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121
To determine which of the groups differ from each other, it is necessary to
compare all pairs of groups. Pair-wise comparisons test the hypothesis that any two
survival functions are equal. The Log Rank (Mantel-Cox) test produces a Chi-square
statistic and corresponding level of statistical significance for each pairing. Table 18
summarizes the pair-wise comparisons.
Table 18: Region -- Pairwise Comparisons
East Asia
Europe
Sub-Saharan
Africa
Oceania
N. America
S. America
Mid. East
& N. Africa
Cent. Asia Europe
4.26
(.039)**
Sub-Saharan
Africa
2.258
(.133)
8.576
(.003)*
Oceania
8.770
(.003)*
17.447
(.000)*
17.854
(.000)*
N. America
.842
(.359)
4.300
(.038)**
7.856
(.005)*
11.061
(.001)*
S. America
9.479
(.002)*
.867
(.352)
9.178
(.002)*
20.890
(.000)*
12.725
(.000)*
Mid. East &
N. Africa
1.522
(.217)
6.923
(.009)*
3.516
(.061)***
12.494
(.000)*
.673
(.412)
16.913
(.000)*
Central Asia
28.887
(.000)*
10.816
(.001)*
78.544
(.000)*
28.807
(.000)*
30.744
(.000)*
12.953
(.000)*
34.654
(.000)*
South Asia
1.688
(.194)
2.941
(.086)***
.607
(.436)
8.752
(.003)*
9.769
(.002)*
2.885
(.089)***
6.235
(.013)**
17.462
(.000)*
*statistically significant at .01-level
**statistically significant at .05-level
***statistically significant at .10-level
As the number of comparisons increases statistical significance is more
difficult to achieve. Accordingly, three levels of statistical significance are noted in
Table 18. Twenty-three of 36 comparisons (64%) are statistically significant at the
.01-level. An additional three (8.3%) comparisons are statistically significant at the
Object Description
| Title | Riding the wave: an interdisciplinary approach to understanding the popularity of RTA notifications to the GATT/WTO |
| Author | McClough, David Andrew |
| Author email | mcclough@usc.edu; dmcclou@bgsu.edu |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Political Economy & Public Policy |
| School | College of Letters, Arts and Sciences |
| Date defended/completed | 2008-08-07 |
| Date submitted | 2008 |
| Restricted until | Unrestricted |
| Date published | 2008-10-18 |
| Advisor (committee chair) | Katada, Saori N. |
| Advisor (committee member) |
Nugent, Jeffrey B. Cartier, Carolyn |
| Abstract | The proliferation of Regional Trade Agreements (RTAs) notified to the GATT/WTO since the early 1980s deviates from the long-term trend and reflects participation of nearly every member of the United Nations. This dissertation seeks to explain the current wave of RTA notifications by supplementing the economic model of supply and demand with diffusion theory. Application of the supply and demand model is useful in distinguishing between changes in demand and changes insupply of RTAs. This distinction is seldom emphasized in the current literature examining RTAs. Recent applications of diffusion theory in the discipline of international relations offer a unique opportunity to include a dynamic force in the static analysis of the supply and demand model. Empirical analysis assesses the fit of the RTA diffusion pattern by comparing the RTA diffusion pattern to a cumulative standard normal distribution. The analysis indicates that the diffusion pattern of RTAs resembles the diffusion of an innovation through a social system.; The implication of this finding is that the adoption of an RTA as trade policy is not made independently of the decision by other states. Indeed, the analysis suggests interdependency between states. Further empirical analysis explores economic and political variables that may explain the decision to adopt the RTA as trade policy. The empirical analysis is unique in that survival analysis is utilized to assess the variation in duration to adopt an initial RTA since the early 1980s. A central discovery is that regional designation explains the variation in duration to adopt an initial RTA. Multiple regression analysis confirms the results generated using survival analysis and support the assertion that the proliferation of RTAs likely reflects changes in both the demand for RTAs and the supply of RTAs. This dissertation concludes by considering implications for the WTO resulting from the increase in RTA notifications. |
| Keyword | trade agreements |
| Coverage date | after 1980 |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m1675 |
| Contributing entity | University of Southern California |
| Rights | McClough, David Andrew |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | cisadmin@lib.usc.edu |
| Filename | etd-McClough-2338 |
| Archival file | uscthesesreloadpub_Volume32/etd-McClough-2338.pdf |
Description
| Title | Page 130 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 121 To determine which of the groups differ from each other, it is necessary to compare all pairs of groups. Pair-wise comparisons test the hypothesis that any two survival functions are equal. The Log Rank (Mantel-Cox) test produces a Chi-square statistic and corresponding level of statistical significance for each pairing. Table 18 summarizes the pair-wise comparisons. Table 18: Region -- Pairwise Comparisons East Asia Europe Sub-Saharan Africa Oceania N. America S. America Mid. East & N. Africa Cent. Asia Europe 4.26 (.039)** Sub-Saharan Africa 2.258 (.133) 8.576 (.003)* Oceania 8.770 (.003)* 17.447 (.000)* 17.854 (.000)* N. America .842 (.359) 4.300 (.038)** 7.856 (.005)* 11.061 (.001)* S. America 9.479 (.002)* .867 (.352) 9.178 (.002)* 20.890 (.000)* 12.725 (.000)* Mid. East & N. Africa 1.522 (.217) 6.923 (.009)* 3.516 (.061)*** 12.494 (.000)* .673 (.412) 16.913 (.000)* Central Asia 28.887 (.000)* 10.816 (.001)* 78.544 (.000)* 28.807 (.000)* 30.744 (.000)* 12.953 (.000)* 34.654 (.000)* South Asia 1.688 (.194) 2.941 (.086)*** .607 (.436) 8.752 (.003)* 9.769 (.002)* 2.885 (.089)*** 6.235 (.013)** 17.462 (.000)* *statistically significant at .01-level **statistically significant at .05-level ***statistically significant at .10-level As the number of comparisons increases statistical significance is more difficult to achieve. Accordingly, three levels of statistical significance are noted in Table 18. Twenty-three of 36 comparisons (64%) are statistically significant at the .01-level. An additional three (8.3%) comparisons are statistically significant at the |
