Empirics and the Pollution Haven Hypothesis PHH

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Empirics and the Pollution Haven Hypothesis (PHH)

• November 10, 2007

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Empirical questions related to PHH

• Do investment flows respond to differences in
  environmental standards?
• Has trade liberalization increased pollution
  intensity in developing countries?
• Have tighter standards in developed countries led
  to loss in pollution-intensive industries?
• The literature does not attempt to determine
  whether countries use environmental policies that
  are too weak, in order to attract investment or
  increase market share of dirty goods. That is,
  the literature does not attempt to uncover the
  motive of environmental policy.

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What is a statistical model?

• We are interested in relation between net exports
  and pollution control costs.
• We know that net exports depend on other
  variables (e.g. supply of factors remember the
  HOS model and Rybczynski theorem)
• If we have data on these variables we can
  estimate a relation between exports and pollution
  control costs, while controlling for other
  variables (e.g. supply of factors).
• We are (usually) interested in sign and magnitude
  of coefficient on pollution control costs, and on
  whether the coefficient is statistically
  significant.

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More details on statistical model (a.k.a.
regression equation)

• The subscript i identifies the country and the
  subscript t identifies the time period. For the
  PPH, the dependent variable y is a measure of
  exports of the dirty good, the explanatory
  variable x is a measure of pollution control
  costs, z contains other explanatory variables,
  called control variables (e.g. factor
  endowments for the PHH) e is the equation
  error, a composite of factors that we do not
  observe, but which affect the dependent variable,
  and a component that takes into account the
  inherent randomness of the process.
• The statistical problem is to estimate the
  parameters, particularly beta, and determine
  whether it is positive and statistically
  different than 0.
• There are many technical problems missing data,
  data with measurement errors, correlation between
  error and explanatory variables, misspecification
  of model.

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Alternatives for addressing question Does trade
harm the environment?

• Theory, i.e. try to determine the likely relation
  between trade and the environment using logic.
  Theory helps you think clearly but is
  inconclusive.
• Case studies, i.e. finding examples where the
  relation appears positive or negative. These are
  useful, but they leave you wondering how
  representative the case studies are.
• Statistical models these have the advantage of
  being based on widely accepted principles, but
  the data seldom exactly conforms to the
  statistical assumptions.

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The empirical evidence

• Early studies use US data to categorize
  industries into dirty and clean sectors (based on
  emissions per of output, or per employee, or on
  abatement costs).
• The statistical exercise looks for link between
  dirty and clean good trends in production or
  export (share) and country characteristics such
  as income, income growth, and openness.
• Are developing countries moving toward dirty
  industries?
• This type of exercise ignores possible changes in
  technique -- it assumes that changes in
  composition translate directly into changes in
  pollution. Also ignores other explanatory
  values, such as factor endowments.

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Early evidence

• Early research found that a rise in environmental
  control costs in North was positively correlated
  with increases in dirty good share of exports
  from developing countries, and decreases in dirty
  good share of exports from rich countries.
• The Lucas and Wheeler study found that toxic
  releases per unit of output (measured by GNP)
  fell as countries became richer, due to changes
  in composition. Poorer countries had the largest
  increases in toxic intensity.
• Birdsall and Wheeler found that pollution
  intensity increased most rapidly in Latin
  American countries after OECD pollution
  regulation became stricter.

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Interpretation of these results

• These findings are consistent with PHH, but are
  also consistent with an explanation based on
  changes in factor endowments (capital
  accumulation).
• Evidence for the importance of capital
  accumulation
• (i) Over 90 of dirty good production in 1988
  was in OECD countries, suggesting that location
  of dirty good production reflects more than weak
  environmental regulation.
• (ii) If stricter environmental policies in rich
  countries were responsible for reallocation of
  dirty good production (as in PHH) then we would
  see an increase in the relative price of dirty
  goods if capital accumulation in South caused
  the reallocation, the relative price would fall.
  Data does not show a clear upward or downward
  trend in relative price.
• (iii) All studies show that poor countries alter
  their mix of production toward dirty goods, the
  more open countries have a cleaner mix.
  Pollution intensity grew most rapidly in the more
  closed economies.

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Early studies of trade effect of pollution
control costs

• Tobey uses cross country data on exports of 5
  dirty commodities and country-specific factor
  endowments and measures of environmental
  stringency.
• Few degrees of freedom (not much data).
  Coefficient on environmental stringency
  insignificant, but so are most of the
  coefficients on factor endowments.
• The statistical model does not explain much of
  anything.

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The relation between trade flows and measures of
environmental stringency

• Link net exports (as share of value of industry
  production) to industry-specific measure of
  environmental stringency (e.g., abatement costs)
  and industry characteristics (such as cost shares
  of labor, capital, and maybe tariff rates).
• The PHH implies that the coefficient on the
  environmental stringency variable should be
  negative (more stringent environmental policies
  lower net exports.)
• Studies do not find a significant negative
  relation between environmental stringency and net
  exports.

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Statistical reasons why these studies might
incorrectly reject PHH

• Small sample leads to lack of statistical
  significance.
• Several reasons why models might produce biased
  estimates
• Measurement error
• Endogenous explanatory variables
• Omitted explanatory variables that are correlated
  with included variables
• Three examples follow. In the first, a
  statistical model correctly identifies relation
  between pollution control costs and trade. In
  the second two examples, statistical model leads
  to biased estimates. The bias could go in either
  direction.

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What do we mean by speaking of the demand
function and the supply function for pollution?

• The demand function Think of pollution as the
  use of the environment as a dumping ground. Firms
  demand pollution (i.e. they want to use the
  dumping ground more) because it is cheaper for
  them to dump than to clean up or prevent
  pollution. A higher pollution tax (the price of
  dumping) decreases firms demand, so this
  function slopes down.
• Pollution creates a cost to society. Societys
  supply function for pollution equals societys
  marginal cost of pollution. If the marginal cost
  increases, societys supply function for
  pollution slopes up.

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The optimal pollution tax (the price of a unit of
pollution) is given by the intersection of the
supply and demand curves for pollution
Pollution price, equal to the tax
Societys supply function for pollution
Firms demand function for pollution
Pollution quantity
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Example 1 statistical model correctly identifies
a relation between pollution control costs and
trade

• Draw a downward sloping (industry) demand curve
  for pollution (the pollution tax is on vertical
  axis). A lower tax means that firms demand
  for pollution increases.
• Suppose that there is an exogenous increase in
  pollution tax (maybe preferences become more
  green). The higher tax increases abatement costs
  per unit of output.
• Since production costs (inclusive of abatement)
  increase, domestic supply (as a function of
  output price) shifts in.
• At a constant relative commodity price, net
  exports fall. Here more stringent policy lowers
  exports (or raises imports) of the dirty good, as
  the PHH predicts. (See next slide.)

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A higher tax reduces firms level of pollution
(left panel), increasing their production costs,
causing the supply curve to shift in (dotted
curve in right panel)
tax
Price of dirty good
tax
Firms demand for pollution
Firms supply function for dirty good
pollution
Quantity of dirty good
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Example 2 statistical model understates relation
between abatement costs and trade (or gets sign
wrong), due to an omitted explanatory variable
that is incorporated into the error term
leading to correlation between the pollution tax
and the error (a form of endogeneity)

• Suppose that the pollution tax is endogenous it
  is determined (optimally) by the intersection of
  a (industry) demand and (societys) supply
  function for pollution.
• An increase in a factor (e.g. capital) used
  intensively in polluting industry shifts out
  demand curve for pollution. This variable is not
  included in the statistical model, so it gets
  incorporated into the error term.
• This change leads to a higher pollution tax, and
  higher abatement costs.
• However, the increase in the factor also shifts
  out domestic supply function of dirty good.
  (Higher tax and larger supply of factor cut in
  the opposite direction.) Net exports increase.
• Here pollution taxes are positively correlated
  with net exports, contrary to PHH.
• The higher pollution tax does not cause the
  increased export of the dirty good. Instead, the
  exogenous growth in a factor leads to higher
  output of the dirty good and to higher pollution
  costs.
• The higher tax does reduce exports (since the
  relative supply curve would have shifted out more
  in the absence of the tax increase.)
• This measurement problem would not arise if the
  statistical model included the missing variable
  (the stock of capital in this example).

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Increase in factor shifts out demand curve for
pollution (dotted curve, left panel), raising the
pollution tax. By assumption, the higher supply
of factor decreases marginal cost of dirty good,
even with the higher tax, so supply function of
dirty good shifts out (dotted curve in right
panel). A higher pollution tax is correlated
with higher supply of dirty good. Dashed curve
right panel shows the supply effect of increase
in factor, absent the increase in tax
Price of dirty good
tax
Pollution quantity
Dirty good
Supply and demand of pollution
Supply of dirty good
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Example 3 statistical model overstates relation
between abatement costs and trade, due to omitted
explanatory variable

• The statistical model regresses exports on
  pollution abatement costs, but omits
  transportation costs. PHH suggests that high
  abatement costs discourages domestic production
  in dirty sectors, so sectors with higher
  abatement costs would export less.
• Dirty industries (in this example) have higher
  transport costs (e.g. cement) relative to clean
  industries.
• High transport costs discourage exports. Suppose
  that transport costs (e.g. energy costs)
  increase.
• The higher transport costs have a disproportional
  effect on dirty goods (because transport is more
  important in those sectors). The higher
  transport costs have a disproportional effect on
  exports of the dirty goods.
• In this case we have an excluded variable
  (transport costs) that is positively correlated
  with an included variable (abatement costs).
• In this example, the estimate on the coefficient
  of abatement costs is upwardly biased. Here the
  statistical results exaggerate the trade effect
  of abatement costs.

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Some recent statistical evidence

• Evidence from US studies shows that these
  endogeneity and missing variable issues might be
  part of the explanation for the failure of
  statistical evidence to support the PHE.
  Intra-US trade data is better than world trade
  data.
• US studies estimate the relation between
  investment (into US states or counties) and
  measures of environmental stringency. When the
  studies account for endogeneity and
  heterogeneity, they often find a significant
  negative relation between inward investment and
  abatement costs, as the PHH suggests.
• In other words, correcting for endogeneity and
  other statistical problems might uncover stronger
  evidence for PHH.
• Even if stronger pollution control alter
  investment and trade flows on the margin, it is
  unlikely to be strong enough to offset other
  considerations, such as factor endowments.
• PHH may be more important in future. Pollution
  abatement capital expenditures have risen from
  2.8 of new capital expenditures in US in 1984 to
  7 in 1993.

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Related (older) trade and environment studies

• Grossman and Krueger estimated that the
  composition effect of Mexico joining NAFTA
  would likely reduce pollution.
• This composition effect appears to have actually
  occurred. However, it was swamped by scale
  effect (increased aggregate production), leading
  to increased pollution in Mexico.
• Of course, we do not know that this higher
  pollution was a consequence of NAFTA.

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Summary

• Trade is determined by many things (e.g. factor
  endowments, technology, infrastructure,
  institutions).
• Costs of environmental measures are small in most
  sectors, so they likely have only small effect on
  investment decisions and trade flows.
• There is some (emerging) statistical evidence
  that identifies these small effects.
• Environmental costs and cost differences might
  increase over time, (e.g. next version of Kyoto
  Protocol), making PHH more important in the
  future.
• In some sectors these costs are already large
  enough to effect pattern of trade (e.g. battery
  disposal, ship breaking). Basel Convention can
  regulate trade in these sectors (better than
  general trade restrictions).

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Summary, continued

• There are many reasons why countries have
  different levels of environmental protection.
  (Differences in competing needs and constraints,
  preferences, assimilative capacity.)
• Statistical evidence cannot determine the
  rationale for level of environmental protection.
• In addition to (possibly) reallocating production
  of dirty goods from rich to poor countries,
  globalization is (plausibly) associated with
  income growth and technology transfers that at
  least offer the opportunity of environmental
  improvements.

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And most importantly

• Trade policy is a poor substitute for
  environmental policy.
• Remember the Principle of Targeting.