1 / 13

Quantitative Methods in Social Sciences (E774)

Quantitative Methods in Social Sciences (E774). Ze GDP and ze beautiful story of development Group 13 Anaïs Antreasyan Roxane Morger Grace Muhimpundu Alexandra Pittet 4 December 2009. Part 1 Hypothesis. Introduction. What is our hypothesis? The variables we used.

cathy
Download Presentation

Quantitative Methods in Social Sciences (E774)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Quantitative Methods in Social Sciences (E774) Ze GDP and ze beautiful story of development Group 13 Anaïs Antreasyan Roxane Morger Grace Muhimpundu Alexandra Pittet 4 December 2009

  2. Part 1 Hypothesis QM_MDEV_E774(2009)

  3. Introduction • What is our hypothesis? • The variables we used. • Why we chose these variables? QM_MDEV_E774(2009)

  4. Part 2 and 3 Statistical techniques and Policy papers QM_MDEV_E774(2009)

  5. Policy paper 1Center + variability • Mean and median explain the centers. • Standard deviation express this variability. • Observing this variation on a histogramm. • Relation between GDP and other variables to mesure development: Health, education, CO2. QM_MDEV_E774(2009)

  6. Observing center: Results (regrouping part 2 & 3) Less difference among health and educational expenditures than among GDPPC. • Concerning the relationship between • CO2 among the regions the relation to the center is such disperse. • Graph 2 draws this 4 relations: QM_MDEV_E774(2009)

  7. Policy paper 2Sampling, Estimation and Significance Sampling 7 samples which correspond to the regions. Samples are not randomly but intentionally defined. Estimation and Significance • We estimated the mean and standard deviation of each sample as these statistics are the best estimator of the population parameter; they are efficient and unbiased. • One sample mean comparison • In order to see if there is a significant difference between the sample mean and the population mean • Calculate the Z-score using μ and σ • Test the null hypothesis. Two tail test H0: μ1 = μ0 H1: μ1 ≠ μ0 • If │Z│≥ 1.96 At 95 % confidence we can not accept H0 • Two samples mean comparison • In order to see if there are some sample which have a significantly higher mean than the others • Calculate the t-score and the P-value using STATA (command) • Test the null hypothesis. One tail test H0: μ1 = μ2 H1: μ1 > μ2 • If │t│≥ the value in the t-table corresponding to the decrease of freedom, at 95 % confidence we can not accept H0. • Or more simply if the P-value ≤ 0.05 At 95% confidence we can not accept H0 QM_MDEV_E774(2009)

  8. At 95 % confidence, H0 can not be accepted for the region of Africa, Asia, Europe and Northern America. The sample mean are thus significantly different from the population mean. (One sample mean test) • At 95% confidence we can generally assume that (two sample mean test): • Regions which have a higher GDP also have higher CO2 emissions and higher public expenditure on education and health • There is no significant difference between Northern America and Europe’s mean. • Northern America and Europe’s mean are significantly bigger than the mean of Asia and Africa. Results • Asia represents an exception : • Although Asia as a lower GDP per capita than Northern America and Europe its CO2 emissions are not significantly lower • Although Asia as a bigger GDP per capita than Africa its public expenditure on education and health are not significantly higher. There is no significant difference between their public expenses on health, and expenses on education seem to be even higher in Africa. QM_MDEV_E774(2009)

  9. Policy paper 3Correlation By region, sort: pwcorr gdppc cer co2pc edupubexp helpubexp imr, sig obs star (.1) • Is there a correlation between GDP per capita and CO2 emissions per capita ? Ho: Rxy = Rxy = 0 H1: Rxy ≠ Rxy ≠ 0 Africa: P = 0.000 < 0.1 and r = 0.7393 Asia: P = 0.0006 < 0.1 and r = 0.6968 Europe: P = 0.000 < 0.1 and r = 0.7225 For these three regions we see a strong and positive correlation, so we cannot accept Ho. Caribbean: P = 0.0768 < 0.1 and r = 0.5073 Latin America: P = 0.0296 < 0.1 and r = 0.4865 For these two regions we see a moderate positive correlation, so we cannot accept Ho. Oceania and Polynesia: P = 0.0002 < 0.1 and r = 0.9584 Concerning Oceania and Polynesia, we see a very strong positive correlation, so we cannot accept Ho. QM_MDEV_E774(2009)

  10. Regression regress gdppc cer imr co2pc , level (99) • What kind of relation ship is there between GDPpc and three variables of welfare : infantile mortality rate (IMR), CO2 emissions per capita and the combined gross enrolment ration for primary, secondary and tertiary education (%) in 2005 (CER)? • F distribution • R-Squared: 63 % variations • IMR : | t | value > P (0.386 > 0.01) • R-Squared without IMR : 63 % variations • GDPpc – CER : positive strongrelationship R = 0.69 • GDPpc – IMR : negativestrongrelationship R = 0.62 • GDPpc – CO2 : positive strongrelationship Non constant standard deviation R = 0.62 QM_MDEV_E774(2009)

  11. Part 4 Conclusion and Future work QM_MDEV_E774(2009)

  12. Our approach • Our results • What we learned • Policy implications of our research • What is missing from our research • Future work… QM_MDEV_E774(2009)

  13. Enjoy life in numbers!! Thanks… QM_MDEV_E774(2009)

More Related