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Use of Structural Equation Modeling to examine the relationships between Trade, Growth and the Environment in developing countries. Anna Kukla-Gryz Department of Economics, Warsaw University, Poland. Plan of the presentation:. Trade, Growth & the Environment – what do we know?.

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Anna kukla gryz department of economics warsaw university poland

Use of Structural Equation Modeling to examine the relationships between Trade, Growth and theEnvironment in developing countries.

Anna Kukla-Gryz

Department of Economics, Warsaw University, Poland


Plan of the presentation

Plan of the presentation:

  • Trade, Growth & the Environment – what do we know?

  • Questions and Problems

  • Formulation and Estimation of Structural Equation Model

  • Conclusions


Environmental kuznets curve hypothesis

Environmental Kuznets Curve Hypothesis:


Trade growth and environment 1

Trade, Growth and Environment (1):

Openness to

International Trade

?

Increase in Incomes

EKC Hypothesis

Environmental quality


Trade growth and environment 2

Trade, Growth and Environment (2):

1) Pollution Haven Hypothesis:a reduction in trade barriers leads to a shifting of pollution-intensive industry from countries with stringent regulations to countries with weaker regulations (from developed to developing countries)

  • Race-to-the-Bottom Hypotheis: developing countries lower their

  • environmental standards to attract international business

Is - for developing economies - openness to international trade

„good” or „bad” for the environmental quality?


Problems

Problems:

1) Problem with agregation of environmental indicators

(water quality, air quality, e.t.c.).

2) Differences in the deffinitions across countries – particularly in developing countries.


Advantages of structural equation modeling sem

Advantages of Structural Equation Modeling (SEM):

  • Estimation of latent variables (factors), e.g. environmental quality,measured

  • by many indicators.

  • - Estimation of error terms on each observed factor’s indicator. As a result,

  • path coeffcients are unbiased by error terms which increase the comparability

  • of the data between the countries.

  • - SEM allows combining many structural relationships into one model giving

  • a possibility of including many mechanisms in one model, e.g. between openness

  • and economic growth, openness and environmental quality, economic growth

  • and environmental quality.


Description of the formulated structural equation model

Description of the formulated structural equation model:

The structural equation model:

The structural equation model specifies the causal relationships among thevariables, describes the causal effects, and assigns the explained and unexplained variance.

The measurement model for dependent latent variable (factors):

The measurement model specifies how latent variables depend upon orare indicated

by the observed variables.

  • h is a m x1 random vector of latent dependent variables

  • x is a n x1 random vector of exogenous variables

  • y is a p x1 vector of observed indicators of the dependent latent variablesh

  • eis a p x1 vector of measurement errors in y

  • Ly isapxmmatrix of coefficients of the regression of y on h

  • Gis a m xnmatrix of coefficients of thex-variables in the structural relationship

  • Bis a m xmmatrix of coefficients of theh-variables in the structural relationship

  • is a m x1vector of equation errors (random disturbances) in the structural relationship

    betweenh andx


Anna kukla gryz department of economics warsaw university poland

Indicators of the latent variables:

  • Urban population as a percentage of total population

  • Literacy rate of 15-24 years old

  • Non-agricultural workers, percentage of total labour force

  • Mortality rate in children under 5 year olds

  • Health-adjusted life expectancy (HALE)

  • Immunization rate for DPT in one-year-olds

  • Immunization rate for measles in one-year-old

  • Percentage of population with acces to improved water source

  • Percentage of population with acces to improved sanitation

  • Fertilizer use intensity

  • Total Forest area, average percentage change in 1990-2000

  • Carbon dioxide emissions per capita

„structural

changes”

(dev)

„health care

quality”

(health)

„environmental

quality”

(env)


Economic indicators exogenous variables

Economic indicators (exogenous variables):

GDP PPP per capita

Foreign direct investment intensity

International Aid received by country

„Openness”

Freedom Index

Export to developed countries – percentage of total export

Export of manufactured goods (5-8 SITC Rev. 3, without 68)

– percentage of total export

120 developing countries, without CEE countries, year 2000


Conceptual path diagram of the model

Conceptual path diagram of the model:

Goodness of Fit Index (GFI) = 0.793

Adjusted Goodness of Fit Index (AGFI) =0.664

Chi-Square=158.26, df=117, P-value=0.00666, RMSA=0.054


Estimation results 1 structural equations

Estimation Results (1): Structural Equations

health = 0.0243*dev + 0.00572*GDPpc + 0.00389*ex_manu + 0.00376*oda + 0.0202*fi

(8.689) (0.200) (3.720) (2.217) (0.687)

Errorvar.= 0.00958 (0.431), R^2 = 0.968

dev = 6.908*GDPpc - 0.0733*exdev + 67.290*fdigdp - 0.0701*ex_manu + 0.648*fi + 14.660*open

(13.493) (-1.385) (1.611) (-1.561) (0.696) (3.706)

Errorvar.= 106.611 (4.796), R^2 = 0.757

env = 0.0763*dev - 0.0577*GDPpc + 0.00651*exdev + 0.0102*ex_manu - 1.078*open

(4.207) (-0.594) (1.509) (2.724) (-2.569)

Errorvar.= 0.125 (0.748) , R^2 = 0.952


Anna kukla gryz department of economics warsaw university poland

Estimation Results (2): Indirect and Total Effects of Economic Indicators on latent variables

Indirect Effects:

GDPpc exdev fdigdp ex_manu oda fi

health 0.168 -0.002 1.632 -0.002 - - 0.016

(7.449) ( -1.457) (1.583) (-1.470) (0.696)

env0.527 -0.006 5.133 -0.005 - - 0.049

(4.135) (-1.384) (1.566) (-1.479) (0.688)

Total Effects:

GDPpc exdev fdigdp ex_manu oda fi open

health0.173-0.002 1.632 0.0020.0040.036 0.356

(9.971) (-1.457) (1.583) (2.185) (2.217) (1.136) (3.691)

dev 6.908 -0.073 67.290 -0.070 - 0.646 14.660

(13.493) (-1.385) (1.611) (-1.561) (0.696) (3.706)

env0.469 0.001 5.133 0.005 - 0.049 0.040

(5.099) (0.188) (1.566) (1.449) (0.688) (0.105)


Anna kukla gryz department of economics warsaw university poland

Estimation Results (3): Total Effects of Economic Indicators latent’s idicators

GDPpp exdev fdigdp ex_manu oda fi open

Fert13.958** 0.027 152.687* 0.144 - - 1.470 1.183

Water4.015** 0.008 43.917 0.041 - - 0.423* 0.340

Sanit4.243** 0.008 46.412 0.044 - - 0.447* 0.360

Hale2.595** -0.027 24.444 0.033**0.056** 0.537 5.325**

dpt4.253** -0.044 40.062 0.054** 0.092** 0.881 8.728**

lit3.958** -0.042 38.557 -0.040 - - 0.371 8.400**

um50.173** -0.002 1.632 0.002** 0.004** 0.036 0.356**

measles3.917** -0.040 36.900 0.050** 0.085** 0.811 8.039**

agri6.908** -0.073 67.290 -0.070 - - 0.648 14.660**

Forest0.202** 0.000 2.212 0.002 - - 0.021 0.017

urban5.140** -0.055 50.071 -0.052 - - 0.482 10.908**

co20.469** 0.001 5.133 0.005 - - 0.049 0.040


Covariance matrix of exogenous variables

Covariance Matrix of Exogenous Variables:

GDPpp exdev fdigdp ex_manu oda fi open

GDPpc7.738

(9.692)

exdev12.417 384.190

(2.363) (8.662)

fdigdp0.010 0.1010.001

(1.584) (2.431) (6.237)

ex_manu35.747 26.539 -0.134 753.320

(5.458) (0.590) (-2.509) (9.786)

oda-26.778 0.911 0.215-198.670 596.480

(-5.408) (0.025) (2.164) (-4.011) (5.235)

fi-1.628-4.859-0.009 -1.046 -3.941 1.712

(-4.783) (-1.809) (-3.166) (-0.316) (-1.630) (10.685)

open-0.084 0.401 0.002 -0.533 3.037-0.075 0.090

(-1.275) (0.848) (3.178) (-0.770) (3.660) (-2.081) (6.282)


Openness and gdppc in developing countries year 2000

Openness and GDPpc in developing countries, year 2000


Anna kukla gryz department of economics warsaw university poland

GDPpc, carbon dioxide emissions per capita and forest’s average percentagechange in 1990-2000, in developing countries, year 2000


Conclusions 1

Conclusions (1):

The resultsshow that we should bemore skeptical about the existence

of a simpleand predictable relationshipbetween

openness to international trade andpercapita income.

Not significant effects of both export to developed countries (ex_dev)

and „openness” on the „quality of the environment” do not support

the „pollution haven hypothesis”.


Conclusions 2

Conclusions (2):

FDI, openess and ex_dev are possitivelly correlated with each other.

These results suggest that in the analyzed developing countries, in 2000, FDI went to more open economies and came from export-oriented foreign firms.

Further, the estimation reslults show that the only effect of FDI on ”environmedntal quality” was increased fertilizer use intensity.

Total effects of GDPpc on all environmental quality indicators are both negative and positive.

Negative, through increase in carbon dioxide emission per capita and fertilizer use intensity. Positive, through increase in the percentage of the population with access to improved water source and sanitation and through increase in total forest area.


Anna kukla gryz department of economics warsaw university poland

.

Thank you for your attention !


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