Anna Kukla-Gryz Department of Economics, Warsaw University, Poland

1. 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

2. Plan of the presentation: • Trade, Growth & the Environment – what do we know? • Questions and Problems • Formulation and Estimation of Structural Equation Model • Conclusions

3. Environmental Kuznets Curve Hypothesis:

4. Trade, Growth and Environment (1): Openness to International Trade ? Increase in Incomes EKC Hypothesis Environmental quality

5. 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?

6. 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.

7. 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.

8. 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

9. 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)

10. 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

11. 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

12. 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

13. 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)

14. 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

15. 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)

16. Openness and GDPpc in developing countries, year 2000

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

18. 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”.

19. 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.

20. . Thank you for your attention !