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Quantitative Methods in Social Sciences (E774) . A Statistical Analysis of the Relationship between Public Policies and GDP per capita Group 1 Alexandra Hill Friederike Lemme Marina Peterhans Gustavo Diniz 04 December 2009. Part 1: Hypothesis. Understanding of dataset: Data2_Global
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Quantitative Methods in Social Sciences (E774) A Statistical Analysis of the Relationship between Public Policies and GDPper capita Group 1 Alexandra Hill Friederike Lemme Marina Peterhans Gustavo Diniz 04 December 2009
Part 1: Hypothesis • Understanding of dataset: Data2_Global • sample of countries • different periods of time • cross-section data • Indicators: • Assumptions: • Indicators Public policies • Random samples • Regressions: linearity and expected value of residual = 0 • Validity and Reliability - GDP per capita (gdppc) - Public expenditure on health (helpubexp) - Public expenditure on education (edupubexp) - CO2 emissions per capita (co2pc) QM_MDEV_E774(2009)
Hypotheses from Paper 1 and 2 • Paper 1 • A region’s average wealth is related to its average public policies. • Which group a country is in (developed, in transition, and developing economies) is related to its public policies • Paper 2 • The group developing economies is not meaningful because high-income developing economies’ and the low-income developing economies’ public policies cannot be considered to belong to the same group QM_MDEV_E774(2009)
Hypotheses from Paper 3 • Paper 3 • There is a relationship between GDP per capita and CO2 per capita emissions and there is no correlation between GDP per capita and health or education public expenditure • There is a directed relationship from CO2 emissions per capita to GDP per capita. However there is no directed relationship from health or education expenditure to GDP per capita • And finally, there is a conditional relationship between all three indicators and GDP per capita QM_MDEV_E774(2009)
Part 2: Statistical techniques • Paper 1 • Describing center: Mean, Median • Describing variability: Standard deviation, Skewness • Paper 2: • Sampling: Random samples, Frequency distributions • Estimation and significance: • Point estimate of the mean and of the standard deviation • Standard error • CI for the mean • T-test for comparing sample means • F-test for equal variances QM_MDEV_E774(2009)
Statistical techniques • Paper 3: • Scatter plots • Correlation: • Pearson’s and Spearman’s rank coefficient • R2 and p-values from the t-test • Regression • Bivariate and multivariate regression • Find residuals and plot residuals • Leverage test for outliers • T-test for the coefficients • Test for heteroskedasticity • R2 explanatory power of the model • Only for multivariate • Test for multicollinearity • F-test for joint significance for multivariate test • Plot fitted values of x and y for each x QM_MDEV_E774(2009)
Part 3: Results - Policy Paper 1 Table 1. Economic Well-being and Educational, Environmental, and Health Policies Among Regions* *Source: Basu, SudipRanjan. “Data2 Global.xls”. 2009.
Results - Policy Paper 2 Figure 1. Frequency distributions for Carbon dioxide emissions per capita (t CO2), 2004 by income levels* * Source: Basu, SudipRanjan. “Data2 Global.xls”. 2009. QM_MDEV_E774(2009)
STATA output: Public Expenditure on Education * • STATA command: sdtesti 3.06 1.59 45 3.91 2.25, level (95) * Source: Basu, SudipRanjan. “Data2 Global.xls”. 2009.
Table 2. Comparing Means of the High-Income with Low-Income Developing Economies * * Source: Basu, SudipRanjan. “Data2 Global.xls”. 2009.
Results - Policy Paper 3 Correlations and bivariate regressions Figure 1. Scatter Plot of All Countries for gdppc and helpubexp * Table 2. Gdppc as the dependent variable* * Source: Basu, SudipRanjan. “Data2 Global.xls”. 2009.
Multivariate regressions Figure 2. Residual Plot for Multivariate Regression with Outliers Included * Test for heteroskedasticity:estat hettest Figure 3. Regression Plots for co2pc, helpubexp, and edupubexp with Outliers* Test for Multicollinearity: vif predict h, hat Critical Value for Leverage Test is 0.054 ≈ 2*k/n * Source: Basu, SudipRanjan. “Data2 Global.xls”. 2009.
Multivariate regressions after removing outliers • STATA Command: reggdppc co2pc helpubexpedupubexp Prediction Equation: gdppc = 1377.712*co2pc + 2407.096*helpubexp + 13.018*edupubexp – 3740.766 + ε F-test: H0: β1 = β2 = β3 = 0 H1: at least one β1, β2, or β3 ≠ 0 Fobtained>Ftheoretical, we cannot accept H0. Therefore, at least one of the independent variables has a significant impact on gdppc. QM_MDEV_E774(2009)
Part 4: Conclusions • What’s new about our approach? • Testing the categories of the World Bank • Useful only for certain indicators • What did we learn from this exercise? • Even if we run regressions, we cannot say anything about causality • Is it because you spend more on education and health that you are richer? Or is it because you are richer that you can spend more on education and health? • Policy implications of our research… • Public policies: on a country-based analysis, taking into consideration specific contexts • The public expenditure on health and education of a country is not a very useful or telling indicator. • NO statistical relationship between edupubexp and gdppc • More precise indicators: accuracy / effectiveness • CO2 emissions: focus not only on developed economies, but also on developing countries with a high-income per capita level QM_MDEV_E774(2009)
Future work • Shortcomings of our dataset • Main problem: panel data is missing • What are missing elements of our research and analysis? • Control variables and instrumental variables • Random samples • More indicators to run regressions • Better categories, better indicators • Possible areas of future research… • Outcome of policies X Quantitative spending QM_MDEV_E774(2009)
