income ranges by

including a dummy variable in our regressions for each $2,000 interval of per

capita GDP. These relatively unrestricted regressions suggested that a cubic

function of per capita CD? would fit the data fairly wellS The cubic

equations are the main focus of our subsequent analysis.9

In the equation for concentrations of SO2, we included dummy variables

for the location within the city (central city or suburban) and for the land

use of the area near the testing site (industrial, commercial, or

residential). We also included a dummy variable for the method of measurement

<gas bubbler or otherwise). Another dummy indicated whether the city was

Located along a coastline or not (reflecting the disbursement properties of

the local atmosphere). We included a variable for the population density of

the city and a dummy variable for whether the city was located in a country

ruled by a Communist government.'6 Finally, a linear time trend was included

allow for the possibility that pollution has been abating (or worsening)

We also estimated equations in which we entered per capita GDP in

quadratic form. In general, the quadratic equations do not fit

quite as well as

the

cubic equations, though in many cases the shape of the estimated

relationship

between

income and pollution

is found to be roughly the same.

Population densities were collected from several different sources.

These sources and other details of our data set are available upon request.

worldwide, in response to increased global awareness or for other reasons)'

The regressions for suspended particles and dark matter included a

similar sat of right-hand-side variables, except that we did not include a

dummy variable for the method of monitoring dark matter, because all

measurements were taken in the same way. Since dust is an important natural

source of particulate matter, we included as an additional explanatory

variable a dummy that indicates whether the measurement site is located within

100 miles of a desert.'2

Some commentators have argued that a country's level of pollution might

be directly related to its openness to international trade, perhaps becAuse

environmental regulations tend to a least common denominator. To test this

hypothesis, we estimated one set of regressions in which we

included the trade

intensity of the country in which the site is located (ratio

of the sum

exports

and imports to CDP)

a separate determinant of the concentration of

the

air

pollutants)3

For each pollutant, we estimated a random effects model, allowing

for a

component

of the error term that is common to a given year's observations at

different sites located in the same city." We find that the variance

of the

We began our analysis with separate dummy variables for

each year in our

sample, but the estimates of this model strongly suggested a

simple, linear time

trend.

However,

we coded the desert dummy variable as zero

for Cordoba.

Argentina. in view of the fact that a mountain range

lies between this city and

the nearby desert. The regressions fit somewhat better

with Cordoba treated this

way, although none of our conclusions

about the relationship of particulate

pollution to CDP depends upon this designation.

The data on trade intensities were taken from the

World Bank database.

That is, if

is the total residual in the equation for some pollutant

at site i in city j at time t, we assume that pj

— ait

where ait is the

common.to.the-city component,

is the idiosyncratic component, and

common-to-the-city

component of the estimated residuals is relatively large in

comparison to the idiosyncratic (site-by-tine) component, so that ordinary

least squares would give an inconsistent estimate of the standard errors of

the regression. We also calculated one set of estimates that allowed for

fixed site effects, In other words, we included a separate dummy variable for

each

of the different sites in our various samples -

The

fixed effects were

intended to capture the unobservable topographical and meteorological

conditions

at a site that might contribute to its ability to absorb or

disburse pollution. Of course, when we included the fixed site effects we

dropped the dummy variables reflecting the location of the site within the

city and the location of the city on a coastline or near a desert, since these

influences were no longer separately identified. The model with fixed site

effects provides an especially stringent test of the relationship between

national income and pollution, inasmuch as it ignores all information

contained

in the cross-country variation in pollution levels and relies

instead only on the variation

in air quality that resulted

at the various

sites

from changes in CDP during the twelve years of GEMS observations

(and

then

only that part of the variation that cannot be explained by a common

linear time trend).

1.3 Findings

The results of our various estimates of the random effects models for

the three pollutants are given in Tables 2-4. These regressions do not

include the model with fixed site effects, which we discuss separately below.

—

Our estimation takes into account the unbalanced nature of this

panel data set.

Figure 2 displays, for the median of daily observations

on each

Pollutant, the estimated coefficients on the dunimy

variables indicating

whether a country1s per capita COP falls in the

range from $2,000 to $3999

from $4,000 to $5,999, and so on.

The coefficient estimates have been

plotted

above the midpoint in the range;

e.g.. above $3,000 for CDP in the

range frort

$2,000 to $3999. These coefficients should be

interpreted as indicating the

amount of extra pollution a.country with a per capita GD?

in a given range is

likely to have, holding constant the values of other

explanatory variables

(site location, city population density, etc.), relative

to a country with a

per capita COP in the range from zero to $2,000. The figure also

shows the

estimated amount of extra pollution that is associated

with a given level of

per capita COP (relative to a country with a per capita GDP of $1,000)

that

comes from regressing the pollution concentrations on

per capita GD?, per

capita COP-squared, per capita COP-cubed, and the remaining

explanatory

variables. The figure shows that, in each

case, the cubic functional form

approximates well the shape of the relationship between pollution

and GOP that

is indicated by the less restrictive regressions,

Figure

depicts the estimated relationship between per capita GD? and

S0,

derived form the cubic equations, for both the 50th and 95th

percentile

of daily observations, For both measures, the concentration of

502 rises

with

per

capita CDP at low levels of national income, falls with

per capita CDP in

the broad range between $5,000 and $14,000 (1985 U.S. dollars), and then

levels off or perhaps begins to rise again.'1 The

turning point in the

There are only two countries in our sample (the United States and Canada)

with per capita incomes in excess of $16,000, so the fact that the estimated

curves turn upward in this range probably should not be viewed as strong evidence

for a renewed positive relationship between national product and

SO2 pollution

at high income levels,

predicted relationship for the median of daily observations comes at $4,119,

while that for the 95th percentile observation occurs at $4,630. We estimate

that a country with per capita CD? of $5,000 will have a 20 pg nC3 greater

concentration of 502 for the 95th percentile of its daily observations, as

compared to a country with a per capita CD? of $1,000, all else equal.

Table 2 indicates that the hypothesis that SO2 pollution is unrelated to the

level of CD? can be rejected at the 1/100th of one percent significance level

in the regression for the median observation and at the seven percent level in

the regression for the 95th percentile observation (see columns 2 and 5).

Table 2 reveals that several other variables contribute significantly to

the cross-city variation in concentrations of SO2.

For example, cities

located on a coastline are estimated to have lesser concentrations of S0:

6.68 pg m3 lower for the median of daily observations and 46.79

pg m3 lower

for the 95th percentile observation. Concentrations of 502 are higher in the

center city than in the suburbs, lower in residential areas than in commercial

areas, and higher in industrial areas than in commercial areas (although this

effect is not statistically significant at conventional significance levels).

More densely populated cities suffer greater concentrations of SO2, all else

equal. We also find that SO2 pollution has been significantly greater in

cities Located in Communist-ruled countries. Finally, we note that 802 levels

have been trending downward in our sample of cities even after controlling for

the effects of income and other variables. The downward trend may reflect an

increasing global awareness of the health probLems associated with SO2, and

the expanding efforts that are being made worldwide to limit suLfur emissions.

Columns

and 6 of Table 2 present estimates of a model for

802

determination

that

includes trade intensity as an additional explanatory

variable, Contrary to the fears of some environmentalists, we find that 502

Levels are significantly lower in cities located in countries that conduct a

great deaL of trade (relative to their GOP). We have no good economic

explanation for this finding.

Figure 4 depicts the estimated (cubic) relationship between dark matter

and per capita CDP for the median and 95th percentile of daily observations.

Apparently, the nature of the relationship is much the same as for 502. The

concentration

of smoke in the air rises with per capita GOP at low levels of

income, peaks at around $5,000 (1985 U.S. dollars). and faLls with GOP at

higher income levels until it eventually levels off. We see in columns 2 and

5 of Table 3 that the three CD? variables are jointly significant in the

determination

of dark matter pollution at the 1/100th of one percent

significance level. Moreover, the size of the estimated effects are quite

large. We estimate that a country with a per capita CD? of $5,000 will have a

higher

concentration of smoke by about 90 pg

in its median of daily

observations

and 220 pg m3 in its 95th percentile observation, compared to

one with a per capita COP of $1,000. Recall that the WHO

recommends

that

concentrations of smoke not exceed 50-60 pg m for the mean observation and

100-150 pg of3 for the 98th percentile observation.

Not surprisingly, the dummy variable indicating proximity to a desert

has a positive and significant coefficient in the regressions explaining

concentrations of dark matter. A location on a coast reduces a citys

concentration of this type of pollution, and

again

the effect is statistically

significant. We find that smoke pollution levels are greater

in center cities

than in suburbs, and smaller in residential areas than in

commercial areas.

Also, dark matter, like 502, rises with population density,

although this

effect is significant only in the regression for the median of daily

observations. Finally, there appears to be neither a global trend in this

type of pollution (once the upward movement in world incomes has been

accounted for) nor a significant association with trade intensity.

Unlike the other two pollutants, the mass of suspended particles in the

air appears to fall in response to increases in per capita GD? at low levels

of economic deveLopment (see Figure 5). This relationship continues until per

capita GOP reaches about $9,000, whereupon economic growth has no further

effect on the concentration of suspended particles. Again, the estimated

effects are large in comparison to the WHO guidelines, and again the three CD?

variables are jointly significant in the determination of this measure of air

quality.

As with dark matter, cities situated near to a desert are likely to

experience higher concentrations of suspended particles than cities located

elsewhere. This effect is both quantitatively large and highly significant.

The coefficients on the Communist dummy, the center-city dummy and the

industrial area dummy all are positive and statistically significant in the

regressions for both the median and 95th percentile of daily observations.

The global trend in suspended particle pollution apparently has been downward,

although the coefficient on the year variable is statistically significant

only in the regressions for the median of daily observations. Finally, a

country's trade intensity has a small and statistically insignificant effect

on this form of pollution.

Table 5 reports the estimated coefficients from a random effects model

for each type of pollutant that also allows for fixed site effects. The

numbers of site dummy variables that were included in the various regressions

are shown in the table. These estimates

of the relationship between per

capita GD? and the various measures of air quality rely only on

the

covariation between GD? and concentrations of the pollutants over time

within

the individual sites, and not on the cross-country variation in pollution and

GD? at a given moment in time. The estimated relationships

for the median of

daily observations of s2 and dark matter hold up remarkably

well. (see Figure

6). In each case, the data continue to indicate an

inverted-U shaped

relationship between pollution and national income with peak

levels of

pollution occurring for per capita incomes

in the range from $4,000 to $5,000.

Only the estimated coefficients from the

fixed site effects modeL for

suspended particles suggest a different relationship

between per capita GD?

and concentrations of the pollutant than is found in

the regressions without

site effects. In this case, the estimation indicates a

monotonically

increasing relationship between particulate pollution

and national output in

the sample range of output levels.

1.4 Implications for Mexico

Unfortunately, Mexico has not participated in the

GEXS project and

reliable measures of its air pollution are not

available (US GAO, 199lb).

Thus, predictions for Mexico must be inferred

from relationships that hoLd in

other countries at similar stages of development. Surely

the available

evidence suggests that air quality has deteriorated

with economic growth in

Mexico (US GAO, l99lb). Our estimates indicate

that this experience is common

in poor countries, but that the positive

association between two pollutants

and economic output ceases when the typical country

reaches a per capita

income level of about $5,000 (1985 U.S.

dollars). We note that Summers

and

Heston (1991) put Mexico's per capita CDI' in 1988 at $4,996. Thus, we might

expect that further growth in Mexico, as may result from a free trade

agreement with the United States and Canada, wild lead the country to

intensify its efforts to alleviate its environmental problems.

Recent measures taken by the government of Mexico suggest that the

country already may have reached the turning point in tens of air pollution.

In the last year, the Salinas government has reduced the lead content of

petrol, ordered several power stations to burn natural gas instead of sulfur-

generating fuel oil, and shut down oil refineries and private firms that were

found to be major sources of air pollution (The Economist. May 18, 1991).

Also, new cars are being fitted with catalytic convertors, a new fleet of

cleaner buses has been introduced, and drivers have been banned from using

their cars in Mexico City one day each week. To beef up enforcement, the

budget of the environmental protection ministry has been increased sevenfold

(New York Times, Sept. 22, 1991). Further growth may enable the government to

implement fully its planned $2.5 billion, four-year program to clean up Mexico

City.

2 Pollution Abatement Costs and the Pattern of U.S. -

Mexico Investment

and Trade

A main source of concern about a NAFTA is that it will enable firms to

circumvent U.S. environmental protection laws. If the costs of meeting

pollution controls are high in the United States and low or negligible in

Mexico,

then the asymmetry in standards or enforcement efforts can create a

competitive advantage for Mexican producers

and can motivate U.S. fins to

relocate

their production facilities south of the border. In these

circumstances liberalization of trade and investment

flows can strengthen the

incentives for "environmental dumping

A number of authors1 including for example Pethig (1976), Siebert

(1977). 'lobe (1979) and KcGuire (1982), have studied

the theoretical

relationship between environmental regulations and the pattern of trade. They

find that strict environmental standards or costly controls

weaken a country's

competitive position in pollution-intensive

industries and diminish its

exports (or increase its imports) of

the product of such sectors. Countries

that fail to regulate industrial pollutioo increase their specialization

activities that damage the environment. Kccuire has extended

these results to

include direct foreign investment: controls cause firms active

in the

pollution-intensive industry to relocate their

activities to the less

regulated countries.

While these theoretical predictions are plausible and intuitive,

they

have found little support in previous empirical

studies of trade and

investment patterns. For example, Tobey (1990) has

tested the hypothesis that

environmental regulations have altered the pattern of trade

in goods produced

by "dirty" industries.

lie finds that a qualitative variable describing

the

stringency

of environmental controls in 23 countries fails to

contribute to

the determination of their net exports of the

five most pollutionintensi'le

commodities.

Similarly. Walter (1982) and Leonard (1988)

conclude that there

is little evidence that pollution abatement costs

have influenced the location

decisions of multinational firms. Apparently,

the cross-country variation in

the costs of meeting environmental controls is not so

large as to be a major

factor in the determination of nations' comparative

advantages.

Our purpose in this section is to address

this issue in connection with

the pattern of U.S. imports from Mexico and the pattern of U.S. foreign

investments in Mexico- There is some evidence from a GAO survey suggesting

that a few American furniture manufacturers may have moved their operations to

Mexico in response to the State of California's tightening of air pollution

control standards for paint coatings and solvents (US CAO, l991c). But the

question remains open as to whether the overall sectoral pattern of U.S.

economic relations with Mexico has been meaningfully affected by the higher

costs of pollution abatement in the United States. If the pattern of

specialization has been so influenced, then the composition effect of a

further liberaLization of trade and investment may be damaging to the

environment.

Using data from

the

Bureau of Census' 1988 survey of pollution abatement

costs in American industries (U.S. DOC, 1988). we have conducted three sets of

tests. We have studied the 1987 pattern of U.S. imports from Mexico in 3-

digit SIC manufacturing industries, the pattern of 1987 U.S. imports from

Mexico that have entered the country under the offshore assembly provisions

(again at the 3-digit SIC level), and the sectoral pattern of value added by

maquiladora plants in (approximateLy) 2-digit industry categories. In each

case we have investigated whether pollution abatement costs in the United

States help to explain the pattern of Mexican specialization and trade.

Our estimates of the determinants of the pattern of manufactured imports

from Mexico are recorded in the first two columns of Table 6. We use the

ratio of 1987 U.S. imports from Mexico to total U.S. shipments in the same

industry as the dependent variable. The explanatory variables include

factor

shares (reflecting the intensity of use of the various factors by the

different industries; see Harkness (19781), the U.S. effective tariff rate on

imports of goods in the industry, and the ratio of pollution abatement costs

(operating expenses) to total value added in the U.S. industry.16 1e also

report another regression that includes the average injury rate in the

industry as an additional independent variable. Since firm outlays for worker

compensation insurance are roughly proportional to injury rates (Krueger and

Burton, 1990). the injury variable proxies for one (large) component of the

cost to American manufacturing firms of U.S. labor protection laws.

We computed the factor shares as follows. Je took the payroll expenses

in an industry to represent a combined compensation for unskilled or "raw"

Labor and human capital. Payments to unskilled labor were defined as the

product of the number of workers in the industry and the economy-wide average

yearly income of workers in manufacturing with less than a high school

education. We formed a share by dividing this amount by value added, and

considered the remaining part of the total labor share to represent the

payment to human capital.11 Finally, we calculated the share of capital

value added as the difference between one and the total payroll share.t619

The

Cen5us survey did not include the apparel industry (SIC category 23).

For these observations and four others with missing data, we inserted the average

ratio of pollution abatement costs to value added for all manufacturing sectors

included in the survey.

For tvelve industries, this method gives a negative number as

the

estimate of the share of human capital. To ensure that our results were

not

sensitive to the choice of income for an unskilled worker, we

also computed

factor shares using the income level for unskilled workers ($10,819)

that wade

the minimum share of human capital our sample of industries equal to zero.

The

estimated import equations with these measures of factor shares

look much the

same as for our original measures.

The

import figures and the effective tariff rates were provided

to us by

Greg Schoepfle of the U.S. Department of Labor.

The tariff rates were estimated

by dividing the total duties collected on imports from

Mexico in a two digit SIC

industry by the total value of imports in the industry.

Data On shipments,

value added, employment, and payroll were taken from

the N.B.E.R. trade and

immigration data set (see Abowd, 1987).

Since the trade data are classified

Factor intensities figure prominent].y in the determination of the

pattern of U.S. imports from Mexico. The ratio of imports to total US.

shipments is smaller in industries that are highly intensive in their use of

human or physicat capital. This means, of course, that Mexico exports to the

United States goods that have a relatively high share of unskilled Labor in

total factor cost. The coefficients on the physical. and human capital

variables both are statistically significant at the five percent level, but

the latter coefficient is quantitatively much larger. We estimate that a ten

percentage point increase in the share of human capital reduces the ratio of

imports to shipments by 0.52 percentage points, while a ten percentage point

increase in the share of physical capital lowers the import ratio by only 0.24

percentage points. Note that the mean ratio of imports to shipments across

all manufacturing industries is 0.69 percent.

We estimate that the import ratio rises with the share of pollution

abatement costs in U.S. industry value added, as would be predicted by a model

of environmental dumping. However, the impact of these regulatory costs is

according to the old (1972) SIC categories, we used manufacturing census data

that were bridged to this classification scheme. The average income for a worker

in the manufacturing sector with less than a high school education was calculated

from the 1987 Current PQvulation Survey by the authors. Injury rates by industry

were taken from the Eureau of Labor Statistics publication. Occunational Injuries

and Illness in

the

United States by Industry. In the few cases where an injury

rate was not available for a three digit industry, we used instead the average

injury rate for the applicable 2-digit industry.

are also experimenting with a second procedure for dividing the labor

share into component parts representing the shares of unskilled labor and human

capital. In this, we form factor share for different skill groups (workers with

a high school education, workers with some college education, and workers

with

a college degree) by taking the product of the industry labor force, the fraction

of the industry's workers in the skill group in question, and the economy-wide

average income of workers in that skill group, and then dividing by

value added.

If this procedure yields substantially different conclusions, we will report on

the results in a future draft of this paper.

both quantitatively small and statistically

Insignificant

Consider for

example an industry that has the

Dean

ratio of pollution abatement costs

industry value added and another that has a ratio that is

two standard

deviations higher. We estimate that the latter

industry will have a greater

ratio of imports to U.S. shipments by about 0.05

percent, which is less than

1/20th of a standard deviation in the import variable. This

finding can be

understood from the fact that pollution abatement costs

average only 1.38

percent of value added across all manufacturing industries and rise to

only

4.85 percent in an industry that is two standard deviations

above the mean.

The implied variation in competitiveness is small in

comparison with that

which arises from cross industry variations in Labor

costs, for example.

We note that the injury rate has a positive coefficient when

it is

included in the import equation, but this variable too has

a very small and

statistically insignificant effect. Finally the coefficient on the tariff

variable has the theoretically predicted negative sign (imports

are smaller in

industries with high effective tariff rates), but also is not significant.2°

We turn next to the determinants of the pattern of U.S. imports from

Mexico under the offshore assembly provision (i.e., import

category 207.00 in

the old TSUSA tariff schedule). U.S. trade law provides for

duty free re-

entry

American-made components embodied in imported final

goods.

In cases

where

intermediate

products are exported for further processing or assembly

abroad, the applicable tariff rate applies only to the foreign value added.

Nearly 44 percent of the value of Kexican exports to the United States

qualified for this customs treatment in 1987 (Schoepfle, 1991). We study

simultaneity bias may exist here, insofar as many political-economic

theories of tariff formation predict that high tariff rates will endogenously

emerge in industries in which import penetration is great.

these imports separately, because much of the

output by maquiladora plants

enters the United States in this manner, and maquiladoras

are the source of

most of the item 807.00 U.S. imports from Mexico (Schoepfle.

1991). Thus, the

sectoral pattern of item 807.00 imports gives us an idea

as to the pattern of

maquiladora activity. -

The

middle two columns of Table 6 reports estimates of

an equation for

the ratio of the Mexican value added in imports

entering under the 807.00 code

in 1987 to the total value added by the U.S. industry. We would

expect this

variable to be high in industries where maquiladoras are

especially active;

i.e. •

those

in which Mexico enjoys a competitive advantage. The independent

variables in these equations are the same as before, namely the factor

shares,

the share of pollution abatement-costs in U.S. industry value

added, and in

one set of estimates, the industry injury rate. The tariff variable was

omitted from these regressions because our effective tariff rate

applies to

all imports from Mexico, and does not reflect the

average tariff paid on

imports that entered under the offshore assembly provision. We use a tobit

model to estimate these equations in view of the fact that item 807.00

imports

are zero in 58 of the 136 Industry categories.- Since the import share cannot

be negative, a censored regression model is appropriate.

The foreign content of U.S. item 807.00 imports from Mexico is highest

in relation to total value added in the corresponding U.S. industry in sectors

that make relatively intensive use of unskilled labor. This is not

surprising, since many U.S. manufacturers attempt to outsource to maquiladora

operations the most unskilled labor intensive phases of the production

process. We find a negative association between the Mexican content of item

807.00 Imports as a fraction of total U.S. industry value added and both the

human capital share and the physical capital share in U.S. industry costs.

The coefficient on the physical capital variable is estimated to be nearly

twice as large in absolute value as that on the human capital variable

(reversing the ordering found for total imports from Mexico), and the former

coefficient is statistically significant at the five percent level whereas the

latter is statistically significant at only the ten percent level.

Again, we fail to find a significant positive relationship between the

size of pollution abatement costs (as a fraction of value added) in the U.S.

manufacturing industry and the scale of sectoral activity in Mexico. In fact,

the foreign content of item 807.00 imports from Mexico appears to be lower in

relation to the size of the U.S. industry in sectors where U.S. pollution

abatement costs are relatively high. The negative coefficient on the

abatement cost variable is found to be statistically significant at the five

percent level, although this may of course reflect a spurious correlation

between these costs and some omitted variable. When the U.S. injury rate is

included in the equation for item 807.00 imports, the estimated coefficient on

this variable is also negative. This would imply that relatively little

Mexican assembly activity takes place in those industries where the cost to

US. employers of workers' compensation insurance and other accident-related

costs are especially high. However, in this case, the estimated coefficient

is not significantly different from zero, so we cannot reject the hypothesis

that the association between injury rates and the pattern of Mexican assembly

operations is nil.

The final set of estimates recorded in Table 6 relate to the activity of

Mexican maquiladoras. The Instituto Nacional de Estadistica Geografia E

Informatica (INECA), a private Mexican research institute, has surveyed all

maquiladoras on the government's List of in-bond producers. Their

publication, Eiadistica de Ia Industria Maouiladora de Exoortacion. 1978-

UM. provides data on value added by maquiladoras in eleven different

manufacturing industries. We developed a concordance of the available data to

a 2-digit SIC basis and sought to explain the ratio in 1987 of value added by

maquiladoras to value added in the corresponding U.S. industry.2' The

explanatory variables are defined as before, except that now we calculate

factor shares and pollution abatement costs as a fraction of value added at

the 2-digit SIC level.

The estimated coefficients from the models of maquiladora activity

confirm our findings for total U.S. imports from Mexico and for imports under

tariff item 807.00. Again we find that Mexican competitive advantage derives

from an abundance of unskilled labor, and that value added by maquiladoras

declines in relation to value added in the United States the greater are the

shares of human and physical capital in industry cost. Although neither

coefficient has been estimated precisely enough to allow for a clear rejection

of the hypothesis that the associations are zero, the magnitudes of the

coefficients are very much in line with what we have found before. Also, we

find no evidence in support of the hypothesis that U.S. regulatory costs

contribute to the explanation of the pattern of maquiladora activity. Neither

the coefficient on the abatement cost variable nor that on the injury rate

variable has the (positive) sign that one would expect if American firms are

investing in maquiladora plants primarily to avoid the high costs of

al We note that INECA withheld industry-leveL data for industries with

sixteen or fewer maquiladora establishments. Thus, our analysis treats as zero

the maquiladora activity in sectors where some small amount of production may

have taken place.

environmental

and labor protection laws at home. Evidently, the

costs

involved in complying with these laws are small in relation to the

other

components of total cost that determine whether it is profitable to operate in

the United States or Mexico.

3 Resource Reallocation: Implications for Pollution

Our findings in Section 2 suggest that relative factor supplies

govern the

pattern of trade between Mexico and its neighbors to the North. We might expect,

therefore, that trade liberalization will stimulate Mexican production in

unskilled labor-intensive industries, while the United States and Canada will

shift resources into sectors that make relatively heavy use of capital and

skilled labor. The removal by Mexico of barriers to direct foreign investment

can have the opposite effect on international patterns of specialization, if

foreign

firms bring with them the factors that are scarce in

Mexico. Then local

production

may expand in (moderately) capital and skilled-labor intensive

sectors. The question that arises is, what are the environmental, implications

these potential resource reallocations?

To answer this question fully, we need several pieces of information.

First, we need to know which sectors will expand in each country, and to what

extent.

Second, we need to know the pollution-intensities of the various

industries.

Finally, we must know how NAFrA will affect the production

technologies used in each location, so as to gauge any changes in pollution

generated per unit of output.

Estimates of NAflA impacts on the production

structure in each country are available from computational modeling exercises.

Brown, Deardorff, and Stern (1991), for example, have predicted resource

movements for several different scenarios of policy change under a NAFTA.

Unfortunately, the remaining informational requirements pose more serious

difficulties. Concerning the pollution generated by different industries, the

United States collects data only on releases of toxic waste (and that of

questionable quality), while Mexico coLlects no such data whatsoever.

And

although our findings in Section 1 suggest that production techniques might well.

change in response to a trade agreement that generates economic growth in Mexico,

it is difficult to assess how these changes will be distributed across

industries.

In this section we draw upon the detailed estimates of Brown, Deardorff,

and Stern (BBS) to derive some possible environmental implications of the

resource reallocations that would result from a NAFTA. After describing the SOS

modeling exercise, we turn to two issues. First, we discuss the model's

predictions about each country's demand for the services of utilities. Second,

we use the information available in the U.S. EPA's Toxic Resources Inventory to

analyze how a NAFTA might affect releases of hazardous waste by the manufacturing

sectors in the three partner countries.

The BbS estimates are based on a computable general equilibrium model of

the economic interactions between the United States, Canada,

Mexico,

a group of

31 other major trading nations, and an abbreviated fifth region comprising the

rest of the world.

The model aggregates production into 23 categories of

tradable goods and six categories of non-tradable goods and services.

The

industries are treated either as perfectLy competitive or monopolistically

competitive, with the latter set exhibiting economies of scale. Output in each

sector is produced from intermediate inputs and an aggregate of capital and

Labor. The authors allow for varying degrees of substitution between capital and

labor in the different sectors, but treat labor as a homogeneous input. The

latter assumption is unfortunate in view of our

finding in Section 2 that human

capital

endowments play a central role in determining the

bilateral trade pattern

between the United States and Mexico.

Since it is not clear what policy measures will be

included in a NAFTA, BDS

explore a number of alternatives. In one scenario they

assume the removal of all

bilateral tariffs between the United States, Canada and

Mexico, and an easing of

U.S. quantitative restrictions that generates a

twenty five percent increase in

U.S. imports of agricultural products, food, textiles, and

apparel from Mexico.

In a second scenario they allow for these same forms of trade

liberalization and

also a relaxation of restrictions on direct foreign investment in

hexico. The

liberalization of investment is assumed to result in an (exogenous)

ten percent

increase in Mexico's capital stock. It should be noted that in both

of these

cases, the estimated impacts include not only the removal of existing barriers

between Mexico and its trade partners, but also the ultimate implementation of

the policy changes that comprise the already concluded Canada-U.S. free

trade

agreement.

It is well known

that

many pollutants, such as sulfur dioxide, nitric

oxide, nitrogen dioxide, and

carbon

dioxide, are byproducts of electricity

generation, especially when fossil fuels are burned in the process. Thus, an

important determinant of the net effect of trade liberalization on air pollution

will be the induced change in the demand for electricity. Unfortunately, the

disaggregation in the EDS model does not allow us to identify

the likely impacts

of a NAFTA on

electricity use, inasmuch as the model treats electricity as a

component

of the broader category of utilities.

It is interesting to note,

nonetheless, that BDS predict a decline of 0.56 percent in output by the Mexican

utilities sector in response to a hypothetical agreement involving an elimination

of tariffs and an increase in U.S. import quotas.

This prediction can be

understood from the fact that the scenario generates output contractions in ten

of twenty one Mexican manufacturing sectors, and expansions are anticipated

primarily in labor-intensive industries such as food, textiles, apparel, leather

products, and footwear, which presumably are not the sectors that use energy most

intensively. ut just as Mexico will shift resources into activities that

require relatively little energy input, the United States and Canada

may

expected to do the opposite. The model predicts modest increases of 0.07 percent

and 0.09 percent, respectively, in production by utilities in the United States

and Canada.

Quite different conclusions emerge from the scenario that attempts to

capture the effects of a potential liberalization of investment flows.

The

exogenous ten percent increase in the Mexican capital stock that is taken to be

the outgrowth of an easing of restrictions on foreign investment effects an

expansion of every manufacturing industry there, with the implication that demand

for utilities must rise.

The experiment generates an increase in utilities

output in Mexico of 9.31. percent. Presumably, air quality would deteriorate in

this case, unless the associated thcome growth gave rise to political demands for

more stringent standards and tougher enforcement.

We turn next to toxic waste.

Our

focus

on this form of pollution is

primarily a reflection of data availability. As far as we know, this is the only

type of pollution for which data on releases are collected at the

firm level.

U.S. law requires all manufacturing firms with ten or more employees that use at

Least 10,000 pounds of one or more of over 300 chemicals to report their annual

chemical releases to the EPA.

Lines of business are recorded along with

information on releases, enabling aggregation of the data to the industry level.

Several pitfalls in the use of the data contained in the Toxic Release

Inventory (TRI) should be noted.22 First, since the report reflects releases

rather than exposures, it cannot reflect the great differences that exist in the

rates at which different chemicals are dispersed or transformed.

Second,

releases are measured only in terms of weight, and no effort is made to account

for the fact that some high volume releases are relatively benign, while other

lower volume releases may create great health risks. Third, many toxic chemicals

are omitted entirely from the inventory. Fourth, the inventory does not reflect

emissions by firms outside the manufacturing sector or by federal facilities.

Fifth, the EPA conducts relatively little verification of the information it

receives and makes relatively little effort to ensure compliance with the

reporting requirements.

Finally, there is no way to know whether the

relationships between toxic waste and industry outputs in Canada and Mexico

mirror those in the United States, or whether there are great differences across

countries in the industry-specific relationships between hazardous waste and

quantities of output.

We have used the predictions of the BDS model and the data on the industry

breakdown of toxic releases by U.S. manufacturing firms contained in the TRI to

generate Table 7. The table contains estimated impacts on total toxic releases

by the manufacturing industries of Mexico, United States, and Canada in response

to two scenarios for a NAFTA. As before, the scenarios distinguish a NAFTA that

effects only trade liberalization and one that also includes investment

liberalization measures in Mexico. In constructing the table we have assumed

that an industry-specific fixed coefficient characterizes the relationship

between the amount of toxic release and the quantity of output produced. We have

22The following discussion is based on U.S. GAO (199la).

calculated this coefficient using output and total release data for the United

States in 1989.23 To the extent that releases per unit of output are uniformly

higher or lower in Mexico or Canada than they are in the United States, the

estimates in Table 7 for these countries can be adjusted upward or downward to

reflect these percentage differences. But lacking data for Canada and Mexico,

we cannot address the possibility that these countries have higher or lower

pollution coefficients than the United States in some industries but not in

others.

The table tells a similar story to the one told by the utilities sector.

A liberalization of trade in the absence of increased capital flows causes Mexico

to shift resources toward industries that, on average, generate less pollution

than its current producers. In particular, the EDS model predicts contraction

by the industries producing chemical products and rubber and plastics products,

both of which generate great quantities of waste per unit of output.

The

beneficial environmental effects of these resource flows are largely offset by

an expansion of the electrical equipment industry, but a small positive net

impact remains. The reallocation of resources in the United States and Canada

differ from those in Mexico, as. these countries enjoy comparative advantage in

a complementary set of activities.

The model predicts an expansion of the

chemical products industry in the United States, and of the primary metals

industry in Mexico, with the implication that aggregate chemical releases by

manufacturing enterprises will rise in both of these countries.

Again, the scenario that assumes a ten percent increase in Mexico's capital

23The output data, which were provided to us by Dnisilla Brown, are those

that were used in the calibration of the BDS model.

Since these data are

reported on an ISIC basis, we were forced to reclassify some industries in order

to make them compatible with the SIC-based TRI data.

stock has quite different implications for Mexico. As noted before, such growth

in the capital stock causes output to expand in every Mexican manufacturing

industry. If the relationship between waste and output remains unchanged, then

total chemical releases must rise.

While our estimates in this section must be taken with a large grain of

salt, they suggest conclusions that accord well with intuition. Since Mexico

enjoys comparative advantage in a set of activities (agriculture and labor -

intensive

manufactures) that on the whole are "cleaner than the average, the

composition effect of trade liberalization way well reduce pollution there. On

the other hand, a NAFTA will cause the United States and Canada to specialize

more in physical and human capital-intensive activities, to the possible (slight)

detriment of their local environments. On the global level, a net benefit may

derive from the movement of the dirtier economic activities to the more highly

regulated production environments.

4 Conclusions

Environmental advocacy groups have pointed to several risks that might be

associated with further liberalization of trade between the United States and

Mexico. While they raise a number of valid concerns, our findings suggest that

some potential benefits, especially for Mexico, may have been overlooked. First,

a more liberal trade regime and greater access to the large U.S. market

is likely

to generate income growth in Mexico.

Brown, Deardorff and Stern (1991). for

example, estimate potential short run

welfare

gains to Mexico of between 0.6 and

1.9 percent of CDP.

We have found, through an examination of air quality

measures in a cross-section of countries, that economic growth tends to

alleviate

pollution problems once a country's per capita income

reaches about $4,000 to

$5,000 US. dolLars. Mexico, with a per capita GOP of $5,000, now is at the

critical juncture in its development process where further growth should generate

increased political pressures for environmental protection and perhaps a change

in private consumption behavior. Second, trade liberaLization may well increase

Mexican specialization in sectors that cause less than average amounts of

environmental damage. Our investigation of the determinants of Mexico's trade

pattern strongly suggests that the country draws comparative advantage

from its

large number of relatively unskilled workers and that it imports goods

whose

production requires intensive use of physical and human capital.

The asymmetries

in environmental regulations and enforcement between the United States and Mexico

play at most aminor role in guiding intersectoral resource allocations,

But

since it would appear that labor-intensive and agricultural activities require

less energy input and generate less hazardous waste per unit of output than more

capitaL and human capital-intensive sectors, a reduction in pollution may

well

be a side-benefit of increased Mexican specialization and trade.

Our findings must remain tentative until better data become available. Ve

have been unable to use any information about the pollution situation as it

currently stands hi Mexico, since environmental monitoring there has

been

unsystematic at best. Furthermore, the kinds of pollutants that we can

examine

are limited by data availability (eg. ,

there

are no reliable data on emissions

of carbon dioxide in different countries). Still, one lesson from our study

seems quite general and important. The environmental impacts

of trade

liberalization in any country will depend not only upon the effect of policy

change on the overall scale of economic activity, but also upon

the induced

changes in the intersectoral composition of economic activity

and in the

technologies that are used to produce goods and services.

References

Abowd,

John M.

1990. "The NBER Immigration, Trade, and Labor Markets Data

Files. National Bureau of Economic Research Working Paper No. 3351, May

Bennett, Burton C. ,

Kretzschmar,

Jan C., Akland, Gerald C. , and

de Koning,

Henk W.

1985. "Urban Air Pollution Worldwide." Environmental Science and

Technology 19: 298-304.

Brown, Drusilla K., Deardorff, Alan V., and Stern, Robert K.

1991,

"A North

American Free Trade Agreement: Analytical Issues and a Computational

Assessment." Mimeo. Institute of Public Policy Studies, University of

Michigan.

Gregory, Michael. 1991. "Sustainable Development vs. Economic Growth;

Environmental Protection as an Investment in the Future." Statement on Behalf

of Arizona Toxics Information before the International Trade Commission,

Hearing on Probably Economic Effect on U.S. Industries and Consumers of a Free

Trade Agreement Between the United States and Mexico.

Harlcness, Jon. 1978. "Factor Abundance and Comparative Advantage." American

Economic Review 68: 784-800.

Kelly. Mary E. and Kamp, Dick. 1991. "Mexico-1.LS. Free Trade Negotiations

and the Environment: Exploring the Issues." Mimeo. Texas Center for Policy

Studies, Austin TX, and Border Ecology Project, Naco AZ.

Kormondy. Edward J. •

ed.

1989. International Handbook of Pollution Control.

New York and Westport. CT: Greenwood Press.

Krueger, Alan B. and Burton, John F., Jr. 1990. "The Employers' Costs of

Workers' Compensation Insurance: Magnitudes. Determinants, and Public Policy."

Review of Economics and Statistics 72: 228-260.

L.ave, Lester B. and Seskin, E. P. 1970. "Air Pollution and Human Health."

Science 169: 723-733.

Leonard, H. Jeffrey. 1988. Pollution and the Strunle for World Product.

Cambridge: Cambridge University Press.

Leonard, Rodney E. and Christensen, Eric. 1991. "Economic Effects of a Free

Trade Agreement between Mexico and the United States.' Testimony on Behalf of

Community Nutrition Institute before the International Trade Commission

Hearing on Docket No. 332-307.

Low, Patrick and Safedi, Raed. 1991. "Trade Policy and Pollution." Mimeo.

International Trade Division, World Bank.

KcGuire, Martin C.

1982. "Regulation. Factor Rewards, and International

Trade." Journal of Public Economics 17: 335-354.

National Wildlife Federation. 1990. "Environmenta]. Concerns Related to

United States-Mexico-Canada Free Trade Agreement. Mimeo. National Wildlife

Federation, Washington DC.

New York Tines. 1991,

"Facing Environmental Issues." Sept. 22. Sec. 3: 5,

Ortman, David E,

1991.

"On a Comprehensive North American Trade Agreement."

Testimony on Behalf of Friends of the Earth, National Wildlife Federation, and

the Texas Center for Policy Studies before the Subcommittee on Trade,

Committee on Ways and Means, U.S. House of Representatives.

Pethig, Rudiger. 1976. "Pollution, Welfare, and Environmental Policy in the

Theory of Comparative Advantage." Journal

Environmental Economics and

flanatement 2: 160-169.

Schoepfle, Gregory K.

1991. "Implications for U.S. Employment of the Recent

Growth in Mexican Maquiladoras." Frontera None 3: 25-54.

Siebert, Horst. 1977. "Environmental Quality and the Gains from Trade,'"

Kyiclos 30: 657-673.

Summers, Robert and Heston, Alan. 1991. "The Penn World Table (Mark 5): An

Expanded Set of International Comparisons, 1950-1988," Ouarterly Journal of

Economics 106: 327-368.

Task Force on the Environment and the Internal Market. 1990. 1992: The

Environmental Dimension. Bonn: Economica Verlag.

The Economist. 1991. "Smog City." May 18: 51.

Tobey, James A.

1990. "The Effects of Domestic Environmental Policies on

Patterns of World Trade: An Empirical Test." Kykios 43: 191-209.

United States Department of Commerce. 1988. Manufacturers' Pollution

Abatement Capital Exoenditure and Operatint Costs. Washington DC: Bureau of

the Census.

United States Environmental Protection Agency. 1982. Air quality Criteria

for Pnticulate Matter and Sulfur Oxides. Research Triangle Park, NC: ILS.

EPA.

United States General Accounting Office. 1991a. "Toxic Chemicals: EPA's

Toxic Release Inventory is Useful but Can be Improved." Report No. GAO/RCED-

91-121, Washington DC.

United States General Accounting Office. 199lb. "U.S. -

Mexico trade:

Information on Environmental Regulations and Enforcement," Report No,

GAO/NSIAD.91-227, Washington DC.

United States General Accounting Office. 199lc. "U.S. -

Mexico

Trade: Some

U.S. Wood Furniture Firms Relocated from Los

Angeles

Area to Mexico," Report

No. CAO/NSIAD-91.19l, Washington DC.

Walter, Ingo. 1982. "Environmentally Induced Industrial Relocation to

Developing Countries," In S.J. Rubin and T.R. Graham, eds. Environment and

Trade. New Jersey: Allanheld, Osmun, and Co.

World Health Organization. 1984. Urban Air Pollution. 1973-1980. Geneva:

WHO-

WorLd Resources Institute. 1988. World Resources: 1988-59. New York: Basic

Books.

Yohe, Gary W.

1979. The Backward Incidence of Pollution Control -

Some

Comparative Statics in General Equilibrium." Journal of Environmental and

Economic Mana2ement 6: 187-198.

Figure 1:

Histograms of Air Pollutants

• Ii

—

.06

6 760 ida

od, ado an'..

I I no eo'o

0•t Coic

N.tot

Histogram

95th PctL. at Susaenaea Partocle,

0S -

ado 'so iáa 240

—. nt Cta IC MO lit

riiS!Qgram ii MeOan Gaily

50—2 Measurements

'd. 34. oo

.4.,4o4o 710

.4,

.4. amo

0 tor cta,c — lit

Histogram

of gstr,

Pui

o' Daily S0—2 Measurements

.12

.0l

.02

Sine - -

Ii —

—

.04 -

02 —

a—

—

U —

— s_

34.

400100140200 740 ado ida .4, ida 34.360640

930'. pittetia.

Histogram

of g5, PctL of Dark Matter

36 100 00 260

00 300

ma,,

0.rc.at00.

Histogram

o M000an of 0ar Matter

.4,

.4.

.4,

—0 pIr Ctoic MOte

Histogram of Mecian

of Suspenood Particle,

Figure 2: Fitted Cubic and Unrestricted

Dummies

SO—2 vs.

GDP Per Capita

4000 8000 6000 7000 6000 000 10000 11000 12000 3000 40CC '000 '6000 I

70C0

CDP Per Capita

Dark

Matter vs. GDP

1000 2000 300

U-,

130

120 -

110 -

TOO-

O 90-

80—

70-

60-

50-

40-

30-

-10-

-x

-20-

—30-

—40-

—50 -

—70 -

—80 -

-20

—40

-60

—80

—'00

—120

—140

—160

—150

—00

—223

—240

loG

2000

3000 4000

8000

6000

7000

6000

9000

0000

CDP Per Capito

Suspended

Particles vs.

1000

2000

GDP

CDP Per Copto

(-3

Figure

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

11000 12000 13000 l4000 5000 16000

1/000

CDP Per

Capita

SO—2 vs. GDP Per

Capita

—5

—10

—15

COP Per

Capita

Dark

Matter

Figure

vs. GDP Per

Capita

400

350

300

250

200

150

100

0 1000 2000 3000 4000 5000 6000

7000 8000

'3000 10000

11000

Figure

Suspended

Parlieles vs.

GDP Per

Capita

200

—300

—400

—500

—600

0 1000 2000 3000 4000

5000 6000

7000 8000 9000 10000

11000

12000 13000 14000

150ev 160ev

11000

GOP Per

Capita

Figure 6: Fixed Effects Estimates —

50th

Percentile

10 -

—10

—20 -

1000 2000 3000 4000 5000 6000 7000 8000 9000 1000011000120001 30001400015000

'5000 7006

GOP

Per Capita

SO—2 vs.

C-I

•1J)

GDP Per Capita

________

—30 -

—40

—50

1000

2000

3000

4000 5000

6000

GDP Per Capita

7000

f000 9000

10000

Suspended Particles vs. GDP Per Capita

'000

i-fl

-d

500

450

400

350

300

250

200

'50

100

1000 2000 3000 4000 5000 6000 7000 8000 9000 100001100012000' 300014000 1500016000 7000

GDP Per Capita

Table I.

Descriptive Statistics on Air Pollution in Urban Areas

Pollutant Mean

Std. Dev.

Median

WHO Standard

Sulphur Dioxide

33.08

33.11

26.2 40-60

50th Percentile

Sulphur Dioxide

117.17 11271

87.0

100-150

95th Percentile

Dark Matter

42.22

41.92

29.5 50-60

50th Percentile

Dark Matter

127.47 101.45

102.0 100-150

95th Percentile

Suspended Particles

146.62 126.79 91.0 60-90

50th Percentile

Suspended Particles

301.01

268.01 187.0 150-230

95th Percentile

Icotes: Pollutants are measured in pg per cubic meter. World Health

Organization standards listed for the 50th percentile are for the annual

average measure, and those listed for the 95th percentile are for the 98th

percentile of daily measures. Sample size is 1,370 for sulphur dioxide,

1.021 for suspended particles, and 506 for dark matter.

TABLE 2

flw Dcterminants

Sulphur Dioxide Air Pollution

Random Effects Esümates

(Standard

errors in parnitheses)

Variable

50th Percentile

95th Percentile

(t) (2)

(3)

(4)

(5)

(6)

Per

Capita CD?

4.70

—

3.28

S2,

$3,999

(3.94)

(13.67)

Per

Capita CD?

6.43

—

23.45

—

- $5,999

(4.19)

(14.57)

PerCapitaCDP

-4.15

— —

-15.03

—

S6.

87.999

(5.31)

(18.42)

Per Capita CD? 3.91

20.80

—

- $9,999

(4.55)

(15.79)

Per Capita CD?

-9.33

— —

-3.58

$10,000

1.999

(4.40)

(15.26)

Pa Capita CD? -19.07 -3.58

$I2,-$I3,999

(5.74)

(19.82)

Per Capita CD? -I I.82

— —

-24.62

—

$I4,-$I5,999

(5.11)

(17.61)

PerCapicaGDP

-10.28

—

-11.35

$11,999

(6.63)

(22.34)

Per Capital CD?

—

7. 14 I I.22

—

12.02 22.18

($I,s)

(2.50)

(2.64) (8.66) (9.22)

Per Capita CDP

-1.l2 -l.44 -1.68 -2.42

squared

(0.34) (0.34)

(1.53) (1.18)

Per Capita CD?-

—

0.041 0.047k

—

0.055

0.068

nihed

(0.013) (0.013) (0.043)

(0.044)

Coasl -8.68 4.68 -5.73 -52.62

-46.79

-45.21

(239) (2.32) (2.32)

(8.17) (7.94)

(8.03)

Ceclial

City

9.45

8.94 8.94

35.4r 34.33

3531

(1.83) (1.82) (1.86)

(5.76)

(5.73)

(5.11)

Industrial 1.58 1.32

1.74 10.95

10.39

9.65

(1.95)

(1.94)

(2.01) (6.06)

(&04)

(6.12)

Residential

"5.30'

-5.7t'

497

.4.08

-5.45

-3.23

(1.96)

(1.95)

(2.01)

(6.10)

(6,08)

(6.14)

Pop. density 5,54*

4.09

l0.31

4)74*

35.43'

4911'

(l0,Ls.

ml.)

(2.65)

(2.57)

(4.64)

19.27)

(8.97)

(16.30)

Year

-1.69'

'1,19'

•i,77*

4.38'

-5.32'

512

(0.36)

(0.34)

(0.35)

(1.21)

(1)8)

(1.22)

Communist

i1.59'

11.47'

12.64'

88.05'

88.04'

90,77'

(3.88)

(3.83)

(3.86)

(13.47)

(13.32)

(13.61)

Trade Intensity

—

-15.47'

—

.4096'

(4.38)

(15.36)

p-value for .11

.000!

.06

.07

.007

GD)' v.riables

Pet Capita GDP at which

$4107

$5257

—

$4,635

$6182

pollution reaches rcak

(1.327)

(1.179)

(3.309)

(2,963)

556

554

579

5.593

5.575

5.555

368

378

323

5.431

5.541

5.232

.158

'ISO

.166

.132

.125

.138

Notes:

Equations

also

include

an intercept. a

dummy

to indicate that the type of

area

is unknown, and a dummy

indicate that

the

measurement device is a gas bubbler, o is the estimated variance of the common-to ciiy component of the residuals

and q is the

estimated

variance of the idiosynctatic component of the residual. Sample size is 1.370 for columns I, 2.

4 and

sample size is 1,301 for column, 3 and 6.

Slatistically significant at

.05

level for. two-tailed t-test.

TABLE 3

The Daeruünanis of Disk Miner Potlutioc

Raadom

Effects Estimates

(Standard cryw, in parentheses)

Variable

50th PaceniiI

95th Percentik

(I)

(2)

(3)

(4)

(5)

(6)

PcrCapiiaODP 50.50'

99.21*

$2,000-$3,999

(11.77)

(31.77)

Per Capita GD?

5825

—

118.05'

$4.000 - $5,999

(12.63)

(33.99)

Per Capita GDP

43.49' —

II 1.74*

$6.- $7,999

(13.10)

(35.23)

Per Capita GD?

21.26

—

39.85

—

$8.-$9,999

(13.07)

(35.12)

Per Capita GDP

22.27

30.27

SI0.-$II,999

(13.04)

(35.17)

Per Capita CD?

27.29 —

16.0!

—

$12,000- $13,999

(28.64)

(74.95)

Per Cap.iai CD?

79.33'

30.52

—

173.84'

II). 19'

(S!,s)

(13.04)

(16.79)

(34.32)

(47.45)

Pa Capita CD?.

-12.38'

-5.08'

-25.62'

-16.45'

squared

(2.05)

(154)

(5.38)

(7.16)

Per Capita GOP-

—

0.56'

0.233

—

1.09'

0.68'

cubed

(0.10)

(0.121)

(0.26)

(0.34)

Desert

40.19'

42.1 I'

-10.12

I 13.03

I 18.14'

44.27'

(8.61)

(&49)

(14.21)

(23.24)

(22.67)

(41.85)

Coast

-21.29

.21.75*

-17.68'

-35.85'

-41.44'

'334'

(4.94)

(4.55)

(4.52)

(13.00)

(11.87) (12,50)

Cential City

9.55'

8.26'

10.95'

32.41'

20.32*

21.44'

(4.05)

(3.96)

(3.86)

(9.38)

(9. IS)

(9,50)

lndustnal

0.25

-0.55

-0.13

-5.95

-637 -5.92

(4.23)

(4.18)

(4.06)

(9.62)

(9.51) (9.74)

Residential

.10.60*

.11.56*

.709'

-21.64' 2251' IS.20

(3.98)

(3.93)

(3.87)

(9.12)

(9.01)

(9-34)

Pop. dcosiuy

I.32

I.35

2.92

0.70

0.94

475

(IO/sq. mi.)

(0.37)

(0.36)

(1.17)

(0.98)

(0.95)

(3.17)

Year

-0.26

-0.60

-0.59

0.41

-0.1.5

1.27

(0,58)

(0.56)

(0.58)

(1.51)

(1.4$)

(139)

Comn,isnisi

-2O.44

-20.93

-14.44

-0.98

-9.73

756

(7.91)

(7.54)

(8.51)

(20.87)

(19.78)

(23.39)

Tndt lntcnsuiy

—

-6.51

—

-1L05

(6.35)

(17.84)

rvilua for alt

.OI

.OcOt

.0®t

GDP variabI

Per Capica GDP. which

$4,721

$4240

$4,910

$4,971

pollution niches peak

(771)

(2,180)

(973)

(2,105)

598

953

864

4,865 4.822

4,772

192

186

166

2.183

2,040

2.108

.345

.352

.210

. .315

.315

.22!

Notes:

Equations also thclude an intercep and a dummy to indicaca thai the type ot' ares is unknown. Sasrq,le sirs is 506 for

columns 1, 2. 4 and 5; sawple sirs is 457 for coIu 3 and 6.

Statistically signiticanc ai .05 level for. two-tailed I-test.

TABLE 4

(or

Paiticta PolIt.üoe

Random Effects

Esiim.tc.

(SI.adasd enon in pareotheses)

V.nable

'th f'nccttile

95th Peitcocjic

(I)

(2)

(3)

(4)

(5)

(6)

Per

Capita GOP -102.4'

—

-19L4'

53999

(11.9)

(25.4)

Pet Capita GOP

nc.i•

-247.4'

$5999

(13.5)

(29.0)

PerCapitaGDP

-101.9'

—

-134.2

- $7999

(31.8)

(69.2)

Per Capita GOP

201.2'

—

348.8' —

.59,999

(22.4)

(47.1)

PerCapitaGDP

.flI.t'

-425.0' —

$l0.-S1I.999

(13.0)

(27.7)

PerCapita GOP

-187.9'

—

-368.1'

—

512.000 — $13S99

(14.6)

(31.6)

PetCapitaGO?

-183.1'

—

-366.2'

$14,- $15,999

(12.5)

(27.1)

Per Capita CD?

-184.5

—

-388.2'

—

$l6.- $17.999

(15.5)

(32.9)

Per Capita GOP

:71.97S

-72.54'

—

-144.31' -146.42'

($I.s)

(8.02)

(8.49)

(17.81)

(17.14)

Per Capita GOP-

—

6.03'

5.88'

t2.65' 12.31'

sçnsed

(1.01)

(1.10) (2.28) (2.28)

Pet Capita GDP-

-0.157'

4:143'

—

0353' '0.328'

cvbod

(0.043)

(0040) (0.085) (0.084)

Deane

189.6' 213.3'

289.4' 354.7'

409.8' 489.5'

(19.0) (18.6)

(34.3) (403)

(39.6) (68.9)

Coast

0,42 4.47

2.90

-9.78

0.22 -4.39

(7.73)

(7.58) 0.41) (16.41)

(16.0!)

(15.43)

Cect..J City

it.tt• iz.• 14.76' 32J2'

36.31'

39.IV

(5.24) (5.20) (4.28) 00.47)

(10.40)

(10.36)

Industrial

13.13

15.2P

11.15

44.2V 47555

40.48

(5.79)

(5.77) (5.91) (11.55) (11.49) (I 1.66)

-12.l7

-9.67

-9.45 -1.19 3.83

2.02

(5.83)

(4,78) (5,90)

(11.62)

(11.51)

(11.66)

Pop. density

5.20

4.64 -17.55 41.3$ '43.97

'74.43

(t0.X0/sq. ml.)

(9.99)

(9.64) (12.63) (21.84) (20.95) (26.92)

Year

-2.06

-2.27 -2.26

-1.41 •1.65 4.27

(1.20) (1.14)

(1.15) (2.58)

(2.43)

(2.41)

Communist

90.52

I08.37 107.81

22I.51

256.46

251.l2

(13.21)

(12.6$) (12.54)

(28.68)

(V.28)

(26.52)

Trade Inteo4ty

—

12.39

6.49

(15.17)

(31.23)

p-value tar .11

.0031

.CCOI .0001

.0001

.0001 .0031

GDP v.tiâlea

3.379

3,350

3.482

12.950

12.793 13,085

3,353

3,754

3.0Th

18,043

17.218 14.894

.581

.577

.574

.569

.582

.590

Nolas:

Equaziocs also include an intenq,(. • dummy to indicate the type of a, unknown, and two dwmics to indicate the kind

of iniziumeot used to measure suspended particulate matter. Sa.wle sir. is 1.021 for columns I.

2. 4

and 5:

sample

site

is 971 foe colua3and6.

SiausücalIy si2nificsas at .05 level For. iwo-tailed Hess.

TABLE $

Rsadom Effects Estimates of lb. Determinants of Air

Pollution

Including

Fixed Site Effects

(Standard

en on us putnibeses

so2

Dark

Miner

Susocoded

Paslicics

Variable

pci!

95 pet!

50 pcti

95 pctl

50 pci!

95 pelt

Per Capita GDP

12.54 -8.28 24.35

77.89

63.44•

142.18

(SI,s)

(4.69) (15.11) (17.70)

(49.71) (13.94)

(30.30

Per Capita CDP -

-l.74

0.10 -4.15

-12.10

-2.84

-6.21

squared

(0.52)

(1.67) (2.73) (7.66)

(1.49)

(3.23)

Pet Capita GDP -

0.05

-0.03 0.19

0.51 0.04

0.07

cubed

(.02)

(.06) (0.13)

(0.36) (0.05) (0.11)

Yea,

-l.05

-3.51 -0.91 -1.12 -3.60

(0.28)

(0.90)

(0.42)

(1.17) (0.82) (1.77)

No. of

site

dummies

239

87 87 161 16L

p-nine for

0.I

0.118

0.479 0.250 0.I

0.003L

GOP

variables

161

1.951

141

1,070

919

3679

982 93

755 512 2473

0.76 0.77

0.87 0.82

0.91

Notes: Sample size is 1.370 for

506 for dark master and 1.021 for suqsoded p.iticles. The SO2 equations also include a

dummy for the measuring device, and the suspasded paiticles equations include two dummies for meansang device.

•SatisticsUy significantae .05 level Fort two-tailed t-te.

TABLE 6

The

Detconinant. ol the P.11cm of U.S. Import.

from Mexico and of Value Added by Maqwladors

lndcpcndcnu

Total US Imports from

Mexico

Mexican Value

Added in 807 Imports

Maquiladora Value Aalded as

Vujahics

. Frsct,on of US thinmcnt,

ai Fraction of US Value Added

Fractioc of US Value Added

(1)

(2)

(3)

(4)

(5)

(6)

Coostant

.028

•.027*

.019

.021

.010

.015

(.008)

(.009)

(.006)

(.007)

Human Capital Share

-.053

..053a

-.016

-.015

-.016

(.016)

(.010)

(.012)

(.011)

Physical Capital Sitar.

-.024

-,023

.026

-.027

-.011

-.016

(.010)

(.011)

(®9)

(.010)

(.011)

PollwionAbstemern Coda

.014

.012

.I65a

..151.

-.0t5

-.077

Fraction of US Industry

(.060)

(.061)

(.073)

(.074)

(.098)

(.090)

Value Added

TantfRiie

-.002

00l

—

(.028)

(.029)

Injury Rate

-.011

—

-.028

(.020)

(.016)

(.020)

.127

.095

.236

.392

Sample Size 135

135

136

Mean of Dependent Variable

.0069

.0022

.0012

Notcs Standard won ate in parenthesis. Columns (I) and (2) are 01$ estimates. Columns (3). (4). (5) and (6) an maximum

Iikclibood estimates of Tobit models.

flodicstes statislically signiñcait at .05 level.

TABLE 7

Estimated Impacts of NAFTA 00 Toxic

Releases

by Manufacturing Enterprises

(Pounds in Thousands)

Industry

Trade Liberalization

Only

Trade Sc Investment Liberalization

Maico

ILLS. Canada

Mexico Canada

Food&Tobacco Products

17 40

129

Textile Products

22 499 39

169

487 42

Apparel

lB 28

IS 57 28

Lumber & Wood Products -13

-88 76

74 -88

Furniture

128 210 176

257 148 178

Paper Products

1,198

-953

611

1.198

-971

Printing&Publishing

-10 52

-49

56 40 -48

Chemical Products

-1,430

12j98

-1,178

4.047

12,408 -1,180

Petroleum & Coal Products

62 93 19 29

Rubber & Plastic Products

-484 693

2,072

289 636

2,(2

Leather Products

37 -26 181 1S8 -38

182

Stone, Clay.

Glass

& Concrete 0 124

152 107 112 152

Primary Metals

-15

-1,59!

5,437

2.166 -1,452 5,494

Fabricated Metals

-1 558

254 259 530

254

Nonelectrical Equipment

-61 587 -103

569 -104

Electrical Equipment

1,445 -1,101

217

1,490

-1,091

217

Transportation Equipment

-178

-594

1,435

354

-594

1.424

Misc.

Manufacturing

171 32

338

192

332

Total

-261

13,053

8,017

10,466

13,261

8,032