Quantitative Methods in Social Sciences (E774)

1. Quantitative Methods in Social Sciences (E774) Human Development Indicators & GDP per capita Group 5 Asmaa El Jamali Claudie Lacharité Nurvitria Mumpuniarti Alero Okorodudu 4/4 December 2009

2. Introduction • Differences between human indicators (education, health and environment) among different income level categories • Different priority and focus on policies between developing and developed countries • GDP per capita as a non-comprehensive indicator for comparing development outcomes • Focus on micro level development issues that are country specific. QM_MDEV_E774(2009)

3. Part 1. Hypothesis HEALTH EDUCATION ENVIRONMENT Does income level reflect the level of human development ? Across Income Level Categories Measures in tackling preventive and curative disease Success stories Oil Producing Countries Other influential factors QM_MDEV_E774(2009)

4. Statistical Techniques(Health) • Sampling : High disease burdened countries vs. low disease burdened countries (AIDS case), we cannot conclude that there is significant difference in health expenditure mean. Health Expenditure per Capita (in PPP$) by Income Level of Countries 2004 Life Expectancy at Birth (in years) by Income Level of Countries 2004 Source: Data_2_Global Source: Data_2_Global QM_MDEV_E774(2009)

5. Correlation & Regression(Health) Life Expectancy at Birth by GDP Per Capita in PPP$ (Low Income Countries) Health Expenditure per Capita by GDP per Capita in PPP$ (Low-Income Countries) Source: Data_2_Global Source: Data_2_Global • Example of AIDS in low to lower-middle income countries where the needs for prevention and treatment outstrip resources available. In addition, the growing populations in low to lower-middle income countries. • We found significance, that in a high-disease burden environment, the higher the GDP per capita, the higher is spent on health per capita. QM_MDEV_E774(2009)

6. Correlation & Regression(Health) Life Expectancy at Birth by GDP Per Capita in PPP$ (High-Income OECD Countries) Example of high-income countries, with a disease burden type such as cancer. This may account for the comparatively low life expectancy level that some high-income countries have. There tends to be a gap in research and availability of a medical solution to the public, which affects life expectancy. Source: Data_2_Global

7. Statistical Techniques(Education) Public Expenditure on Education (% of GDP) 2002 -2005 Adult Literacy Rate by Income Level of Countries (1995-2005) Source: Data_2_Global Source: Data_2_Global • Adult literacy rate in lower income countries is significantly lower than other income level categories. • Lower middle income regions spend more on education expenditure than upper middle income regions. • Sometimes, the education expenditure is not reflected in adult literacy rate. The efficiency of education expenditure has to be questioned.

8. Correlation & Regression(Education) Adult Literacy Rate and GDP in Low Income Public Expenditure on Education and GDP in low income Countries Source: Data_2_Global Source: Data_2_Global • Adult literacy rate is statistically significant and positively related to GDP per capita only in low income and lower middle income countries. • There is no significant relation between public expenditure on education and GDP per capita in low income countries.

9. Correlation & Regression(Education) Adult Literacy and Public Expenditure on Education in high income non OECD countries When comparing adult literacy rate and public expenditure on education only one statistically significant and positive relationship was found. In high income non OECD countries, the more these countries are spending on education, the higher their adult literacy rates are. Source: Data_2_Global

10. Multiple Regression Y = α + β1X1 + β2X2 + ɛ Where Y = GDP/capita ($PPP) X1 = Adult Literacy Rate (year) X2 = Life Expectancy at Birth (year) Case in low income countries, regress gdppc_1 le_1 alr_1 Source | SS df MS Number of obs = 40 -------------+------------------------------ F( 2, 37) = 8.00 Model | 8714904.01 2 4357452.01 Prob > F = 0.0013 Residual | 20150643.9 37 544611.997 R-squared = 0.3019 -------------+------------------------------ Adj R-squared = 0.2642 Total | 28865547.9 39 740142.254 Root MSE = 737.98 ------------------------------------------------------------------------------ gdppc_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- le_1 | 42.77754 15.85581 2.70 0.010 10.65062 74.90445 alr_1 | 9.946804 6.114758 1.63 0.112 -2.442873 22.33648 _cons | -1209.702 831.7083 -1.45 0.154 -2894.903 475.4989 ------------------------------------------------------------------------------ Among different income level categories, regression is significant only in lower and lower middle income countries

11. Statistical Techniques(Environment) CO2 emissions per capita (t) by Income Level of Countries (2004) Electricity consumption per capita (kw/h) by Income Level of Countries (2004) Source: Data_2_Global Source: Data_2_Global QM_MDEV_E774(2009)

12. Statistical Techniques(Environment) We expected that the mean of CO2 emissions would have been higher for the high-income OECD region (10,99 t) than the high-income NON-OECD region (22,73 t) because they consume more electricity, but this is not the case. Comparative of the means of the CO2 emissions per capita(t) and the Electricity consumption per capita (kw/h) - 2004 Source: Data_2_Global QM_MDEV_E774(2009)

13. Correlation & Regression (Environment) High Income Countries Oil production level and CO2 emissions per capita We first observed that the correlation between the GDP per capita and the CO2 emissions per capita of this new income level category is not statistically significant. On the other hand, we can conclude, at a 95% confidence level, that the relationship between the level of oil production and the CO2 emissions per capita is statistically significant and positive. Source: Data_2_Global QM_MDEV_E774(2009)

14. Conclusions • What’s new about your approach? • Looking at human development index across different income level assuming different prioritization and hence the formulation of policies that are intended for micro-level issues both in developed and developing countries. • What did you learn from this exercise? • The use of statistics gives us the big picture and allows us to scale down our research in a more detailed and efficient manner. • It helps us confirm or disconfirm our observed assumptions about development issues based on significant statistical tests. • Policy implications of your research • Health : Prioritization based on type of disease-burden • Education : Low income countries urgently need to find other education financing sources to raise their adult literacy rates (educational reforms and international aid) • Environment : Our dependence on fossil fuel resources has to be reduced and shift it more to renewable resources QM_MDEV_E774(2009)

15. Future work • Education : Adult Literacy rate is not a good parameter on gauging human development across most countries, except in low income countries where the variability of adult literacy rate is still high and has to be narrowed upwards. Hence, from lower middle income countries to high income countries, further research on other parameters rather than adult literacy rate, perhaps technology innovation and research can reflect more variation in income level. • Health :There is no data in health expenditure in high income countries • It is important to look at the type of disease burden that affects different regions. This may have an effect on the countries’ health structure, how finances are allocated, and as a result, on the life expectancy of the country. • Environment : The possibility of biased analysis based on the usage of per capita data on Co2 emission and electricity. It may be interesting to see it at a national level. • Also analysis of the relationship between the percentage of the land covered by the forest, the % of the renewal energy usage and the total quantity of CO2 emissions can be further looked into. QM_MDEV_E774(2009)